feat: STEM benchmark initial release

- Complete benchmark specification document (618 lines)
- 3dSAGER pipeline adapted for spatio-temporal 3D entity matching
- Baseline experiment results on The Hague dataset (155K buildings)
- Disaster simulation + RANSAC alignment working end-to-end
- XGBoost F1=0.998 under CRS rotation 184° + 80% damage

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+# Git LFS for large data files
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text
+*.pdf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+task_categories:
+- entity-matching
+- entity-resolution
+language:
+- en
+tags:
+- 3d-entity-matching
+- spatio-temporal
+- entity-resolution
+- cityjson
+- citygml
+- building-matching
+- disaster-response
+- few-shot-learning
+- geospatial
+pretty_name: STEM - Spatio-Temporal 3D Entity Matching Benchmark
+size_categories:
+- 10M-100M
+---
+
+# STEM: Spatio-Temporal 3D Entity Matching Benchmark
+
+[![HuggingFace](https://img.shields.io/badge/HuggingFace-STEM-blue)](https://huggingface.co/datasets/eduzrh/STEM)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+The **first spatio-temporal 3D entity matching benchmark** for evaluating matching algorithms on 3D building objects across both different data sources and different time points. Designed for both **unsupervised** and **few-shot** entity matching.
+
+## Overview
+
+Traditional entity matching operates on tabular records. But real-world geospatial entities — buildings, bridges, infrastructure — are inherently 3D objects. **STEM** enables rigorous evaluation of 3D entity matching under:
+
+- **Cross-source** scenarios (different data providers, LOD levels)
+- **Cross-time** scenarios (multi-epoch building inventories)
+- **Post-disaster** scenarios (CRS misalignment + structural damage)
+
+## Quick Start
+
+```bash
+# Clone the repo
+git clone https://huggingface.co/datasets/eduzrh/STEM
+cd STEM
+
+# Install dependencies
+pip install -r code/3dSAGER/requirements.txt
+
+# Run baseline experiment
+cd code/3dSAGER
+python main.py --dataset_name Hague --evaluation_mode matching --blocking_method bkafi
+```
+
+## Benchmark Tasks
+
+| Task | Description | Difficulty |
+|---|---|---|
+| **Task 1: Cross-Source** | Match buildings across different data providers (same epoch) | Medium |
+| **Task 2: Cross-Time** | Match buildings across different time points (same provider) | Hard |
+| **Task 3: Full ST-EM** | Match across both source AND time + disaster simulation | Very Hard |
+
+## Datasets
+
+| Dataset | Buildings | Epochs | Format | Status |
+|---|---|---|---|---|
+| The Hague (3D BAG) | 155,699 | 1 (2 sources) | CityJSON | ✅ Integrated |
+| Lyon Multi-Epoch | ~150K/epoch | 4 (2009-2018) | CityGML | 🔄 In Progress |
+
+## Baseline Results (The Hague, small subset)
+
+| Model | Precision | Recall | F1 |
+|---|---|---|---|
+| XGBoost | 0.997 | 1.0 | 0.998 |
+| Random Forest | 0.997 | 1.0 | 0.998 |
+| AdaBoost | 0.991 | 1.0 | 0.995 |
+| MLP | 0.958 | 0.979 | 0.968 |
+
+*With disaster simulation (CRS rotation 184° + damage 80%) and RANSAC alignment (rotation error 0.03°)*
+
+## Repository Structure
+
+```
+STEM/
+├── README.md
+├── docs/
+│   └── STEM_benchmark_specification.md    # Full benchmark specification
+├── code/
+│   └── 3dSAGER/                            # Core pipeline adapted for STEM
+│       ├── main.py
+│       ├── pipelines.py
+│       ├── disaster_simulation.py
+│       ├── alignment.py
+│       └── config.py
+├── data/
+│   └── hague/                              # The Hague baseline data
+├── experiments/
+│   ├── results/                            # Experiment outputs & logs
+│   ├── run_full_pipeline.sh
+│   └── run_experiments.sh
+└── .gitattributes
+```
+
+## Citation
+
+```bibtex
+@misc{stem2026,
+  title={STEM: A Spatio-Temporal 3D Entity Matching Benchmark},
+  author={Edu, ZRH},
+  year={2026},
+  publisher={HuggingFace},
+  howpublished={\url{https://huggingface.co/datasets/eduzrh/STEM}}
+}
+```
+
+## License
+
+MIT License — see LICENSE file for details.

code/3dSAGER/alignment.py

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+"""
+alignment.py
+
+Two-stage rigid 3D alignment of candidate buildings onto the index coordinate frame.
+
+Stage 1 — Estimate transform (Arun et al. 1987):
+    Selects high-confidence matched pairs as anchors, estimates rotation R and
+    translation t via SVD least-squares, then validates the result by computing
+    per-anchor residuals.  If the mean residual exceeds the configured threshold
+    the alignment is rejected and the pipeline continues with geometric scores only.
+
+Stage 2 — Re-score and output:
+    Applies (R, t) to all cand centroids and re-scores every pair as:
+        final_score = alpha * geometric_score + (1 - alpha) * spatial_score
+    where spatial_score = 1 / (1 + distance_after_alignment).
+    Then applies (R, t) to all cand geometry and writes an aligned CityJSON file.
+
+Reference:
+    K. S. Arun, T. S. Huang, and S. D. Blostein. 1987.
+    Least-Squares Fitting of Two 3-D Point Sets.
+    IEEE TPAMI 9(5):698-700. doi:10.1109/TPAMI.1987.4767965
+
+Usage:
+    from alignment import RigidAligner
+    import config
+
+    aligner = RigidAligner(config.Alignment, logger=logger)
+    rescored_pairs = aligner.run(
+        object_dict,
+        scored_pairs,          # list of (cand_id, index_id, geometric_score)
+        suffix="seed1",
+        ground_truth_R=simulator.R_crs,   # optional, for evaluation only
+        ground_truth_t=simulator.t_crs,
+    )
+    # rescored_pairs: list of (cand_id, index_id, final_score)
+    # results/aligned_candidates_seed1.json written if alignment succeeded
+"""
+
+import json
+import os
+import logging
+import numpy as np
+from typing import List, Tuple, Optional
+import config as cfg
+
+
+class RigidAligner:
+    """
+    Estimates a rigid 3D transform from anchor pairs and re-scores all matches.
+
+    Parameters
+    ----------
+    align_config : config.Alignment (class reference)
+    logger : logging.Logger  (optional)
+    """
+
+    def __init__(self, align_config=None, logger=None):
+        if align_config is None:
+            align_config = cfg.Alignment
+        self.enabled = align_config.enabled
+        self.min_anchor_pairs = align_config.min_anchor_pairs
+        self.confidence_threshold = align_config.confidence_threshold
+        self.max_residual_threshold = align_config.max_residual_threshold
+        self.alpha = align_config.alpha
+        self.output_crs = align_config.output_crs
+        self.use_ransac = getattr(align_config, 'use_ransac', True)
+        self.ransac_iterations = getattr(align_config, 'ransac_iterations', 1000)
+        self.ransac_inlier_threshold = getattr(align_config, 'ransac_inlier_threshold', 10.0)
+        self.spatial_sigma = getattr(align_config, 'spatial_sigma', 3.0)
+        self.logger = logger or logging.getLogger(__name__)
+
+        # Set after a successful alignment
+        self.R: Optional[np.ndarray] = None
+        self.t: Optional[np.ndarray] = None
+        self.mean_residual: Optional[float] = None
+        self.n_anchors: int = 0
+        self.alignment_succeeded: bool = False
+
+    # ------------------------------------------------------------------ #
+    # Public API
+    # ------------------------------------------------------------------ #
+
+    def run(
+        self,
+        object_dict: dict,
+        scored_pairs: List[Tuple[str, str, float]],
+        suffix: str = "",
+        ground_truth_R: Optional[np.ndarray] = None,
+        ground_truth_t: Optional[np.ndarray] = None,
+    ) -> List[Tuple[str, str, float]]:
+        """
+        Full alignment pipeline: estimate transform → validate → re-score → output.
+
+        Parameters
+        ----------
+        object_dict : dict
+            Full object dict with 'cands' and 'index'.
+        scored_pairs : list of (cand_id, index_id, geometric_score)
+            All test pairs with their classifier probability scores.
+        suffix : str
+            Appended to the output filename.
+        ground_truth_R, ground_truth_t : np.ndarray, optional
+            If provided (from DisasterSimulator), the alignment error is logged
+            for evaluation purposes.  Not used in the alignment itself.
+
+        Returns
+        -------
+        list of (cand_id, index_id, final_score)
+            If alignment succeeded: final_score = alpha*geometric + (1-alpha)*spatial.
+            If alignment failed/skipped: final_score = geometric_score unchanged.
+        """
+        if not self.enabled:
+            self.logger.info("[RigidAligner] Disabled — returning geometric scores.")
+            return scored_pairs
+
+        # --- Stage 1: Estimate transform ---
+        anchors = self._select_anchors(scored_pairs, object_dict)
+        self.n_anchors = len(anchors)
+
+        if self.n_anchors < self.min_anchor_pairs:
+            self.logger.warning(
+                f"[RigidAligner] Only {self.n_anchors} anchor pairs found "
+                f"(need {self.min_anchor_pairs}, threshold={self.confidence_threshold}). "
+                f"Skipping alignment — returning geometric scores."
+            )
+            return scored_pairs
+
+        P, Q = self._build_point_sets(anchors, object_dict)
+        if self.use_ransac:
+            R, t, n_inliers, inlier_mask = self._estimate_rigid_transform_ransac(P, Q)
+            # Validate using INLIER mean residual when RANSAC found enough inliers.
+            # The all-anchor mean is dominated by false-positive anchors (look-alike
+            # buildings the classifier scored high) and unfairly rejects a correct
+            # transform whenever anchor-pool precision is low.
+            if n_inliers >= self.min_anchor_pairs and inlier_mask.any():
+                self.mean_residual = self._compute_residual(P[inlier_mask], Q[inlier_mask], R, t)
+                all_anchor_mean = self._compute_residual(P, Q, R, t)
+                self.logger.info(
+                    f"[RigidAligner] RANSAC: {self.n_anchors} anchors | "
+                    f"{n_inliers} inliers | inlier mean residual = {self.mean_residual:.2f} m "
+                    f"(all-anchor mean = {all_anchor_mean:.2f} m)"
+                )
+            else:
+                self.mean_residual = self._compute_residual(P, Q, R, t)
+                self.logger.info(
+                    f"[RigidAligner] RANSAC: {self.n_anchors} anchors | "
+                    f"only {n_inliers} inliers (< {self.min_anchor_pairs}) | "
+                    f"all-anchor mean residual = {self.mean_residual:.2f} m"
+                )
+        else:
+            R, t = self._estimate_rigid_transform(P, Q)
+            self.mean_residual = self._compute_residual(P, Q, R, t)
+            self.logger.info(
+                f"[RigidAligner] {self.n_anchors} anchors | "
+                f"mean residual = {self.mean_residual:.2f} m"
+            )
+
+        if self.mean_residual > self.max_residual_threshold:
+            self.logger.warning(
+                f"[RigidAligner] Mean residual {self.mean_residual:.2f} m exceeds "
+                f"threshold {self.max_residual_threshold} m. "
+                f"Alignment rejected — returning geometric scores."
+            )
+            return scored_pairs
+
+        self.R, self.t = R, t
+        self.alignment_succeeded = True
+
+        # Optional: log error vs ground-truth transform
+        if ground_truth_R is not None and ground_truth_t is not None:
+            self._log_ground_truth_error(ground_truth_R, ground_truth_t)
+
+        # --- Stage 2: Re-score using aligned positions ---
+        rescored = self._rescore_pairs(scored_pairs, object_dict)
+
+        # Apply transform to full cand geometry and write output
+        self._apply_transform_to_geometry(object_dict['cands'])
+        self._write_cityjson(object_dict['cands'], suffix)
+
+        return rescored
+
+    # ------------------------------------------------------------------ #
+    # Stage 1 helpers
+    # ------------------------------------------------------------------ #
+
+    def _select_anchors(
+        self,
+        scored_pairs: List[Tuple[str, str, float]],
+        object_dict: dict,
+    ) -> List[Tuple[str, str]]:
+        """Return (cand_id, index_id) pairs with score >= confidence_threshold."""
+        cands_keys = set(object_dict['cands'].keys())
+        index_keys = set(object_dict['index'].keys())
+        return [
+            (cid, iid)
+            for cid, iid, score in scored_pairs
+            if score >= self.confidence_threshold
+            and cid in cands_keys
+            and iid in index_keys
+        ]
+
+    @staticmethod
+    def _build_point_sets(
+        anchors: List[Tuple[str, str]],
+        object_dict: dict,
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Build point sets from anchor centroids.
+        P = index centroids (target), Q = cand centroids (source).
+        Goal: find R, t such that P_i ≈ R @ Q_i + t.
+        """
+        P = np.array([object_dict['index'][iid]['centroid'] for _, iid in anchors], dtype=np.float64)
+        Q = np.array([object_dict['cands'][cid]['centroid'] for cid, _ in anchors], dtype=np.float64)
+        return P, Q
+
+    @staticmethod
+    def _estimate_rigid_transform(
+        P: np.ndarray,
+        Q: np.ndarray,
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Arun et al. 1987 SVD least-squares rigid transform.
+
+        Returns R (3x3) and t (3,) such that P_i ≈ R @ Q_i + t.
+        """
+        p_bar = P.mean(axis=0)   # (3,)
+        q_bar = Q.mean(axis=0)   # (3,)
+        P_prime = P - p_bar      # (N, 3) centered
+        Q_prime = Q - q_bar      # (N, 3) centered
+
+        H = Q_prime.T @ P_prime  # (3, 3) cross-covariance
+        U, _, Vt = np.linalg.svd(H)
+        V = Vt.T
+
+        R = V @ U.T
+
+        # Fix reflection (degenerate / coplanar case)
+        if np.linalg.det(R) < 0:
+            V[:, 2] *= -1
+            R = V @ U.T
+
+        t = p_bar - R @ q_bar
+        return R, t
+
+    def _estimate_rigid_transform_ransac(
+        self,
+        P: np.ndarray,
+        Q: np.ndarray,
+    ):
+        """
+        RANSAC-based rigid transform estimation.
+
+        Each iteration samples 3 anchor pairs, estimates R and t via SVD,
+        then counts how many of all anchors are consistent (inliers) under
+        that transform.  The best transform (most inliers) is refitted on
+        all its inliers via SVD for a final least-squares solution.
+
+        Parameters
+        ----------
+        P : (N, 3) index centroids
+        Q : (N, 3) cand centroids
+
+        Returns
+        -------
+        R : (3, 3) rotation matrix
+        t : (3,) translation vector
+        n_inliers : int
+        """
+        n = len(P)
+        best_inlier_mask = np.zeros(n, dtype=bool)
+        best_n_inliers = 0
+        best_R, best_t = self._estimate_rigid_transform(P, Q)  # fallback
+
+        rng = np.random.default_rng(42)
+
+        for _ in range(self.ransac_iterations):
+            # Sample 3 unique anchor pairs
+            idx = rng.choice(n, size=3, replace=False)
+            P_sample, Q_sample = P[idx], Q[idx]
+
+            # Skip degenerate (collinear) samples
+            if np.linalg.matrix_rank(P_sample - P_sample.mean(axis=0)) < 2:
+                continue
+
+            R_cand, t_cand = self._estimate_rigid_transform(P_sample, Q_sample)
+
+            # Count inliers: anchors whose residual < threshold under this transform
+            P_hat = (R_cand @ Q.T).T + t_cand
+            residuals = np.linalg.norm(P_hat - P, axis=1)
+            inlier_mask = residuals < self.ransac_inlier_threshold
+            n_inliers = inlier_mask.sum()
+
+            if n_inliers > best_n_inliers:
+                best_n_inliers = n_inliers
+                best_inlier_mask = inlier_mask
+                best_R, best_t = R_cand, t_cand
+
+        # Refit on all inliers of the best solution
+        if best_n_inliers >= 3:
+            best_R, best_t = self._estimate_rigid_transform(
+                P[best_inlier_mask], Q[best_inlier_mask]
+            )
+            # Recompute inlier mask under the refit transform so it reflects the
+            # final R, t rather than the 3-sample one used to score iterations.
+            P_hat = (best_R @ Q.T).T + best_t
+            residuals = np.linalg.norm(P_hat - P, axis=1)
+            best_inlier_mask = residuals < self.ransac_inlier_threshold
+            best_n_inliers = int(best_inlier_mask.sum())
+            self.logger.info(
+                f"[RigidAligner] RANSAC refit on {best_n_inliers}/{n} inliers "
+                f"(threshold={self.ransac_inlier_threshold} m, "
+                f"iterations={self.ransac_iterations})"
+            )
+        else:
+            self.logger.warning(
+                f"[RigidAligner] RANSAC found only {best_n_inliers} inliers — "
+                f"falling back to full SVD"
+            )
+
+        return best_R, best_t, best_n_inliers, best_inlier_mask
+
+    @staticmethod
+    def _compute_residual(
+        P: np.ndarray,
+        Q: np.ndarray,
+        R: np.ndarray,
+        t: np.ndarray,
+    ) -> float:
+        """Mean Euclidean residual ||R @ q_i + t - p_i|| over all anchor pairs."""
+        P_hat = (R @ Q.T).T + t   # (N, 3)
+        residuals = np.linalg.norm(P_hat - P, axis=1)
+        return float(residuals.mean())
+
+    def _log_ground_truth_error(
+        self,
+        gt_R: np.ndarray,
+        gt_t: np.ndarray,
+    ) -> None:
+        """
+        Log rotation and translation error vs the ground-truth CRS transform.
+
+        The disaster simulator applies:  cand = R_crs @ original + t_crs
+        So the aligner should recover the INVERSE transform:
+            self.R ≈ R_crs^T   (so that R_crs^T @ R_crs = I)
+            self.t ≈ -R_crs^T @ t_crs
+
+        Rotation check: self.R @ gt_R should be close to I.
+        Translation check: self.t should be close to -gt_R^T @ gt_t.
+        """
+        # Rotation error: R_recovered @ R_gt should equal I if perfect
+        R_check = self.R @ gt_R
+        angle_err = np.degrees(np.arccos(
+            np.clip((np.trace(R_check) - 1.0) / 2.0, -1.0, 1.0)
+        ))
+        # Translation error: compare recovered t to the expected inverse translation
+        expected_t = -gt_R.T @ gt_t
+        t_err = np.linalg.norm(self.t - expected_t)
+        self.logger.info(
+            f"[RigidAligner] Ground-truth comparison: "
+            f"rotation error = {angle_err:.2f}°, "
+            f"translation error = {t_err:.1f} m"
+        )
+
+    # ------------------------------------------------------------------ #
+    # Stage 2 helpers
+    # ------------------------------------------------------------------ #
+
+    def _rescore_pairs(
+        self,
+        scored_pairs: List[Tuple[str, str, float]],
+        object_dict: dict,
+    ) -> List[Tuple[str, str, float]]:
+        """
+        Re-score pairs combining geometric score with spatial proximity
+        after aligning cand centroids to the index frame.
+
+        final_score = alpha * geometric_score + (1 - alpha) * spatial_score
+        spatial_score = exp(- d² / (2 · spatial_sigma²))     # Gaussian, σ default 3 m
+        """
+        # Apply R, t to cand centroids only (fast; full geometry updated later)
+        aligned_cand_centroids = {
+            bid: self.R @ np.asarray(data['centroid'], dtype=np.float64) + self.t
+            for bid, data in object_dict['cands'].items()
+        }
+        index_centroids = {
+            bid: np.asarray(data['centroid'], dtype=np.float64)
+            for bid, data in object_dict['index'].items()
+        }
+
+        rescored = []
+        for cid, iid, geo_score in scored_pairs:
+            if cid in aligned_cand_centroids and iid in index_centroids:
+                dist = float(np.linalg.norm(aligned_cand_centroids[cid] - index_centroids[iid]))
+                # Gaussian decay with σ = spatial_sigma (default 3 m, ≈ median true-match
+                # residual). Sharply suppresses look-alikes that land 5–10 m away while
+                # giving high scores to genuine matches at d ≤ σ.
+                spatial_score = float(np.exp(-(dist * dist) / (2.0 * self.spatial_sigma ** 2)))
+                final_score = self.alpha * geo_score + (1.0 - self.alpha) * spatial_score
+            else:
+                final_score = geo_score   # fallback if ID missing
+            rescored.append((cid, iid, final_score))
+
+        # Log score distribution summary
+        geo_scores = [s for _, _, s in scored_pairs]
+        final_scores = [s for _, _, s in rescored]
+        self.logger.info(
+            f"[RigidAligner] Score re-scaling: "
+            f"geometric mean={np.mean(geo_scores):.3f} → "
+            f"final mean={np.mean(final_scores):.3f} "
+            f"(alpha={self.alpha})"
+        )
+        return rescored
+
+    def _apply_transform_to_geometry(self, cands: dict) -> None:
+        """Apply (R, t) to all cand vertices, centroids, and polygon_mesh."""
+        for building in cands.values():
+            verts = building['vertices']
+            building['vertices'] = (self.R @ verts.T).T + self.t
+            building['centroid'] = self.R @ np.asarray(building['centroid'], dtype=np.float64) + self.t
+            new_mesh = []
+            for surface in building['polygon_mesh']:
+                new_surface = [(self.R @ np.array(c, dtype=np.float64) + self.t).tolist() for c in surface]
+                new_mesh.append(new_surface)
+            building['polygon_mesh'] = new_mesh
+
+    # ------------------------------------------------------------------ #
+    # CityJSON output
+    # ------------------------------------------------------------------ #
+
+    def _write_cityjson(self, cands: dict, suffix: str) -> None:
+        """
+        Write aligned candidates to a CityJSON 1.1 file in the index CRS.
+
+        Vertices are stored as floating-point world coordinates (no re-quantization).
+        Each building is written as a Solid LOD2 geometry preserving the original
+        surface structure.
+        """
+        results_dir = cfg.FilePaths.results_path
+        os.makedirs(results_dir, exist_ok=True)
+        out_path = os.path.join(results_dir, f"aligned_candidates_{suffix}.json")
+
+        city_objects = {}
+        all_vertices = []
+        vertex_index = {}   # (x, y, z) rounded → global index
+
+        epsg_code = self.output_crs.split(":")[-1]
+
+        for bid, building in cands.items():
+            verts = building['vertices']   # (N, 3) already aligned
+
+            # Build local vertex index
+            local_idx = {}
+            for v in verts:
+                key = (round(float(v[0]), 6), round(float(v[1]), 6), round(float(v[2]), 6))
+                if key not in vertex_index:
+                    vertex_index[key] = len(all_vertices)
+                    all_vertices.append(list(key))
+                local_idx[key] = vertex_index[key]
+
+            # Encode polygon_mesh as surface boundary index lists
+            boundaries = []
+            for surface in building['polygon_mesh']:
+                ring = []
+                for coord in surface:
+                    key = (round(float(coord[0]), 6), round(float(coord[1]), 6), round(float(coord[2]), 6))
+                    # Find the nearest stored key (handles float drift)
+                    if key not in vertex_index:
+                        key = min(
+                            local_idx.keys(),
+                            key=lambda k: (k[0]-key[0])**2 + (k[1]-key[1])**2 + (k[2]-key[2])**2
+                        )
+                    ring.append(vertex_index[key])
+                boundaries.append([ring])
+
+            city_objects[f"bag_{bid}"] = {
+                "type": "Building",
+                "geometry": [{
+                    "type": "Solid",
+                    "lod": "2",
+                    "boundaries": [boundaries]
+                }],
+                "attributes": building.get("attributes", {})
+            }
+
+        cityjson = {
+            "type": "CityJSON",
+            "version": "1.1",
+            "metadata": {
+                "referenceSystem": f"https://www.opengis.net/def/crs/EPSG/0/{epsg_code}"
+            },
+            "CityObjects": city_objects,
+            "vertices": all_vertices,
+            "alignment_info": {
+                "mean_residual_m": round(self.mean_residual, 3),
+                "n_anchor_pairs": self.n_anchors,
+                "alpha": self.alpha
+            }
+        }
+
+        with open(out_path, 'w', encoding='utf-8') as f:
+            json.dump(cityjson, f, indent=2)
+
+        size_mb = os.path.getsize(out_path) / 1e6
+        self.logger.info(
+            f"[RigidAligner] Aligned CityJSON written: {out_path} "
+            f"({len(city_objects)} buildings, {size_mb:.1f} MB)"
+        )

code/3dSAGER/config.py

@@ -0,0 +1,173 @@
+
+
+class FilePaths:
+    results_path = "results/"
+    saved_models_path = "saved_model_files/"
+    object_dict_path = "data/object_dicts/"
+    dataset_dict_path = "data/dataset_dicts/"
+    property_dict_path = "data/property_dicts/"
+    dataset_partition_path = "data/dataset_partitions/"
+
+
+class Constants:
+    dataset_name = "Hague"  # "Hague", "delivery3", "bo_em", "gpkg"
+    synthetic_folder_name = "example"  # Relevant only if dataset_name is "synthetic"
+    evaluation_mode = "matching"  # "blocking", "matching"
+    dataset_size_version = 'medium'  # 'small', 'medium', 'large'
+    matching_cands_generation = 'negative_sampling'  # 'negative_sampling', 'blocking-based'
+    neg_samples_num = 2  # 2, 5
+    seeds_num = 1
+    train_ratio = 0.6
+    val_ratio = 0.2
+    test_ratio = 1 - train_ratio - val_ratio
+    max_ratio_val = 1000  # Avoid infinity values
+    load_object_dict = False  # Must be False when disaster simulation is active
+    save_object_dict = True  # Cache object dict to avoid reloading from disk
+    load_train_items = False  # Load existing preparatory items
+    save_property_dict = True  # Cache property dict to avoid recomputing
+    load_property_dict = False  # Load the properties dictionary
+    save_dataset_dict = True  # Cache dataset dict to avoid recomputing
+    load_dataset_dict = False  # load existing dataset dictionary
+    file_name_suffix = 'allmodels_v1'  # Stable suffix so model files are reusable across runs
+    max_grid_cells = 20  # None = all cells; 50 = medium run on 8GB RAM
+
+
+class TrainingPhase:
+    training_ratio = 0.5  # Number of positive samples
+    neg_pairs_ratio = 4  # Number of negative samples per positive sample
+    run_preparatory_phase = True  # If False, the preparatory phase will not be run
+
+
+class Features:
+    knn_buildings = 0  # Number of nearest buildings to consider
+    knn_roads = 0  # Number of nearest roads to consider
+    operator = 'division'  # 'division', 'concatenation'
+    object_properties = ["bounding_box_width", "bounding_box_length", "area", "perimeter", "perimeter_ind",
+                         "volume", "convex_hull_area", "convex_hull_volume", "ave_centroid_distance", "height_diff",
+                         "num_floors", "axes_symmetry", "compactness_2d", "compactness_3d", "density",
+                         "elongation", "shape_ind", "hemisphericality", "fractality", "cubeness", "circumference",
+                         "aligned_bounding_box_width", "aligned_bounding_box_length", "aligned_bounding_box_height",
+                         "num_vertices"]
+
+    # object_properties = ["circumference", "density", "convex_hull_area"]
+    normalization = 'log_transform'  # 'log_transform', None
+    neighborhood = []
+    roads = []
+
+
+class Blocking:
+    blocking_method = 'bkafi'  # 'bkafi', 'bkafi_without_SDR', 'ViT-B_32', 'ViT-L_14', 'centroid'
+                                # 'coordinates', 'coordinates_transformed'
+    cand_pairs_per_item_list = [i for i in range(1, 21)]  # total number of neighbors per each candidate object
+    nn_param = cand_pairs_per_item_list[-1] + 1  # number of nearest neighbors to retrieve as candidates
+    nbits = 10  # number of bits to use for LSH
+    # bkafi_dim_list = [dim for dim in range(1, len(Features.object_properties))] # Number of important features to
+    # use for blocking (for the bkafi method)
+    bkafi_dim_list = [dim for dim in range(1, len(Features.object_properties))]  # Number of important features to use
+    dist_threshold = None  # Define it as a hyperparameter or in a flexible manner
+    sdr_factor = False  # If True, the SDR factor will be used in the blocking method
+    bkafi_criterion = 'feature_importance'  # 'std', 'feature_importance'
+    # Neighborhood-aware negative sampling
+    neighborhood_radius = 500.0   # meters (EPSG:7415) — radius for spatial negative sampling
+    neighborhood_neg_ratio = 0.7  # fraction of negatives drawn from within-radius neighbors vs. random
+
+
+class DataPartition:
+    grid_cell_size = 500.0   # meters (EPSG:7415) — side length of each spatial grid cell
+    train_ratio = 0.6        # fraction of grid cells assigned to training
+    contiguous_test = True   # If True, test cells form a spatially contiguous region (BFS from
+                             # a corner) so the demo app covers a coherent train-only area.
+
+
+class DisasterSimulation:
+    enabled = True
+    # CRS simulation — random rotation + large translation applied globally to all cands
+    crs_simulation = True    # simulate unknown CRS (no absolute reference)
+    # Damage simulation — per-building random height reduction
+    damage_probability = 0.8    # fraction of cand buildings to damage
+    min_damage_factor = 0.3     # minimum remaining height fraction (0.3 = 70% collapsed)
+    max_damage_factor = 0.95    # maximum remaining height fraction (near-undamaged)
+
+
+class Alignment:
+    enabled = True
+    min_anchor_pairs = 3          # minimum high-confidence matches required to attempt alignment
+    confidence_threshold = 0.8    # geometric classifier score threshold for anchor selection
+    max_residual_threshold = 50.0 # meters — reject alignment if mean anchor error exceeds this
+    alpha = 0.5                   # weight: 1.0 = geometric score only, 0.0 = spatial score only
+    output_crs = "EPSG:7415"      # index dataset CRS — output aligned CityJSON in this CRS
+    use_ransac = True             # use RANSAC to find robust transform instead of plain SVD
+    ransac_iterations = 1000      # number of RANSAC trials
+    ransac_inlier_threshold = 10.0  # meters — anchor is inlier if residual < this after applying R, t
+    spatial_sigma = 3.0           # meters — Gaussian decay length for post-alignment spatial score.
+                                  # spatial(d) = exp(-d²/(2·σ²)). σ ≈ median true-match residual;
+                                  # σ=3 m gives spatial(0)=1, spatial(3)=0.61, spatial(10)≈0.004.
+    post_align_knn_cutoff = 7.0   # meters — for --post-align-blocking mode in demo/inference.py.
+                                  # After alignment succeeds, replace BKAFI pool with per-cand 1-NN
+                                  # against full index; accept iff post-alignment distance ≤ cutoff.
+
+
+class Models:
+    load_trained_models = False
+    cv = 3
+    model_to_use = 'XGBClassifier'  # Used only for predict.py and feature_importances.py
+    model_list = ['XGBClassifier',  # 'GradientBoostingClassifier', 'BaggingClassifier',
+                  'RandomForestClassifier', 'AdaBoostClassifier', 'MLPClassifier']
+    blocking_model = 'RandomForestClassifier'  # Used only for blocking and for advanced evaluation
+    params_dict = {
+                    'RandomForestClassifier': {"n_estimators": [50],
+                                               "max_depth": [5],
+                                               "min_samples_split": [2],
+                                               "max_features": ["sqrt"]},
+
+                    'SVC': {'C': [0.1, 0.5],
+                           'kernel': ['rbf'],
+                           'gamma': ['scale'],
+                           'degree': [2]
+                            },
+
+                    'LogisticRegression': {'solver': ['lbfgs', 'saga'],
+                                          'multi_class': ['auto'],
+                                          'C': [0.01, 0.1, 1]
+                                           },
+
+                    'MLPClassifier': {'hidden_layer_sizes': [(64, 32)],
+                                      'activation': ['relu'],
+                                      'solver': ['adam'],
+                                      'batch_size': [16],
+                                      'max_iter': [500],
+                                      'early_stopping': [True],
+                                      'n_iter_no_change': [20],
+                                      },
+
+                    'AdaBoostClassifier': {'n_estimators': [100],
+                                           'learning_rate': [0.1],
+                                           'algorithm': ['SAMME']
+                                           },
+
+                    'GradientBoostingClassifier': {'loss': ['log_loss'],
+                                                   'learning_rate': [0.1],
+                                                   'n_estimators': [100],
+                                                   'max_depth': [3],
+                                                   'min_samples_split': [3],
+                                                   'max_features': ['sqrt']
+                                                   },
+
+                    'BaggingClassifier': {'n_estimators': [50],
+                                          'max_samples': [0.8],
+                                          'max_features': [0.8],
+                                          'bootstrap': [True]
+                                          },
+
+                    'XGBClassifier': {'max_depth': [4],
+                                      'objective': ['binary:logistic'],
+                                      'learning_rate': [0.1],
+                                      'n_estimators': [100],
+                                      'gamma': [0],
+                                      'tree_method': ['hist'],
+                                      'n_jobs': [4],
+                                      }
+    }
+
+
+

code/3dSAGER/dataset_configs.json

@@ -0,0 +1,36 @@
+{
+    "bo_em": {
+        "cands_path": "data/240823/em/",
+        "index_path": "data/240823/bo/",
+        "roads_path": "data/roads/roads1.geojson",
+        "general_file_name": "em_bo1",
+        "object_dict_path": "data/240823/"
+    },
+    "gpkg": {
+        "cands_path": "data/200224/history2021/",
+        "index_path": "data/200224/solid2021/",
+        "roads_path": "data/roads/roads1.geojson",
+        "general_file_name": "gpkg1",
+        "object_dict_path": "data/200224/"
+    },
+    "delivery3": {
+        "index_path": "data/210424/all_pairs/set_1/new/",
+        "cands_path": "data/210424/all_pairs/set_2/new/",
+        "roads_path": "data/roads/roads1.geojson",
+        "general_file_name": "delivery3_all_pairs",
+        "object_dict_path": "data/210424/all_pairs/"
+    },
+    "Hague": {
+        "cands_path": "../The Hague/Source B/",
+        "index_path": "../The Hague/Source A/",
+        "roads_path": "",
+        "general_file_name": "Hague",
+        "object_dict_path": "data/object_dicts/Hague/"
+    },
+    "synthetic_example": {
+        "cands_path": "data/synthetic/example/LOD3-0.2.json",
+        "index_path": "data/synthetic/example/LOD3.json",
+        "roads_path": "",
+        "general_file_name": "synthetic_example"
+    }
+}

code/3dSAGER/disaster_simulation.py

@@ -0,0 +1,221 @@
+"""
+disaster_simulation.py
+
+Simulates a post-disaster scenario on the candidate building set.
+
+Two independent simulations are applied in order (CRS first, then damage):
+
+  1. CRS simulation (global):
+     All cand buildings are rotated by a random angle around the Z-axis and shifted
+     by a large random translation, simulating a dataset with no absolute coordinate
+     reference.  The internal geometry of each building is preserved exactly; only
+     the global frame changes.  This forces the model to rely on rotation-invariant
+     features (aligned BB, volume, area, compactness) rather than axis-aligned ones.
+
+  2. Damage simulation (per-building):
+     A random subset of cand buildings have their height reduced, simulating partial
+     collapse.  Each damaged building keeps its ground footprint but loses height
+     according to a random damage factor.
+
+Only 'cands' are ever modified.  The 'index' (reference dataset) is never touched.
+
+Usage:
+    from disaster_simulation import DisasterSimulator
+    import config
+
+    simulator = DisasterSimulator(config.DisasterSimulation, seed=1)
+    object_dict = simulator.apply(object_dict)
+    # simulator.R_crs, simulator.t_crs  — ground-truth transform for evaluation
+    # simulator.damage_log              — per-building damage factors for inspection
+"""
+
+import numpy as np
+import config as cfg
+
+
+class DisasterSimulator:
+    """
+    Applies CRS simulation and damage simulation to the candidate set.
+
+    Parameters
+    ----------
+    sim_config : config.DisasterSimulation (class reference)
+    seed : int
+        Controls randomness for both simulations.  Different seeds per pipeline
+        run ensure the model trains on varied scenarios.
+    """
+
+    _Z_EPSILON = 1e-4   # threshold to distinguish above-ground vertices from ground
+
+    def __init__(self, sim_config=None, seed=42):
+        if sim_config is None:
+            sim_config = cfg.DisasterSimulation
+        self.enabled = sim_config.enabled
+        self.crs_simulation = sim_config.crs_simulation
+        self.damage_probability = sim_config.damage_probability
+        self.min_damage_factor = sim_config.min_damage_factor
+        self.max_damage_factor = sim_config.max_damage_factor
+        self._rng = np.random.default_rng(seed)
+
+        # Set after apply() — expose for external evaluation
+        self.R_crs = None      # (3,3) rotation matrix applied to all cands
+        self.t_crs = None      # (3,) translation vector applied to all cands
+        self.damage_log = {}   # {building_id: damage_factor}  (1.0 = undamaged)
+
+    # ------------------------------------------------------------------ #
+    # Public API
+    # ------------------------------------------------------------------ #
+
+    def apply(self, object_dict: dict) -> dict:
+        """
+        Apply CRS simulation then damage simulation to cands in-place.
+
+        Parameters
+        ----------
+        object_dict : dict
+            Full object dict with keys 'cands', 'index', 'mapping_dict', etc.
+
+        Returns
+        -------
+        dict
+            Same object_dict with modified cands.
+        """
+        if not self.enabled:
+            return object_dict
+
+        if self.crs_simulation:
+            object_dict = self._apply_crs_simulation(object_dict)
+
+        object_dict = self._apply_damage_simulation(object_dict)
+
+        self._print_summary(object_dict)
+        return object_dict
+
+    # ------------------------------------------------------------------ #
+    # CRS simulation
+    # ------------------------------------------------------------------ #
+
+    def _apply_crs_simulation(self, object_dict: dict) -> dict:
+        """
+        Apply a single random rotation (around Z) + large translation to ALL cands.
+
+        The same (R_crs, t_crs) is applied to every building so internal
+        relative geometry is preserved — only the global frame changes.
+        """
+        # Random rotation angle in [0, 2π)
+        theta = self._rng.uniform(0.0, 2.0 * np.pi)
+        cos_t, sin_t = np.cos(theta), np.sin(theta)
+        self.R_crs = np.array([
+            [ cos_t, -sin_t, 0.0],
+            [ sin_t,  cos_t, 0.0],
+            [   0.0,    0.0, 1.0]
+        ])
+
+        # Large random translation — no absolute reference
+        tx = self._rng.uniform(-100_000.0, 100_000.0)
+        ty = self._rng.uniform(-100_000.0, 100_000.0)
+        self.t_crs = np.array([tx, ty, 0.0])
+
+        for bid, building in object_dict['cands'].items():
+            self._transform_building(building, self.R_crs, self.t_crs)
+
+        print(f"[DisasterSimulator] CRS simulation applied: "
+              f"rotation={np.degrees(theta):.1f}°, "
+              f"translation=({tx:.0f}, {ty:.0f}) m")
+        return object_dict
+
+    @staticmethod
+    def _transform_building(building: dict, R: np.ndarray, t: np.ndarray) -> None:
+        """Apply rigid transform (R, t) to all geometry of one building in-place."""
+        # vertices: (N, 3)
+        verts = building['vertices']
+        building['vertices'] = (R @ verts.T).T + t
+
+        # centroid: (3,)
+        building['centroid'] = R @ np.asarray(building['centroid'], dtype=np.float64) + t
+
+        # polygon_mesh: list of surfaces, each surface = list of [x, y, z]
+        new_mesh = []
+        for surface in building['polygon_mesh']:
+            new_surface = []
+            for coord in surface:
+                v = np.array(coord, dtype=np.float64)
+                new_surface.append((R @ v + t).tolist())
+            new_mesh.append(new_surface)
+        building['polygon_mesh'] = new_mesh
+
+    # ------------------------------------------------------------------ #
+    # Damage simulation
+    # ------------------------------------------------------------------ #
+
+    def _apply_damage_simulation(self, object_dict: dict) -> dict:
+        """
+        Randomly reduce height of a fraction of cand buildings.
+
+        Each damaged building keeps its ground footprint but all vertices
+        above z_min are scaled: z_new = z_min + (z - z_min) * damage_factor
+        """
+        cands = object_dict['cands']
+        cand_ids = list(cands.keys())
+        n_to_damage = int(round(self.damage_probability * len(cand_ids)))
+
+        # Select which buildings to damage
+        damaged_indices = self._rng.choice(len(cand_ids), size=n_to_damage, replace=False)
+        damaged_ids = [cand_ids[i] for i in damaged_indices]
+
+        # One damage factor per building
+        damage_factors = self._rng.uniform(
+            self.min_damage_factor, self.max_damage_factor, size=n_to_damage
+        )
+
+        for bid, factor in zip(damaged_ids, damage_factors):
+            self._damage_building(cands[bid], factor)
+            self.damage_log[bid] = round(float(factor), 4)
+
+        # Undamaged buildings recorded as 1.0
+        for bid in cand_ids:
+            if bid not in self.damage_log:
+                self.damage_log[bid] = 1.0
+
+        print(f"[DisasterSimulator] Damage simulation: "
+              f"{n_to_damage}/{len(cand_ids)} buildings damaged "
+              f"(factor range [{self.min_damage_factor}, {self.max_damage_factor}])")
+        return object_dict
+
+    def _damage_building(self, building: dict, damage_factor: float) -> None:
+        """Reduce height of a single building in-place."""
+        verts = building['vertices']  # (N, 3)
+        z_min = float(verts[:, 2].min())
+
+        # Update vertices array
+        mask = verts[:, 2] > (z_min + self._Z_EPSILON)
+        verts[mask, 2] = z_min + (verts[mask, 2] - z_min) * damage_factor
+        building['vertices'] = verts
+
+        # Update polygon_mesh
+        new_mesh = []
+        for surface in building['polygon_mesh']:
+            new_surface = []
+            for coord in surface:
+                x, y, z = coord[0], coord[1], coord[2]
+                if z > z_min + self._Z_EPSILON:
+                    z = z_min + (z - z_min) * damage_factor
+                new_surface.append([x, y, z])
+            new_mesh.append(new_surface)
+        building['polygon_mesh'] = new_mesh
+
+        # Update centroid z
+        new_z_max = float(verts[:, 2].max())
+        c = np.asarray(building['centroid'], dtype=np.float64)
+        c[2] = (z_min + new_z_max) / 2.0
+        building['centroid'] = c
+
+    # ------------------------------------------------------------------ #
+    # Reporting
+    # ------------------------------------------------------------------ #
+
+    @staticmethod
+    def _print_summary(object_dict: dict) -> None:
+        print(f"[DisasterSimulator] Done. "
+              f"cands: {len(object_dict['cands'])}, "
+              f"index: {len(object_dict['index'])} (unchanged)")

code/3dSAGER/main.py

@@ -0,0 +1,41 @@
+import config
+import argparse
+from utils import *
+from pipelines import PipelineManager
+import warnings
+
+warnings.filterwarnings("ignore")
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset_name', type=str, default=config.Constants.dataset_name)
+    parser.add_argument('--evaluation_mode', type=str, default=config.Constants.evaluation_mode)
+    parser.add_argument('--run_preparatory_phase', type=bool, default=config.TrainingPhase.run_preparatory_phase)
+    parser.add_argument('--blocking_method', type=str, default=config.Blocking.blocking_method)
+    parser.add_argument('--seeds_num', type=int, default=config.Constants.seeds_num)
+    parser.add_argument('--dataset_size_version', type=str, default=config.Constants.dataset_size_version)
+    parser.add_argument('--vector_normalization', type=str2bool, default=True)
+    parser.add_argument('--sdr_factor', type=str2bool, default=False)
+    parser.add_argument('--neg_samples_num', type=int, default=config.Constants.neg_samples_num)
+    parser.add_argument('--bkafi_criterion', type=str, default=config.Blocking.bkafi_criterion)
+    parser.add_argument('--run_blocker_train', type=str2bool, default=False)
+    parser.add_argument('--matching_cands_generation', type=str,
+                        default=config.Constants.matching_cands_generation)
+    parser.add_argument('--contamination_mode', type=str2bool, default=False)
+
+    args = parser.parse_args()
+    logger = define_logger()
+    print_config(logger, args)
+    result_dict = {}
+    for seed in range(1, args.seeds_num+1):
+        logger.info(f"Seed: {seed}")
+        logger.info(3*'--------------------------')
+        pipeline_manager_obj = PipelineManager(seed, logger, args)
+        result_dict[seed] = pipeline_manager_obj.result_dict
+    if not args.run_blocker_train:
+        generate_final_result_csv(result_dict, args)
+    logger.info("Done!")
+
+

code/3dSAGER/pipelines.py

@@ -0,0 +1,910 @@
+import copy
+import json
+import os
+import config
+from utils import *
+from blocking import Blocker
+from collections import defaultdict
+from process_pairs import PairProcessor
+from object_properties import ObjectPropertiesProcessor
+import numpy as np
+from sklearn.model_selection import train_test_split
+from classifier import FlexibleClassifier
+from abc import ABC, abstractmethod
+from multiprocessing import Pool, cpu_count
+from disaster_simulation import DisasterSimulator
+from alignment import RigidAligner
+
+
+class PipelineManager:
+    def __init__(self, seed, logger, args, min_surfaces_num=10):
+        self.dataset_name = args.dataset_name
+        self.seed = seed
+        self.logger = logger
+        self.with_prep_training = args.run_preparatory_phase
+        self.min_surfaces_num = min_surfaces_num
+        self.evaluation_mode = args.evaluation_mode
+        self.blocking_method = args.blocking_method
+        self.dataset_size_version = args.dataset_size_version
+        self.neg_samples_num = args.neg_samples_num
+        self.vector_normalization = args.vector_normalization
+        self.sdr_factor = args.sdr_factor
+        self.bkafi_criterion = args.bkafi_criterion
+        self.matching_cands_generation = args.matching_cands_generation
+        self.run_blocker_train = args.run_blocker_train
+        self._simulator = None   # set by _read_objects when disaster simulation runs
+        self.dataset_dict = self._create_dataset_dict()
+        self.flexible_classifier_obj = self._train_and_evaluate()
+        self.result_dict = self._get_result_dict()
+
+    def _read_objects(self):
+        dataset_config = json.load(open('dataset_configs.json'))[self.dataset_name]
+        train_object_dict, test_object_dict = self._load_object_dict_wrapper()
+        partition_path = (f"{config.FilePaths.dataset_partition_path}"
+                          f"{self.dataset_name}_seed{self.seed}.pkl")
+        if not os.path.exists(partition_path):
+            self.logger.info(f"Partition file missing — generating for seed {self.seed}...")
+            import argparse
+            from data_partition import DataPartitionGenerator
+            partition_args = argparse.Namespace(
+                dataset_name=self.dataset_name,
+                train_neg_samples_list=[2, 5],
+                train_size_ratio_list={"small": 0.1, "medium": 0.4, "large": 0.6},
+                test_size_ratio_list={"small": 0.1, "medium": 0.5, "large": 1.0},
+                test_negative_samples_list=[2, 5],
+            )
+            gen = DataPartitionGenerator(partition_args)
+            gen.create_dataset_partition_dict(self.seed)
+        data_partition_dict = load_dataset_partition_dict(self.dataset_name, self.logger, self.seed)
+        if test_object_dict is not None and train_object_dict is not None:
+            return data_partition_dict, train_object_dict, test_object_dict
+        self.logger.info("Generating test object dict and train object dict")
+        # Raw-object cache: produced by preprocess_hague.py (run once).
+        # If missing, fall back to reading CityJSON files directly.
+        # Each seed deepcopies before simulation so the cached version is never mutated.
+        raw_cache_path = (f"{config.FilePaths.object_dict_path}"
+                          f"{self.dataset_name}_raw.joblib")
+        if os.path.exists(raw_cache_path):
+            self.logger.info(f"Loading preprocessed cache: {raw_cache_path}")
+            raw_object_dict = joblib.load(raw_cache_path)
+            self.logger.info(
+                f"  → {len(raw_object_dict['cands'])} cands, "
+                f"{len(raw_object_dict['index'])} index buildings"
+            )
+        else:
+            self.logger.info("No preprocessed cache found — running full read (slow).")
+            raw_object_dict = getattr(self, f'_read_objects_{self.dataset_name}')(dataset_config)
+            os.makedirs(os.path.dirname(raw_cache_path), exist_ok=True)
+            joblib.dump(raw_object_dict, raw_cache_path, compress=3)
+            self.logger.info(f"Raw object_dict cached to: {raw_cache_path}")
+        # Optional: restrict to the first N shared grid cells for quick testing
+        max_cells = config.Constants.max_grid_cells
+        if max_cells is not None and 'grid_meta' in raw_object_dict:
+            raw_object_dict = self._filter_to_grid_cells(raw_object_dict, max_cells)
+        # Work on a fresh copy so the cached version is never modified by simulation
+        object_dict = copy.deepcopy(raw_object_dict)
+        # Apply disaster simulation to cands before property extraction.
+        # load_object_dict must be False when disaster mode is active (cached dicts
+        # are pre-simulation and would produce incorrect features).
+        self._simulator = DisasterSimulator(config.DisasterSimulation, seed=self.seed)
+        object_dict = self._simulator.apply(object_dict)
+        train_object_dict, test_object_dict = self._partition_object_dict(object_dict, data_partition_dict)
+        if config.Constants.save_object_dict:
+            self._save_object_dicts(train_object_dict, test_object_dict, dataset_config)
+        return data_partition_dict, train_object_dict, test_object_dict
+
+    @staticmethod
+    def _filter_to_grid_cells(raw_object_dict, max_cells):
+        """
+        Restrict both cands and index to buildings in the `max_cells` most-populated
+        grid cells that are shared between both sources.
+        Picking by population (not coordinate order) maximises pair coverage from the
+        pre-built partition dict and ensures a spatially representative quick test.
+        """
+        from collections import Counter
+        cands_cells = set(b['grid_cell'] for b in raw_object_dict['cands'].values())
+        index_cells = set(b['grid_cell'] for b in raw_object_dict['index'].values())
+        shared = cands_cells & index_cells
+        # Sort shared cells by number of cands in descending order
+        cell_pop = Counter(b['grid_cell'] for b in raw_object_dict['cands'].values())
+        shared_cells = set(sorted(shared, key=lambda c: -cell_pop[c])[:max_cells])
+
+        filtered = dict(raw_object_dict)  # shallow copy of top-level keys
+        filtered['cands'] = {k: v for k, v in raw_object_dict['cands'].items()
+                             if v['grid_cell'] in shared_cells}
+        filtered['index'] = {k: v for k, v in raw_object_dict['index'].items()
+                             if v['grid_cell'] in shared_cells}
+
+        # Rebuild mapping dicts for filtered subset
+        filtered['mapping_dict']     = {}
+        filtered['inv_mapping_dict'] = {}
+        for src in ('cands', 'index'):
+            keys = sorted(filtered[src].keys())
+            filtered['mapping_dict'][src]     = {i: k for i, k in enumerate(keys)}
+            filtered['inv_mapping_dict'][src] = {k: i for i, k in enumerate(keys)}
+
+        import logging
+        logging.getLogger(__name__).info(
+            f"[grid filter] {max_cells} shared cells → "
+            f"{len(filtered['cands'])} cands, {len(filtered['index'])} index buildings"
+        )
+        return filtered
+
+    def _load_object_dict_wrapper(self):
+        test_object_dict, train_object_dict = None, None
+        train_full_path, test_full_path = self._get_object_dict_paths()
+        if config.Constants.load_object_dict:
+            train_object_dict = load_object_dict(self.logger, train_full_path, 'train_object_dict')
+            test_object_dict = load_object_dict(self.logger, test_full_path, 'test_object_dict')
+        return train_object_dict, test_object_dict
+
+    def _save_object_dicts(self, train_object_dict, test_object_dict, dataset_config):
+        self._print_object_dict_stats(train_object_dict, test_object_dict)
+        self.logger.info(f"Saving test object dict")
+        train_full_path, test_full_path = self._get_object_dict_paths()
+        self.logger.info(f"Saving train object dict to {train_full_path}")
+        joblib.dump(train_object_dict, train_full_path)
+        self.logger.info(f"Saving test object dict to {test_full_path}")
+        joblib.dump(test_object_dict, test_full_path)
+        return
+
+    @staticmethod
+    def _print_object_dict_stats(train_object_dict, test_object_dict):
+        print(f"Number of cands in train: {len(train_object_dict['cands'])}")
+        print(f"Number of index in train: {len(train_object_dict['index'])}")
+        print(f"Number of cands in test: {len(test_object_dict['cands'])}")
+        print(f"Number of index in test: {len(test_object_dict['index'])}")
+
+    def _get_object_dict_paths(self):
+        object_dict_path = f"{config.FilePaths.object_dict_path}{self.dataset_name}/"
+        if not os.path.exists(object_dict_path):
+            os.makedirs(object_dict_path)
+        if self.evaluation_mode == 'blocking':
+            train_full_path = f"{object_dict_path}train_blocking_{self.dataset_size_version}"
+            test_full_path = f"{object_dict_path}test_blocking_{self.dataset_size_version}"
+        else:
+            train_full_path = (f"{object_dict_path}train_matching_{self.dataset_size_version}_"
+                               f"neg_samples_num={self.neg_samples_num}")
+            test_full_path = f"{object_dict_path}test_matching_{self.matching_cands_generation}" \
+                             f"_{self.dataset_size_version}_neg_samples_num={self.neg_samples_num}"
+        return f"{train_full_path}_seed_{self.seed}.joblib", f"{test_full_path}_seed_{self.seed}.joblib"
+
+    def _partition_object_dict(self, object_dict, data_partition_dict):
+        if self.evaluation_mode == "blocking":
+            return self._clean_object_dict_blocking(object_dict, data_partition_dict)
+        else:
+            return self._clean_object_dict_matching(object_dict, data_partition_dict)
+
+    def _clean_object_dict_blocking(self, object_dict, data_partition_dict):
+        dataset_version = self.dataset_size_version
+        train_object_dict = {'cands': {}, 'index': {}}
+        test_object_dict = {'cands': {}, 'index': {}}
+        avail_cands = set(object_dict['cands'].keys())
+        avail_index = set(object_dict['index'].keys())
+        train_pairs = data_partition_dict['train']['negative_sampling'][dataset_version][2]
+        train_pairs = [p for p in train_pairs if p[0] in avail_cands and p[1] in avail_index]
+        test_data_partition = data_partition_dict['test']['blocking'][dataset_version]
+        test_cands_ids = [i for i in test_data_partition['cands'] if i in avail_cands]
+        test_index_ids = [i for i in test_data_partition['index'] if i in avail_index]
+        train_object_dict['cands'] = {pair[0]: object_dict['cands'][pair[0]] for pair in train_pairs}
+        train_object_dict['index'] = {pair[1]: object_dict['index'][pair[1]] for pair in train_pairs}
+        test_object_dict['cands'] = {object_id: object_dict['cands'][object_id] for object_id in test_cands_ids}
+        test_object_dict['index'] = {object_id: object_dict['index'][object_id] for object_id in test_index_ids}
+        return train_object_dict, test_object_dict
+
+    def _clean_object_dict_matching(self, object_dict, data_partition_dict):
+        train_object_dict = {'cands': {}, 'index': {}}
+        test_object_dict = {'cands': {}, 'index': {}}
+        dataset_version = self.dataset_size_version
+        neg_num = self.neg_samples_num
+        candidates_generation = self.matching_cands_generation
+        train_pairs = data_partition_dict['train'][self.matching_cands_generation][dataset_version][neg_num]
+        test_pairs = data_partition_dict['test']['matching'][candidates_generation][dataset_version][neg_num]
+        # Filter pairs to IDs that exist in the loaded object_dict (important for
+        # quick-test runs where max_files_per_source limits coverage)
+        avail_cands = set(object_dict['cands'].keys())
+        avail_index = set(object_dict['index'].keys())
+        train_pairs = [p for p in train_pairs if p[0] in avail_cands and p[1] in avail_index]
+        test_pairs  = [p for p in test_pairs  if p[0] in avail_cands and p[1] in avail_index]
+        self.logger.info(f"Filtered to {len(train_pairs)} train pairs, {len(test_pairs)} test pairs "
+                         f"(available cands={len(avail_cands)}, index={len(avail_index)})")
+        train_object_dict['cands'] = {pair[0]: object_dict['cands'][pair[0]] for pair in train_pairs}
+        train_object_dict['index'] = {pair[1]: object_dict['index'][pair[1]] for pair in train_pairs}
+        test_object_dict['cands'] = {pair[0]: object_dict['cands'][pair[0]] for pair in test_pairs}
+        test_object_dict['index'] = {pair[1]: object_dict['index'][pair[1]] for pair in test_pairs}
+        return train_object_dict, test_object_dict
+
+    @staticmethod
+    def _remove_train_objects_from_object_dict(object_dict, train_ids):
+        for objects_type in object_dict.keys():
+            object_dict[objects_type] = {
+                object_id: object_data
+                for object_id, object_data in object_dict[objects_type].items()
+                if object_id not in train_ids
+            }
+        return object_dict
+
+    @staticmethod
+    def _compute_object_centroid(vertices):
+        unique_vertices = np.array(vertices)
+        return unique_vertices.mean(axis=0)
+
+    @staticmethod
+    def _get_vertices(polygon_mesh):
+        return np.unique(np.array([coord for surface in polygon_mesh for coord in surface]), axis=0)
+
+    @staticmethod
+    def _get_polygon_mesh(data, obj_key, vertices, min_surfaces_num):
+        boundaries = data['CityObjects'][obj_key]['geometry'][0]['boundaries'][0]
+        if len(boundaries) < min_surfaces_num:
+            return None
+        polygon_mesh = []
+        for surface in boundaries:
+            polygon_mesh.append([vertices[i] for sub_surface_list in surface for i in sub_surface_list])
+        vertices = PipelineManager._get_vertices(polygon_mesh)
+        centroid = PipelineManager._compute_object_centroid(vertices)
+        return {'polygon_mesh': polygon_mesh, 'vertices': vertices, 'centroid': centroid}
+
+    def _insert_polygon_mesh(self, object_dict, obj_type, obj_data, obj_ind, min_surfaces_num=10):
+        vertices = obj_data['vertices']
+        obj_key = list(obj_data['CityObjects'].keys())[0]
+        polygon_mesh = self._get_polygon_mesh(obj_data, obj_key, vertices, min_surfaces_num)
+        if polygon_mesh is not None:
+            object_dict[obj_type][obj_ind] = polygon_mesh
+        return object_dict
+
+    def _read_objects_bo_em(self, dataset_config):
+        objects_path_dict = read_object_path_dict(dataset_config)
+        object_dict = defaultdict(dict)
+        for objects_type, objects_path in objects_path_dict.items():
+            file_list = [f for f in os.listdir(objects_path) if f.endswith('.json')]
+            for filename in file_list:
+                file_ind = int(filename.split('.')[0])
+                json_data = read_json(objects_path, file_ind)
+                object_dict = self._insert_polygon_mesh(object_dict, objects_type, json_data, file_ind)
+            object_dict[objects_type] = dict(sorted(object_dict[objects_type].items()))
+        return object_dict
+
+    def _read_objects_gpkg(self, dataset_config):
+        objects_path_dict = read_object_path_dict(dataset_config)
+        object_dict = defaultdict(dict)
+        for objects_type, objects_path in objects_path_dict.items():
+            file_list = [f for f in os.listdir(objects_path) if f.endswith('.json')]
+            for filename in file_list:
+                file_ind = int(filename.split('.')[0])
+                json_data = read_json(objects_path, file_ind)
+                json_data = json.loads(json_data)
+                object_dict = self._insert_polygon_mesh(object_dict, objects_type, json_data, file_ind)
+            object_dict[objects_type] = dict(sorted(object_dict[objects_type].items()))
+        return object_dict
+
+    def _read_objects_delivery3(self, dataset_config):
+        objects_path_dict = read_object_path_dict(dataset_config)
+        object_dict = defaultdict(dict)
+        mapping_dict = defaultdict(dict)
+        inv_mapping_dict = defaultdict(dict)
+        for objects_type, objects_path in objects_path_dict.items():
+            file_list = [f for f in os.listdir(objects_path) if f.endswith('.json')]
+            for file_ind, file_name in enumerate(file_list):
+                file_name = file_name.split('.')[0]
+                json_data = read_json(objects_path, file_name)
+                object_dict = self._insert_polygon_mesh(object_dict, objects_type, json_data, file_ind)
+                mapping_dict[objects_type][file_ind] = file_name
+                inv_mapping_dict[objects_type][file_name] = file_ind
+            object_dict[objects_type] = dict(sorted(object_dict[objects_type].items()))
+        object_dict['mapping_dict'] = mapping_dict
+        object_dict['inv_mapping_dict'] = inv_mapping_dict
+        return object_dict
+
+    def _read_objects_Hague(self, dataset_config):
+        """
+        Fallback reader used only when preprocess_hague.py has not been run.
+        Prefer running `python preprocess_hague.py` once to create the cache.
+        """
+        from preprocess_hague import preprocess
+        raw_cache_path = f"{config.FilePaths.object_dict_path}{self.dataset_name}_raw.joblib"
+        self.logger.info("Running preprocess_hague.preprocess() to build cache...")
+        return preprocess(
+            cands_path=dataset_config['cands_path'],
+            index_path=dataset_config['index_path'],
+            output_path=raw_cache_path,
+            cell_size=config.DataPartition.grid_cell_size,
+            min_surfaces=self.min_surfaces_num,
+        )
+
+    def _generate_object_dict_mappings(self, object_dict, objects_path_dict):
+        object_dict['mapping_dict'], object_dict['inv_mapping_dict'] = {}, {}
+        for object_type in objects_path_dict:
+            keys = list(object_dict[object_type].keys())
+            object_dict['mapping_dict'][object_type] = {i: k for i, k in enumerate(keys)}
+            object_dict['inv_mapping_dict'][object_type] = {k: i for i, k in enumerate(keys)}
+        return object_dict
+
+    @staticmethod
+    def _process_object_file(file_ind, file_path, object_type, min_surfaces_num):
+        print(f"Processing file {file_ind}")
+        with open(file_path, 'r') as f:
+            data = json.load(f)
+        vertices = data['vertices']
+        partial_dict = {}
+        for obj_key in data['CityObjects'].keys():
+            try:
+                new_obj_key = PipelineManager.standardize_obj_key(obj_key, object_type)
+                polygon_mesh_data = PipelineManager._get_polygon_mesh(data, obj_key, vertices,
+                                                                      min_surfaces_num=min_surfaces_num)
+                if polygon_mesh_data is not None:
+                    partial_dict[new_obj_key] = polygon_mesh_data
+            except:
+                continue
+        return partial_dict
+
+    @staticmethod
+    def standardize_obj_key(obj_key, object_type):
+        if object_type == 'cands':
+            return obj_key.split('bag_')[1]
+        elif object_type == 'index':
+            return obj_key.split('NL.IMBAG.Pand.')[1].split('-0')[0]
+        else:
+            raise ValueError('Invalid source')
+
+    @staticmethod
+    def read_objects_synthetic(self, dataset_config):
+        pass
+
+    # def _generate_training_pairs(self):
+    #     np.random.seed(self.seed)
+    #     index_ids = list(self.train_object_dict['index'].keys())
+    #     pos_pairs = [(obj_id, obj_id) for obj_id in self.train_object_dict['cands'].keys()]
+    #     neg_pairs = [(obj_id, np.random.choice(index_ids)) for obj_id in self.train_object_dict['cands'].keys()]
+    #     neg_pairs = [(cand_id, index_id) for cand_id, index_id in neg_pairs if cand_id != index_id]
+    #     return pos_pairs, neg_pairs
+
+    # def _run_blocker(self):
+    #     self.logger.info(f"Running blocking for training phase")
+    #     dummy_feature_importance_scores = self._get_dummy_feature_importance_scores()
+    #     dummy_property_ratios = self._get_dummy_property_ratios()
+    #     blocker = Blocker(self.dataset_name, self.train_object_dict, self.train_property_dict,
+    #                       dummy_feature_importance_scores, dummy_property_ratios, 'bkafi', 'train')
+    #     self.logger.info(f"The blocking process for the training phase ended successfully")
+    #     self.train_pos_pairs_dict, self.train_neg_pairs_dict = blocker.pos_pairs_dict, blocker.neg_pairs_dict
+    #     save_blocking_output(self.train_pos_pairs_dict, self.train_neg_pairs_dict, self.seed, self.logger, 'train')
+    #     return
+
+    @staticmethod
+    def _get_dummy_feature_importance_scores():
+        feature_names = get_feature_name_list(config.Features.operator)
+        dummy_model_name = config.Models.blocking_model
+        return {dummy_model_name: [(feature, 1) for feature in feature_names]}
+
+    def _get_dummy_property_ratios(self):
+        property_ratios = {prop: {'mean': 1.0, 'std': 0.0}
+                           for prop in self.train_property_dict.keys()}
+        return property_ratios
+
+    def _run_blocker(self, feature_importance_dict, train_property_ratios):
+        blocking_method = self.blocking_method
+        self.logger.info(f"Running blocking method {blocking_method}")
+        blocker = Blocker(self.dataset_name, self.test_object_dict, self.test_property_dict, feature_importance_dict,
+                          train_property_ratios,  self.blocking_method, self.sdr_factor, self.bkafi_criterion, 'test')
+        self.logger.info(f"The blocking process ended successfully")
+        self._save_blocking_output(blocker.pos_pairs_dict, blocker.neg_pairs_dict, blocker.blocking_execution_time)
+        self.blocking_result_dict = self._evaluate_blocking(blocker.pos_pairs_dict, blocker.blocking_execution_time)
+        return
+
+    def _run_blocker_train(self, feature_importance_dict, train_property_ratios):
+        blocking_method = self.blocking_method
+        self.logger.info(f"Running blocking method {blocking_method} for train set")
+        blocker = Blocker(self.dataset_name, self.train_object_dict, self.train_property_dict, feature_importance_dict,
+                          train_property_ratios,  self.blocking_method, self.sdr_factor, self.bkafi_criterion, 'test')
+        self.logger.info(f"The blocking process for train set ended successfully")
+        pos_pairs_dict, neg_pairs_dict, execution_time = (blocker.pos_pairs_dict, blocker.neg_pairs_dict,
+                                                          blocker.blocking_execution_time)
+        self._save_blocking_output(pos_pairs_dict, neg_pairs_dict, execution_time, train_set_mode=True)
+        return
+
+    def _save_blocking_output(self, pos_pairs, neg_pairs, blocking_execution_time, train_set_mode=False):
+        blocking_dict = {'pos_pairs': pos_pairs, 'neg_pairs': neg_pairs,
+                         'blocking_execution_time': blocking_execution_time}
+        blocking_output_path = self._get_blocking_output_path()
+        if train_set_mode:
+            blocking_output_path = blocking_output_path.replace('Operator', 'Train_Operator')
+        try:
+            joblib.dump(blocking_dict, blocking_output_path)
+            message = f"Blocking results were saved successfully to {blocking_output_path}"
+            self.logger.info(message)
+        except Exception as e:
+            self.logger.error(f"Error happened while saving blocking results: {e}")
+        return
+
+    def _get_blocking_output_path(self):
+        file_name = get_file_name()
+        blocking_results_path = config.FilePaths.results_path + 'blocking_output/'
+        vector_normalization = 'True' if self.vector_normalization else 'False'
+        sdr_factor = 'True' if self.sdr_factor else 'False'
+        if not os.path.exists(blocking_results_path):
+            os.makedirs(blocking_results_path)
+        blocking_results_path = (f"{blocking_results_path}{file_name}_"
+                                 f"{self.dataset_size_version}_neg_samples_num{self.neg_samples_num}"
+                                 f"_vector_normalization_{vector_normalization}_sdr_factor_{sdr_factor}_"
+                                 f"bkafi_criterion={self.bkafi_criterion}_seed={self.seed}.joblib")
+        return blocking_results_path
+
+
+    # def _get_property_dict_path(self, train_or_test):
+    #     file_name = get_file_name_property_dict()
+    #     property_dict_path = config.FilePaths.property_dict_path
+    #     vector_normalization = self.vector_normalization if self.vector_normalization is not None else 'None'
+    #     if not os.path.exists(property_dict_path):
+    #         os.makedirs(property_dict_path)
+    #     property_dict_path = (f"{property_dict_path}{file_name}_{train_or_test}_{self.evaluation_mode}_"
+    #                           f"{self.dataset_size_version}_neg_samples_num={self.neg_samples_num}_"
+    #                           f"vector_normalization={vector_normalization}_seed={self.seed}.joblib")
+    #     return property_dict_path
+
+    # def _save_blocking_evaluation(self, blocking_evaluation_dict, blocking_method_arg=None):
+    #     file_name = get_file_name(blocking_method_arg)
+    #     blocking_results_path = config.FilePaths.results_path
+    #     vector_normalization = config.Features.normalization
+    #     vector_normalization_str = vector_normalization if vector_normalization is not None else "None"
+    #     sdr_factor = config.Blocking.sdr_factor
+    #     sdr_factor_str = "True" if sdr_factor else "False"
+    #     bkafi_criterion = config.Blocking.bkafi_criterion
+    #     if not os.path.exists(blocking_results_path):
+    #         os.makedirs(blocking_results_path)
+    #     try:
+    #         blocking_results_path = (f"{blocking_results_path}blocking_evaluation_results_{file_name}_"
+    #                                  f"{self.dataset_size_version}_neg_samples_num{self.neg_samples_num}_"
+    #                                  f"vector_normalization={vector_normalization_str}_sdr_factor_{sdr_factor_str}_"
+    #                                  f"bkafi_criterion={bkafi_criterion}_seed={self.seed}.joblib")
+    #         joblib.dump(blocking_evaluation_dict, blocking_results_path)
+    #         self.logger.info(f"Blocking evaluation results were saved successfully")
+    #     except Exception as e:
+    #         self.logger.error(f"Error happened while saving blocking evaluation results: {e}")
+
+    def _evaluate_blocking(self, pos_pairs_dict, blocking_execution_time):
+        index_ids = set(self.test_object_dict['index'].keys())
+        cand_ids = set(self.test_object_dict['cands'].keys())
+        max_intersection = index_ids.intersection(cand_ids)
+        if 'bkafi' in self.blocking_method:
+            blocking_res_dict = self._evaluate_bkafi_blocking(max_intersection, pos_pairs_dict, blocking_execution_time)
+        else:
+            blocking_res_dict = self._evaluate_not_bkafi_blocking(max_intersection, pos_pairs_dict,
+                                                                  blocking_execution_time)
+        # self._save_blocking_evaluation(blocking_res_dict)
+        return blocking_res_dict
+
+    def _evaluate_bkafi_blocking(self, max_intersection, pos_pairs_dict, blocking_execution_time):
+        blocking_res_dict = defaultdict(dict)
+        for bkafi_dim in pos_pairs_dict.keys():
+            for cand_pairs_per_item in pos_pairs_dict[bkafi_dim].keys():
+                pos_pairs = set(pos_pairs_dict[bkafi_dim][cand_pairs_per_item])
+                blocking_recall = round(len(pos_pairs) / len(max_intersection), 3)
+                blocking_res_dict[bkafi_dim][cand_pairs_per_item] = {'blocking_recall': blocking_recall,
+                                                                     'blocking_execution_time':
+                                                                         blocking_execution_time[bkafi_dim]}
+                if cand_pairs_per_item == 10:
+                    self.logger.info(f"Blocking recall for {self.blocking_method}_dim {bkafi_dim} and "
+                                     f"cand_pairs_per_item {cand_pairs_per_item}: {blocking_recall}")
+                    self.logger.info(3*'- - - - - - - - - - - - -')
+        return blocking_res_dict
+
+    def _evaluate_not_bkafi_blocking(self, max_intersection, pos_pairs_dict, blocking_execution_time):
+        blocking_res_dict = defaultdict(dict)
+        for cand_pairs_per_item in pos_pairs_dict.keys():
+            pos_pairs = set(pos_pairs_dict[cand_pairs_per_item])
+            blocking_recall = round(len(pos_pairs) / len(max_intersection), 3)
+            blocking_res_dict[cand_pairs_per_item] = {'blocking_recall': blocking_recall,
+                                                      'blocking_execution_time': blocking_execution_time}
+            # self.logger.info(f"Blocking recall for {self.blocking_method}, cand_pairs_per_item "
+            #                  f"{cand_pairs_per_item}: {blocking_recall}")
+            # self.logger.info(3*'--------------------------')
+        return blocking_res_dict
+
+    def _create_dataset_dict(self):
+        dataset_dict = self._load_dataset_dict_wrapper()
+        if dataset_dict is not None:
+            return dataset_dict
+        data_partition_dict, self.train_object_dict, self.test_object_dict = self._read_objects()
+        self.train_pos_pairs, self.train_neg_pairs = self._extract_pairs(data_partition_dict, 'train')
+        self.train_property_dict = self._generate_property_dict('train')
+        if self.evaluation_mode == "matching":
+            self.test_pos_pairs, self.test_neg_pairs = self._extract_pairs(data_partition_dict, 'test')
+        self.test_property_dict = self._generate_property_dict('test')
+        feature_dict = self._generate_feature_dict()
+        dataset_dict = self._create_final_dict(feature_dict)
+        return dataset_dict
+
+    def _extract_pairs(self, data_partition_dict, train_or_test):
+        if self.evaluation_mode == "blocking":
+            pair_list = data_partition_dict[train_or_test]['negative_sampling'][self.dataset_size_version] \
+                [self.neg_samples_num]
+        else:
+            if train_or_test == 'train':
+                pair_list = data_partition_dict[train_or_test][self.matching_cands_generation] \
+                    [self.dataset_size_version][self.neg_samples_num]
+            else:
+                pair_list = data_partition_dict[train_or_test]['matching'][self.matching_cands_generation] \
+                    [self.dataset_size_version][self.neg_samples_num]
+        # Same filter as _clean_object_dict_matching: drop pairs whose IDs aren't in the
+        # loaded object dict, so PairProcessor never sees IDs missing from property_dict.
+        object_dict = self.train_object_dict if train_or_test == 'train' else self.test_object_dict
+        avail_cands = set(object_dict['cands'].keys())
+        avail_index = set(object_dict['index'].keys())
+        before = len(pair_list)
+        pair_list = [p for p in pair_list if p[0] in avail_cands and p[1] in avail_index]
+        dropped = before - len(pair_list)
+        if dropped:
+            self.logger.info(f"_extract_pairs[{train_or_test}]: dropped {dropped}/{before} pairs "
+                             f"with IDs not in object_dict (kept {len(pair_list)})")
+        pos_pairs = [pair for pair in pair_list if pair[0] == pair[1]]
+        neg_pairs = [pair for pair in pair_list if pair[0] != pair[1]]
+        return pos_pairs, neg_pairs
+
+    def _load_dataset_dict_wrapper(self):
+        dataset_dict = None
+        if config.Constants.load_dataset_dict:
+            dataset_dict = self._load_dataset_dict()
+            if dataset_dict is not None:
+                return dataset_dict
+        return dataset_dict
+
+    # def _get_pos_and_neg_pairs_for_training(self):
+    #     bkafi_dim = min(self.train_pos_pairs_dict.keys())
+    #     cand_pairs_per_item = min(self.test_pos_pairs_dict[bkafi_dim].keys())
+    #     pos_pairs = self.train_pos_pairs_dict[bkafi_dim][cand_pairs_per_item]
+    #     neg_pairs = self.train_neg_pairs_dict[bkafi_dim][cand_pairs_per_item]
+    #     return pos_pairs, neg_pairs
+
+    # def _get_pos_and_neg_pairs(self, train_or_test):
+    #     pos_pairs_dict = self.test_pos_pairs_dict if train_or_test == 'test' else self.train_pos_pairs_dict
+    #     neg_pairs_dict = self.test_neg_pairs_dict if train_or_test == 'test' else self.train_neg_pairs_dict
+    #     bkafi_dim = min(pos_pairs_dict.keys())
+    #     cand_pairs_per_item = min(pos_pairs_dict[bkafi_dim].keys())
+    #     pos_pairs = pos_pairs_dict[bkafi_dim][cand_pairs_per_item]
+    #     neg_pairs = neg_pairs_dict[bkafi_dim][cand_pairs_per_item]
+    #     return pos_pairs, neg_pairs
+
+    def _load_train_items(self):
+        feature_importance_dict, matching_pairs_property_ratios = None, None
+        try:
+            feature_importance_dict = load_feature_importance_dict(self.seed, self.logger)
+            matching_pairs_property_ratios = load_property_ratios(self.seed, self.logger)
+        except:
+            self.logger.info("Could not load training phase items. Running training phase pipeline")
+        return feature_importance_dict, matching_pairs_property_ratios
+
+    def _generate_property_dict(self, train_or_test):
+        rel_object_dict = self.train_object_dict if train_or_test == 'train' else self.test_object_dict
+        if config.Constants.load_property_dict:
+            property_dict = self._load_property_dict(train_or_test)
+            if property_dict is not None:
+                return property_dict
+        self.logger.info(f"Generating {train_or_test} property dictionary")
+        obj_property_processor = ObjectPropertiesProcessor(rel_object_dict, self.vector_normalization)
+        property_dict = obj_property_processor.prop_vals_dict
+        property_dict_generation_time = obj_property_processor.property_dict_generation_time
+        self.logger.info(f"Property dictionary generation time: {property_dict_generation_time}\n")
+        if config.Constants.save_property_dict:
+            self._save_property_dict(property_dict, train_or_test)
+        return property_dict
+
+    def _save_property_dict(self, property_dict, train_or_test):
+        try:
+            property_dict_path = self._get_property_dict_path(train_or_test)
+            joblib.dump(property_dict, property_dict_path)
+            self.logger.info(f"{train_or_test}_property_dict was saved successfully")
+            self.logger.info('')
+        except Exception as e:
+            self.logger.error(f"Error happened while saving {train_or_test}_property_dict: {e}")
+        return
+
+    def _load_property_dict(self, train_or_test):
+        property_dict_path = self._get_property_dict_path(train_or_test)
+        try:
+            property_dict = joblib.load(property_dict_path)
+            self.logger.info(f"{train_or_test}_property_dict was loaded successfully")
+            return property_dict
+        except Exception as e:
+            self.logger.error(f"Error happened while loading {train_or_test}_property_dict: {e}")
+            return None
+
+    def _get_property_dict_path(self, train_or_test):
+        file_name = get_file_name_property_dict()
+        property_dict_path = config.FilePaths.property_dict_path
+        vector_normalization = 'True' if self.vector_normalization else 'False'
+        if not os.path.exists(property_dict_path):
+            os.makedirs(property_dict_path)
+        property_dict_path = (f"{property_dict_path}{file_name}_{train_or_test}_{self.evaluation_mode}_"
+                              f"{self.dataset_size_version}_neg_samples_num={self.neg_samples_num}_"
+                              f"vector_normalization={vector_normalization}_seed={self.seed}.joblib")
+        return property_dict_path
+
+    def _generate_feature_dict(self):
+        feature_dict = {'train': {}, 'test': {}} if self.evaluation_mode == 'matching' else {'train': {}}
+        for train_or_test in feature_dict.keys():
+            pos_pairs, neg_pairs, property_dict = self._get_rel_pairs_and_property_dict(train_or_test)
+            self.logger.info(f"Generating {train_or_test} feature vectors")
+            for label, pairs_list in zip([0, 1], [neg_pairs, pos_pairs]):
+                feature_dict[train_or_test][label] = PairProcessor(property_dict, pairs_list).feature_vec
+        return feature_dict
+
+    def _get_rel_pairs_and_property_dict(self, train_or_test):
+        if train_or_test == 'train':
+            pos_pairs, neg_pairs = self.train_pos_pairs, self.train_neg_pairs
+            property_dict = self.train_property_dict
+        else:
+            pos_pairs, neg_pairs = self.test_pos_pairs, self.test_neg_pairs
+            property_dict = self.test_property_dict
+        return pos_pairs, neg_pairs, property_dict
+
+    def _create_final_dict(self, feature_dict):
+        np.random.seed(self.seed)
+        dataset_dict = {'train': {}, 'test': {}} if self.evaluation_mode == 'matching' else {'train': {}}
+        for train_or_test in dataset_dict.keys():
+            merged_features, merged_labels = self._merge_features_and_labels(feature_dict, train_or_test)
+            if train_or_test == 'test' and self.evaluation_mode == 'matching':
+                # Store pair IDs alongside X/Y so alignment can map scores to building IDs
+                merged_pairs = self.test_neg_pairs + self.test_pos_pairs
+                dataset_dict = self._prepare_dataset(dataset_dict, train_or_test,
+                                                     merged_features, merged_labels,
+                                                     merged_pairs=merged_pairs)
+            else:
+                dataset_dict = self._prepare_dataset(dataset_dict, train_or_test,
+                                                     merged_features, merged_labels)
+        if config.Constants.save_dataset_dict:
+            self._save_dataset_dict(dataset_dict)
+        return dataset_dict
+
+    def _save_dataset_dict(self, dataset_dict):
+        dataset_dict_path = self._get_dataset_dict_path()
+        saving_message = f"dataset_dict was saved successfully"
+        error_message = f"Error happened while saving dataset_dict: "
+        try:
+            joblib.dump(dataset_dict, dataset_dict_path)
+            self.logger.info(saving_message)
+            self.logger.info('')
+        except Exception as e:
+            self.logger.error(f"{error_message}{e}")
+        return
+
+    def _load_dataset_dict(self):
+        dataset_dict_path = self._get_dataset_dict_path()
+        try:
+            dataset_dict = joblib.load(dataset_dict_path)
+            self.logger.info(f"dataset_dict was loaded successfully")
+            return dataset_dict
+        except Exception as e:
+            self.logger.error(f"Error happened while loading dataset_dict: {e}")
+            return None
+
+    def _get_dataset_dict_path(self):
+        dataset_dict_dir = config.FilePaths.dataset_dict_path
+        file_name = get_file_name()
+        if not os.path.exists(dataset_dict_dir):
+            os.makedirs(dataset_dict_dir)
+        dataset_dict_path = (f"{dataset_dict_dir}{file_name}_{self.evaluation_mode}_{self.dataset_size_version}_"
+                             f"neg_samples={self.neg_samples_num}_seed={self.seed}.joblib")
+        return dataset_dict_path
+
+    def _merge_features_and_labels(self, feature_dict, train_or_test):
+        neg_feature_vecs, pos_feature_vecs = feature_dict[train_or_test][0], feature_dict[train_or_test][1]
+        merged_features = neg_feature_vecs + pos_feature_vecs
+        merged_labels = [0] * len(neg_feature_vecs) + [1] * len(pos_feature_vecs)
+        return merged_features, merged_labels
+
+    @staticmethod
+    def _prepare_dataset(dataset_dict, file_type, merged_features, merged_labels,
+                         merged_pairs=None):
+        """
+        Shuffle features/labels (and optionally pair IDs) together and store in dataset_dict.
+
+        merged_pairs : list of (cand_id, index_id), parallel to merged_features.
+            When provided, dataset_dict[file_type]['pairs'] is stored in the same
+            shuffled order as X/Y — required for mapping classifier scores back to
+            building IDs in the alignment step.
+        """
+        if merged_pairs is not None:
+            combined = list(zip(merged_features, merged_labels, merged_pairs))
+            np.random.shuffle(combined)
+            dataset_dict[file_type]['X'] = np.array([e[0] for e in combined])
+            dataset_dict[file_type]['Y'] = np.array([e[1] for e in combined])
+            dataset_dict[file_type]['pairs'] = [e[2] for e in combined]
+        else:
+            combined = list(zip(merged_features, merged_labels))
+            np.random.shuffle(combined)
+            dataset_dict[file_type]['X'] = np.array([e[0] for e in combined])
+            dataset_dict[file_type]['Y'] = np.array([e[1] for e in combined])
+        return dataset_dict
+
+    def _train_and_evaluate(self, ):
+        if self.evaluation_mode == 'blocking':
+            feature_importance_dict, train_property_ratios = self._train_for_blocking()
+            if self.run_blocker_train:
+                self._run_blocker_train(feature_importance_dict, train_property_ratios)
+            else:
+                self._run_blocker(feature_importance_dict, train_property_ratios)
+            flexible_classifier = None
+        else:
+            flexible_classifier = self._run_matching_pipeline()
+            self._run_alignment(flexible_classifier)
+        return flexible_classifier
+
+    def _run_alignment(self, flexible_classifier_obj):
+        """
+        Stage 4: Estimate rigid 3D transform from high-confidence matches and
+        re-score all test pairs combining geometric + spatial proximity.
+
+        Requires:
+          - evaluation_mode == 'matching'
+          - dataset_dict['test']['pairs'] populated by _create_final_dict
+          - config.Alignment.enabled == True
+        """
+        if not config.Alignment.enabled:
+            return
+        if flexible_classifier_obj is None:
+            return
+        test_split = self.dataset_dict.get('test', {})
+        if 'pairs' not in test_split:
+            self.logger.warning("[_run_alignment] No pair IDs in dataset_dict['test']. "
+                                "Ensure load_dataset_dict=False so pairs are freshly built.")
+            return
+
+        # Pick the primary model for scoring
+        model_name = config.Models.model_to_use
+        if model_name not in flexible_classifier_obj.best_model_dict:
+            model_name = next(iter(flexible_classifier_obj.best_model_dict))
+        best_model = flexible_classifier_obj.best_model_dict[model_name]['model']
+
+        X_test = test_split['X']
+        pairs  = test_split['pairs']   # list of (cand_id, index_id), same shuffle order as X
+
+        # Geometric scores: P(match=1)
+        proba = best_model.predict_proba(X_test)
+        match_class_idx = list(best_model.classes_).index(1)
+        geo_scores = proba[:, match_class_idx]
+
+        scored_pairs = [(cid, iid, float(s)) for (cid, iid), s in zip(pairs, geo_scores)]
+
+        self.logger.info(
+            f"[_run_alignment] {len(scored_pairs)} test pairs | "
+            f"model={model_name} | "
+            f"anchors with score>={config.Alignment.confidence_threshold}: "
+            f"{sum(1 for _,_,s in scored_pairs if s >= config.Alignment.confidence_threshold)}"
+        )
+
+        aligner = RigidAligner(config.Alignment, logger=self.logger)
+        ground_truth_R = getattr(self._simulator, 'R_crs', None)
+        ground_truth_t = getattr(self._simulator, 't_crs', None)
+
+        rescored_pairs = aligner.run(
+            self.test_object_dict,
+            scored_pairs,
+            suffix=f"seed{self.seed}",
+            ground_truth_R=ground_truth_R,
+            ground_truth_t=ground_truth_t,
+        )
+
+        # Log final score improvement summary
+        if aligner.alignment_succeeded:
+            top_geo    = sorted(scored_pairs,   key=lambda x: x[2], reverse=True)[:10]
+            top_final  = sorted(rescored_pairs, key=lambda x: x[2], reverse=True)[:10]
+            self.logger.info(
+                f"[_run_alignment] Top-10 mean geometric score: "
+                f"{np.mean([s for _,_,s in top_geo]):.3f} → "
+                f"final score: {np.mean([s for _,_,s in top_final]):.3f}"
+            )
+
+        # Precision / Recall / F1 before and after alignment
+        self._log_alignment_metrics(scored_pairs, rescored_pairs, self.test_object_dict)
+
+    def _log_alignment_metrics(self, scored_pairs, rescored_pairs, test_object_dict,
+                               score_threshold=0.5, dist_thresholds=(10.0, 25.0, 50.0)):
+        """
+        Log two sets of metrics:
+
+        1. Score-based (before alignment only) — classifier Precision/Recall/F1
+           at score_threshold.  Measures how well the geometric features identify matches.
+
+        2. Distance-based (after alignment) — for each matched candidate, check whether
+           its aligned centroid is within dist_threshold meters of its true match centroid.
+           This is the correct evaluation after alignment: if the transform is good,
+           every candidate should be physically co-located with its match.
+
+        Note: candidates with no true match in the index are never counted as false
+        negatives — recall is only over the intersection (buildings with a real match).
+        """
+        # --- 1. Score-based metrics (before alignment) ---
+        def _prf(pairs):
+            tp = sum(1 for cid, iid, s in pairs if s >= score_threshold and cid == iid)
+            fp = sum(1 for cid, iid, s in pairs if s >= score_threshold and cid != iid)
+            fn = sum(1 for cid, iid, s in pairs if s <  score_threshold and cid == iid)
+            precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
+            recall    = tp / (tp + fn) if (tp + fn) > 0 else 0.0
+            f1        = (2 * precision * recall / (precision + recall)
+                         if (precision + recall) > 0 else 0.0)
+            return round(precision, 3), round(recall, 3), round(f1, 3)
+
+        pre_p, pre_r, pre_f1 = _prf(scored_pairs)
+        self.logger.info(
+            f"[Metrics] Before alignment (score≥{score_threshold}) — "
+            f"Precision: {pre_p}  Recall: {pre_r}  F1: {pre_f1}"
+        )
+
+        # --- 2. Distance-based metrics (after alignment) ---
+        # After alignment, test_object_dict['cands'] centroids are already transformed.
+        # True matches are pairs where cand_id == index_id.
+        cands  = test_object_dict.get('cands', {})
+        index  = test_object_dict.get('index', {})
+        # All matched cands (ground truth positives in the test set)
+        matched_cands = {cid for cid, iid, _ in scored_pairs if cid == iid and cid in cands and iid in index}
+        n_matched = len(matched_cands)
+
+        if n_matched == 0:
+            self.logger.warning("[Metrics] No matched pairs found for distance evaluation.")
+            return
+
+        # Compute distance from each aligned cand centroid to its true match index centroid
+        distances = []
+        for cid in matched_cands:
+            c_centroid = np.asarray(cands[cid]['centroid'], dtype=np.float64)
+            i_centroid = np.asarray(index[cid]['centroid'], dtype=np.float64)
+            distances.append(np.linalg.norm(c_centroid - i_centroid))
+
+        distances = np.array(distances)
+        self.logger.info(
+            f"[Metrics] After alignment (distance-based, n={n_matched} matched buildings) — "
+            f"mean dist: {distances.mean():.1f} m  |  "
+            f"median dist: {np.median(distances):.1f} m  |  "
+            f"max dist: {distances.max():.1f} m"
+        )
+        for d_thresh in dist_thresholds:
+            recall_d = round((distances < d_thresh).sum() / n_matched, 3)
+            self.logger.info(
+                f"[Metrics] After alignment  distance recall@{d_thresh:.0f}m: {recall_d} "
+                f"({(distances < d_thresh).sum()}/{n_matched} buildings within {d_thresh:.0f} m of true match)"
+            )
+
+    def _train_for_blocking(self):
+        self.logger.info("Training for blocking")
+        if config.Constants.load_train_items:
+            feature_importance_dict, matching_pairs_property_ratios = self._load_train_items()
+            if feature_importance_dict is not None and matching_pairs_property_ratios is not None:
+                return feature_importance_dict, matching_pairs_property_ratios
+        params_dict = self._read_config_models()
+        load_trained_models = config.Models.load_trained_models
+        cv = config.Models.cv
+        flexible_classifier_obj = FlexibleClassifier(self.dataset_dict, self.train_property_dict, params_dict,
+                                                     self.seed, self.logger, self.dataset_name, 'blocking',
+                                                     self.dataset_size_version, self.neg_samples_num,
+                                                     load_trained_models, cv)
+        feature_importance_dict = flexible_classifier_obj.feature_importance_extraction()
+        train_property_ratios = flexible_classifier_obj.get_property_ratios()
+        return feature_importance_dict, train_property_ratios
+
+    def _run_matching_pipeline(self):
+        self.logger.info("Training for matching")
+        params_dict = self._read_config_models()
+        load_trained_models = config.Models.load_trained_models
+        cv = config.Models.cv
+        flexible_classifier_obj = FlexibleClassifier(self.dataset_dict, None, params_dict, self.seed, self.logger,
+                                                     self.dataset_name, 'matching', self.dataset_size_version,
+                                                     self.neg_samples_num, load_trained_models, cv)
+        return flexible_classifier_obj
+
+
+    def _read_config_models(self):
+        model_list = config.Models.model_list if self.evaluation_mode == 'matching' else [config.Models.blocking_model]
+        params_dict = dict()
+        for model in model_list:
+            params_dict[model] = config.Models.params_dict[model]
+        return params_dict
+
+    def _get_result_dict(self):
+        if self.evaluation_mode == 'blocking':
+            if self.run_blocker_train:
+                return None
+            return {'blocking': self.blocking_result_dict}
+        elif self.evaluation_mode == 'matching':
+            return {'matching': self.flexible_classifier_obj.result_dict}
+        else:
+            raise ValueError(f"Evaluation mode {self.evaluation_mode} is not supported")

code/3dSAGER/requirements.txt

@@ -0,0 +1,40 @@
+# 3dSAGER — Python 3.9
+# Install with: pip install -r requirements.txt
+# Note: faiss should be installed via conda: conda install -c conda-forge faiss-cpu
+
+# Core ML / data
+numpy==1.26.4
+pandas==2.3.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+scipy==1.13.1
+joblib==1.5.2
+
+# Geometry / GIS
+shapely==2.0.5
+pyproj==3.6.1
+geopandas==0.14.4
+
+# Visualisation
+matplotlib==3.9.2
+
+# Utilities
+tqdm==4.67.1
+Pillow==9.4.0
+
+# Vector search (install via conda for best performance)
+# conda install -c conda-forge faiss-cpu
+# pip fallback:
+faiss-cpu==1.9.0
+
+# Deep learning (optional — required only for ViT-based blocking)
+torch==2.5.1
+torchvision==0.20.1
+# CLIP (OpenAI):
+# pip install git+https://github.com/openai/CLIP.git
+
+# Optional — CityGML utilities (generateCityGML.py, randomiseCity.py only)
+# lxml>=4.9
+
+clip 
+pyarrow

docs/STEM_benchmark_specification.md

@@ -0,0 +1,618 @@
+# STEM: Spatio-Temporal 3D Entity Matching Benchmark
+
+## Construction Specification Document
+
+**Version**: 1.0  
+**Date**: 2026-07-08  
+**Authors**: ZRH Edu  
+**Target Venue**: VLDB Journal Special Issue on "Spatio-Temporal Data Management and Analytics: Machine Learning-based Solutions and Beyond"  
+**HuggingFace Repository**: https://huggingface.co/datasets/eduzrh/STEM
+
+---
+
+## Table of Contents
+
+1. [Project Overview](#1-project-overview)
+2. [Research Motivation](#2-research-motivation)
+3. [Dataset Sources](#3-dataset-sources)
+4. [Benchmark Design](#4-benchmark-design)
+5. [Code Architecture](#5-code-architecture)
+6. [Environment Setup](#6-environment-setup)
+7. [Download Plan](#7-download-plan)
+8. [Preprocessing Pipeline](#8-preprocessing-pipeline)
+9. [Experimental Protocol](#9-experimental-protocol)
+10. [Delivery Checklist](#10-delivery-checklist)
+
+---
+
+## 1. Project Overview
+
+### 1.1 Goal
+
+Build the first **spatio-temporal 3D entity matching benchmark** that enables rigorous evaluation of matching algorithms for 3D building objects across both **different data sources** (cross-source entity resolution) and **different time points** (temporal entity resolution). The benchmark is designed to support both **unsupervised** and **few-shot** entity matching paradigms.
+
+### 1.2 Core Contributions
+
+| Contribution | Description |
+|---|---|
+| **STEM-3D Benchmark** | Multi-epoch, multi-source 3D building entity matching dataset with ground-truth labels |
+| **Disaster Simulation Pipeline** | Controllable CRS misalignment + structural damage for realistic post-disaster cross-time matching |
+| **Spatio-Temporal Evaluation Suite** | Standardized metrics for cross-time, cross-source 3D entity matching |
+| **Open-Source Toolkit** | Full codebase for reproduction, extending 3dSAGER to spatio-temporal setting |
+
+### 1.3 Why 3D Entity Matching Matters
+
+Traditional entity matching (EM) operates on tabular records. However, real-world geospatial entities — buildings, bridges, infrastructure — are inherently 3D objects. Matching them across datasets is critical for:
+
+- **Disaster response**: Match pre-disaster and post-disaster building inventories to assess damage
+- **Urban digital twins**: Fuse multi-source 3D city models (Overture, 3DBAG, OpenStreetMap, commercial providers)
+- **Cadastral reconciliation**: Match building registrations across jurisdictions and over time
+- **Infrastructure monitoring**: Track structural changes across satellite/airborne LiDAR surveys
+
+The **spatio-temporal** dimension adds unique challenges:
+1. Buildings can be **modified** (extensions, demolitions, renovations) between time points
+2. CRS (Coordinate Reference System) may differ between sources and epochs
+3. Geometric properties change with time (height reduction from damage, volume changes from extensions)
+4. No absolute coordinate alignment is available (post-disaster scenario)
+
+---
+
+## 2. Research Motivation
+
+### 2.1 The Spatio-Temporal Entity Matching Problem
+
+**Definition (Strong Spatio-Temporal EM)**: Given two collections of 3D building objects S_A_t and S_B_t' from potentially different sources A, B and different time points t, t', find all matching pairs (a in S_A_t, b in S_B_t') where a and b refer to the same real-world building.
+
+**Key differences from standard EM**:
+1. **Coordinate-unaware matching**: Cannot rely on absolute coordinates (different CRS, post-disaster shift)
+2. **Temporal geometry drift**: Properties change between t and t' → geometric feature ratios deviate from 1.0
+3. **Structural emergence/disappearance**: New buildings appear, old buildings are demolished
+4. **Cross-source schema heterogeneity**: Different LOD levels, attribute schemas, modeling conventions
+
+### 2.2 The (ε, δ)-Systematic Discrepancy Framework
+
+Following the 3dSAGER paper, we formalize cross-source systematic discrepancy:
+
+> For any geometric property p, there exists a source-specific scaling factor F_S^p such that for all matching buildings:  
+> p_cand / p_index ≈ F_S^p
+
+In the spatio-temporal setting, we extend this to:
+
+> p_cand_t' / p_index_t ≈ F_S^p · F_T^p
+
+where F_S^p is the source discrepancy and F_T^p is the temporal change factor.
+
+### 2.3 Methodological Approach
+
+We adopt and extend the **3dSAGER pipeline** for spatio-temporal entity matching:
+
+Raw 3D City Models (CityJSON/CityGML) → Preprocessing → Object Properties (25 geometric features) → Pairwise Feature Vectors (F÷ division operator) → BKAFI Blocking → ML Classifier (XGBoost, RF, etc.) → Rigid Alignment (RANSAC + SVD) → Final Matching
+
+**Key extensions for spatio-temporal**:
+1. **Temporal property delta features**: Δp = |p_t' - p_t| as additional features
+2. **Disaster-aware CRS simulation**: Random Z-rotation + damage (height reduction) for post-disaster scenarios
+3. **Multi-epoch evaluation**: Match across (t=2009, t'=2012/2015/2018) pairs
+4. **(ε, δ)-Temporal discrepancy tracking**: Measure F_T^p for each time gap
+## 3. Dataset Sources
+
+### 3.1 Primary Dataset: Lyon Multi-Epoch 3D City Model
+
+**Source**: Métropole de Lyon Open Data + Zenodo  
+**Format**: CityGML LOD2  
+**Temporal coverage**: 2009, 2012, 2015, 2018 (4 epochs, 9-year span)  
+**Coverage**: Lyon metropolitan area (59 communes)  
+**Key features**:
+- Same source/provider across all epochs (minimizes cross-source artifacts)
+- Known to contain real building changes (demolitions, new constructions, renovations)
+- Already used in 3D change detection research
+- CityGML format with semantic building parts
+
+| Epoch | Source | URL | Status |
+|---|---|---|---|
+| 2009 | Zenodo | https://zenodo.org/record/3611354 | Open access |
+| 2012 | Zenodo | https://zenodo.org/record/3611354 | Open access |
+| 2015 | Zenodo | https://zenodo.org/record/3611354 | Open access |
+| 2018 | Grand Lyon Portal | https://data.grandlyon.com | Open access |
+
+**Data characteristics**:
+- CRS: EPSG:3946 (RGF93 / CC46)
+- LOD: LOD2 (buildings with roof geometry, no detailed facade elements)
+- Format: CityGML 2.0
+- Estimated building count: ~150,000–200,000 per epoch
+
+### 3.2 Secondary Dataset: 3dSAGER Hague Benchmark
+
+**Source**: 3dSAGER project  
+**Format**: CityJSON  
+**Provider**: 3D BAG (Netherlands)  
+**Coverage**: The Hague, Netherlands  
+**Temporal**: Single epoch (2021/2022) with two co-registered sources (Source A, Source B)  
+**URL**: https://tinyurl.com/3dSAGERdataset  
+**Key features**:
+- Provides **ground-truth match labels** across Source A/B
+- Two sources with different LOD representations of the same area
+- Used as baseline for cross-source evaluation (matching mode)
+
+**Benchmark statistics** (medium version):
+- ~3,000–5,000 buildings
+- 25 geometric properties per building
+- Train/val/test split by spatial grid cells
+
+### 3.3 Temporal POI Data (Auxiliary)
+
+**Source**: OpenStreetMap History API  
+**Purpose**: Ground-truth verification for POI-level temporal entity matching  
+**Entities**: Restaurants, shops, offices with temporal change logs  
+**Temporal resolution**: Daily snapshots  
+**Usage**: Validate temporal matching methodology at smaller scale
+
+### 3.4 Dataset Summary Table
+
+| Dataset | Type | Buildings | Epochs | LOD | Format | Labels |
+|---|---|---|---|---|---|---|
+| Lyon 2009–2018 | Primary | ~150K/epoch | 4 | LOD2 | CityGML | Build from persistence |
+| Hague A/B | Baseline | ~4K | 1 (2 sources) | LOD1/LOD2 | CityJSON | Provided |
+| OSM POI History | Auxiliary | ~5K POIs | Daily | N/A | GeoJSON | OSM IDs |
+
+---
+
+## 4. Benchmark Design
+
+### 4.1 Task Taxonomy
+
+The STEM benchmark defines **three sub-tasks**:
+
+#### Task 1: Cross-Source 3D Entity Matching (Standard)
+- **Input**: Source A buildings + Source B buildings (same epoch, different providers)
+- **Goal**: Match corresponding buildings
+- **Baseline dataset**: Hague A/B
+- **Difficulty**: Cross-source schema heterogeneity (LOD differences)
+
+#### Task 2: Cross-Time 3D Entity Matching (Temporal)
+- **Input**: Buildings from epoch t + buildings from epoch t' (t ≠ t', same provider)
+- **Goal**: Match persistent buildings across time
+- **Primary dataset**: Lyon 2009–2018 (6 time pairs: 2009→2012, 2009→2015, 2009→2018, 2012→2015, 2012→2018, 2015→2018)
+- **Difficulty**: Temporal geometry drift, building modifications, demolitions/new constructions
+
+#### Task 3: Cross-Source + Cross-Time 3D Entity Matching (Full ST-EM)
+- **Input**: Source A at epoch t + Source B at epoch t'
+- **Goal**: Match buildings across both source and time
+- **Constructed via**: Apply disaster simulation (CRS + damage) to cross-time pairs
+- **Difficulty**: Maximum — both source and temporal discrepancies compound
+
+### 4.2 Ground-Truth Label Construction
+
+For the Lyon multi-epoch dataset, we construct ground-truth labels as follows:
+
+1. **Spatial persistence filter**: Buildings with centroid distance < 2m across epochs are candidates
+2. **Geometric consistency check**: Area ratio and volume ratio within [0.7, 1.3] → likely same building
+3. **Building ID tracking**: Use CityGML `gml:id` attributes where available (Lyon may have persistent IDs)
+4. **Manual sampling verification**: Random sample of 200 pairs per epoch gap for manual validation
+5. **Confidence tiers**:
+   - **High confidence**: Persistent CityGML ID + geometric consistency
+   - **Medium confidence**: Spatial + geometric consistency only
+   - **Low confidence**: Spatial proximity only (for hard negative mining)
+
+### 4.3 Evaluation Metrics
+
+| Metric | Description |
+|---|---|
+| **Precision** | TP / (TP + FP) |
+| **Recall** | TP / (TP + FN) |
+| **F1-Score** | Harmonic mean of Precision and Recall |
+| **Recall@k** | Recall within top-k candidates per query building |
+| **MRR** | Mean Reciprocal Rank |
+| **Distance Recall** | Recall at spatial distance thresholds (10m, 25m, 50m) |
+| **Temporal F1 Drop** | F1 degradation as time gap increases |
+| **Cross-Source F1 Drop** | F1 degradation from single-source to cross-source matching |
+| **Cross-Time F1 Drop** | F1 degradation from single-epoch to cross-epoch matching |
+
+### 4.4 Training Regime Taxonomy
+
+| Regime | Training Data | Test Data | Setting |
+|---|---|---|---|
+| **Supervised** | Labeled pairs from t→t' | Held-out pairs from same t→t' | Full labels available |
+| **Few-shot** | K labeled pairs (K=5,10,20,50) | All remaining pairs | Scarce supervision |
+| **Unsupervised (Zero-shot)** | No labels from target pair | All pairs from t→t' | Cross-epoch generalization |
+| **Cross-epoch transfer** | Train on t1→t2, test on t3→t4 | Transfer to unseen epochs | Temporal generalization |
+| **Cross-city transfer** | Train on Hague, test on Lyon | Transfer to unseen city | Geographic generalization |
+
+### 4.5 Benchmark Splits
+
+```
+STEM/
+├── lyon/
+│   ├── 2009/, 2012/, 2015/, 2018/    # CityJSON-converted buildings
+│   ├── labels/                        # Ground-truth match pairs per epoch pair
+│   └── splits/                        # train/val/test CSV files
+├── hague/
+│   ├── source_a/, source_b/
+│   ├── labels.csv
+│   └── splits/
+├── disaster/
+│   ├── simulated/                     # Disaster simulation outputs
+│   └── configs/                       # Simulation parameters
+└── metadata/
+    ├── dataset_card.md
+    ├── statistics.json
+    └── changelog.md
+```
+## 5. Code Architecture
+
+### 5.1 Repository Structure
+
+```
+STEM/
+├── README.md
+├── requirements.txt
+├── setup.py
+├── config/
+│   ├── default.yaml                  # Default configuration
+│   ├── lyon.yaml                     # Lyon-specific config
+│   ├── hague.yaml                    # Hague-specific config
+│   └── disaster.yaml                 # Disaster simulation config
+├── src/
+│   ├── __init__.py
+│   ├── data/
+│   │   ├── __init__.py
+│   │   ├── download.py               # Dataset downloader
+│   │   ├── preprocess.py             # CityJSON/GML conversion
+│   │   ├── labels.py                 # Ground-truth label construction
+│   │   └── splits.py                 # Train/val/test splitting
+│   ├── features/
+│   │   ├── __init__.py
+│   │   ├── properties.py             # 3dSAGER geometric properties (extended)
+│   │   ├── temporal.py               # Temporal delta features
+│   │   └── pairwise.py               # Pairwise feature vectors
+│   ├── matching/
+│   │   ├── __init__.py
+│   │   ├── blocking.py               # BKAFI + extended blocking methods
+│   │   ├── classifier.py             # ML classifier training
+│   │   ├── alignment.py              # RANSAC rigid alignment
+│   │   └── disaster.py               # Disaster simulation
+│   ├── evaluation/
+│   │   ├── __init__.py
+│   │   ├── metrics.py                # Evaluation metrics
+│   │   ├── analysis.py               # Result analysis utilities
+│   │   └── visualization.py          # Plots and reports
+│   └── utils/
+│       ├── __init__.py
+│       ├── io.py                     # File I/O utilities
+│       ├── geometry.py               # Geometric computation helpers
+│       └── logging.py                # Experiment logging
+├── experiments/
+│   ├── baseline_hague.py             # Reproduce 3dSAGER baseline
+│   ├── cross_time_lyon.py            # Cross-time matching experiments
+│   ├── few_shot_benchmark.py         # Few-shot evaluation
+│   ├── zero_shot_benchmark.py        # Unsupervised/zero-shot evaluation
+│   └── disaster_scenario.py          # Post-disaster matching
+├── notebooks/
+│   ├── 01_data_exploration.ipynb
+│   ├── 02_feature_analysis.ipynb
+│   └── 03_results_visualization.ipynb
+├── tests/
+│   ├── test_properties.py
+│   ├── test_blocking.py
+│   └── test_labels.py
+└── docs/
+    ├── api.md
+    ├── benchmark_guide.md
+    └── paper_figures/
+```
+
+### 5.2 Key Design Decisions
+
+1. **CityJSON as canonical format**: All datasets converted to CityJSON v1.1 for uniformity
+2. **Modular experiment scripts**: Each experiment type is a standalone script with YAML config
+3. **Reproducibility**: All random seeds logged; configs versioned with experiment outputs
+4. **HuggingFace Hub integration**: `push_to_hub()` calls in all data generation scripts
+5. **Streaming support**: Large CityJSON files use streaming parsing (ijson)
+
+### 5.3 Extension Points
+
+| Extension | Location | Purpose |
+|---|---|---|
+| New dataset | `src/data/preprocess.py` | Add CityJSON conversion for new cities |
+| New feature | `src/features/properties.py` | Add geometric/temporal property |
+| New blocking method | `src/matching/blocking.py` | Add candidate generation method |
+| New classifier | `src/matching/classifier.py` | Add ML/DL model |
+| New evaluation metric | `src/evaluation/metrics.py` | Add custom metric |
+
+---
+
+## 6. Environment Setup
+
+### 6.1 Python Environment
+
+```bash
+# Core environment
+Python 3.9+ (tested with 3.9–3.12)
+pip install -r requirements.txt
+
+# Key dependencies (all installed via pip):
+numpy==1.26.4
+pandas==2.3.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+shapely==2.0.5
+pyproj==3.6.1
+geopandas==0.14.4
+faiss-cpu==1.9.0
+scipy==1.13.1
+joblib==1.5.2
+matplotlib==3.9.2
+tqdm==4.67.1
+
+# Optional — PyTorch + CLIP (for ViT-based blocking):
+torch>=2.0.0
+torchvision>=0.15.0
+clip (from OpenAI GitHub)
+
+# HuggingFace:
+huggingface_hub>=0.20.0
+datasets>=2.14.0
+```
+
+### 6.2 Disk Space Requirements
+
+| Item | Estimated Size |
+|---|---|
+| Lyon 2009 CityGML | ~2–5 GB (compressed) |
+| Lyon 2012 CityGML | ~2–5 GB (compressed) |
+| Lyon 2015 CityGML | ~2–5 GB (compressed) |
+| Lyon 2018 CityGML | ~2–5 GB (compressed) |
+| Hague benchmark (A + B) | ~500 MB |
+| Processed CityJSON files | ~2–3 GB per epoch |
+| Feature caches (joblib) | ~200 MB per epoch |
+| Model files | ~50 MB |
+| **Total estimated** | **~25–40 GB** |
+
+## 7. Download Plan
+
+### 7.1 Step-by-Step Download Procedure
+
+#### Phase 1: Hague Baseline Dataset (Priority: HIGH)
+
+The 3dSAGER dataset contains The Hague buildings in CityJSON format, split into Source A and Source B directories. This is the baseline benchmark used in the 3dSAGER paper.
+
+URL: https://tinyurl.com/3dSAGERdataset  
+Expected output: data/hague/source_a/*.json, data/hague/source_b/*.json
+
+#### Phase 2: Lyon Multi-Epoch Dataset (Priority: HIGH)
+
+**Lyon 2009, 2012, 2015 from Zenodo**: https://zenodo.org/record/3611354  
+Contains CityGML files for Lyon arrondissements across three historic versions.
+
+**Lyon 2018 from Grand Lyon Portal**:  
+https://data.grandlyon.com/jeux-de-donnees/maquettes-3d-texturees-2018-communes-metropole-lyon/donnees
+
+#### Phase 3: Optional Additional Data
+
+- 3DBAG historical versions (API: https://api.3dbag.nl/)
+- OSM POI History (Overpass API with date filters)
+
+### 7.2 Download Verification
+
+- SHA256 hashes recorded for all downloaded files
+- Building counts cross-referenced with published statistics
+- Spatial extent and CRS validation
+## 8. Preprocessing Pipeline
+
+### 8.1 CityGML → CityJSON Conversion
+
+```
+CityGML (.gml/.xml)
+    ↓  citygml-tools or custom parser
+CityJSON (.json)
+    ↓  CRS unification → EPSG:7415 (or local UTM)
+    ↓  Centroid computation
+    ↓  Building-only filtering
+    ↓  Attribute standardization
+    ↓  Save as joblib/.pkl cache
+```
+
+**Tools**:
+- `citygml-tools` (Java CLI): `citygml-tools to-cityjson input.gml`
+- `cityjson` Python package: for validation and manipulation
+- Alternative: `cjio` Python package
+
+### 8.2 Ground-Truth Label Construction
+
+```
+For each epoch pair (ti, tj):
+├── Load buildings from ti and tj
+├── Compute pairwise centroid distances
+├── Filter: distance < 2m → candidate matches
+├── Compute geometric consistency (area, volume ratios)
+├── Filter: ratio ∈ [0.7, 1.3] → probable matches
+├── Check CityGML ID persistence (if available)
+├── Assign confidence tier (high/medium/low)
+├── Manual sampling for verification (200 pairs)
+└── Output: pairs CSV with [id_ti, id_tj, confidence, area_ratio, volume_ratio, distance]
+```
+
+### 8.3 Feature Extraction
+
+For each building, extract 25 geometric properties (from 3dSAGER):
+- **Size**: area, perimeter, volume, convex_hull_area, convex_hull_volume
+- **Shape**: compactness_2d, compactness_3d, cubeness, hemisphericality, fractality
+- **Dimensions**: bounding_box_width/length, aligned_bounding_box_width/length/height
+- **Distribution**: density, elongation, shape_ind, axes_symmetry, num_vertices, circumference
+- **Context**: ave_centroid_distance, height_diff, num_floors, perimeter_ind
+
+**Temporal extension** (new for STEM):
+- `delta_area`: |area_ti - area_tj| / area_ti
+- `delta_volume`: |volume_ti - volume_tj| / volume_ti
+- `delta_height`: |height_ti - height_tj| / height_ti
+- `delta_compactness_3d`: same for compactness
+- `time_gap_months`: temporal distance between epochs
+
+### 8.4 Disaster Simulation (for Task 3)
+
+```python
+DisasterSimulator:
+    CRS Simulation:
+        - Random Z-axis rotation: θ ~ Uniform(0, 360°)
+        - Random translation: t ~ Uniform(-100km, +100km) in X, Y
+        - Applied to all candidate buildings
+    
+    Damage Simulation:
+        - Per-building probability: P = 0.8
+        - Height reduction factor: f ~ Uniform(0.3, 0.95)
+        - Modified height = original_height × f
+        - Propagate to volume, convex_hull_volume, compactness_3d
+```
+
+---
+
+## 9. Experimental Protocol
+
+### 9.1 Baseline Experiment (Task 1: Cross-Source)
+
+| Parameter | Value |
+|---|---|
+| Dataset | Hague (Source A vs Source B) |
+| Features | 25 geometric properties |
+| Feature operator | Division (F÷) |
+| Blocking method | BKAFI |
+| Classifier | XGBoost |
+| Train:Val:Test | 60:20:20 |
+| Evaluation | Precision, Recall, F1, Recall@k |
+| Seeds | 5 |
+
+### 9.2 Cross-Time Experiment (Task 2)
+
+| Parameter | Value |
+|---|---|
+| Dataset | Lyon (6 epoch pairs) |
+| Train:Val:Test | 60:20:20 (per epoch pair) |
+| Features | 25 geometric + 5 temporal delta |
+| Classifier | XGBoost, GradientBoosting, RF, MLP |
+| Evaluation mode | matching + blocking |
+| Key analysis | F1 vs. time gap, property drift vs. time gap |
+
+### 9.3 Few-Shot Experiment
+
+| Shots | Setting |
+|---|---|
+| K = 5 | 5 labeled pairs per epoch pair |
+| K = 10 | 10 labeled pairs |
+| K = 20 | 20 labeled pairs |
+| K = 50 | 50 labeled pairs |
+| K = 100 | 100 labeled pairs |
+
+### 9.4 Full ST-EM Experiment (Task 3)
+
+| Parameter | Value |
+|---|---|
+| Base data | Lyon cross-time pairs |
+| Simulation | CRS rotation + damage |
+| Alignment | RANSAC rigid alignment |
+| Re-scoring | α × geometric + (1-α) × spatial |
+| Evaluation | Distance-based recall (10m, 25m, 50m) |
+
+### 9.5 Unsupervised/Zero-Shot Experiment
+
+| Setting | Description |
+|---|---|
+| Cross-epoch transfer | Train on 2009→2012, test on 2015→2018 |
+| Cross-city transfer | Train on Hague, test on Lyon (adapt features) |
+| Property-ratio heuristic | F÷ operator + clustering, no labels |
+## 10. Delivery Checklist
+
+### 10.1 Phase 1: Infrastructure (Current)
+
+- [x] 3dSAGER codebase cloned and analyzed
+- [x] Python environment with all dependencies installed
+- [x] HuggingFace repo initialized and configured
+- [x] Specification document written (this file)
+
+### 10.2 Phase 2: Data Acquisition
+
+- [x] Download 3dSAGER Hague dataset (Source A: 309MB, 36 CityJSON files; Source B: partial, 3 files)
+- [ ] Complete Source B download (remaining CityJSON files)
+- [ ] Download Lyon 2009-2015 from Zenodo (395 MB zip)
+- [ ] Download Lyon 2018 from Grand Lyon portal
+- [ ] Verify all downloads (counts, CRS, integrity)
+
+### 10.3 Phase 3: Preprocessing
+
+- [ ] Convert Lyon CityGML → CityJSON
+- [ ] Unify CRS across all epochs
+- [ ] Extract building geometries and compute centroids
+- [ ] Generate geometric properties per building
+- [ ] Cache processed data as joblib files
+
+### 10.4 Phase 4: Benchmark Construction
+
+- [ ] Construct ground-truth labels for all 6 Lyon epoch pairs
+- [ ] Manual verification of random samples
+- [ ] Generate train/val/test splits
+- [ ] Compute dataset statistics
+- [ ] Write dataset card (README.md for HuggingFace)
+
+### 10.5 Phase 5: Baseline Experiments
+
+- [ ] Run 3dSAGER baseline on Hague dataset (reproduce paper results)
+- [ ] Run cross-time matching on Lyon (supervised)
+- [ ] Run few-shot experiments (K = 5, 10, 20, 50)
+- [ ] Run disaster scenario (CRS + damage + alignment)
+- [ ] Run unsupervised transfer experiments
+
+### 10.6 Phase 6: Upload & Documentation
+
+- [ ] Upload all data to HuggingFace (eduzrh/STEM)
+- [ ] Upload all code to HuggingFace
+- [ ] Upload experiment results and logs
+- [ ] Write paper-ready result tables and figures
+- [ ] Push specification document
+
+### 10.7 Phase 7: Paper Writing
+
+- [ ] Draft VLDBJ paper outline
+- [ ] Write methodology section
+- [ ] Generate all figures and tables
+- [ ] Write experiments and results
+- [ ] Polish and submit
+
+---
+
+## Appendix A: Risk Analysis
+
+| Risk | Probability | Impact | Mitigation |
+|---|---|---|---|
+| Lyon data not downloadable | Low | High | Fall back to synthetic multi-epoch from 3DBAG versions |
+| CityGML IDs not persistent | Medium | Medium | Use geometric + spatial heuristics for labeling |
+| CityGML→CityJSON conversion fails | Low | Medium | Use `citygml-tools` Java tool as backup |
+| HuggingFace storage limits | Low | Low | Use Git LFS for large files; tier upgrade if needed |
+| Lyon building count too large | Medium | Medium | Use spatial subset (1–2 arrondissements) |
+| Disk space insufficient for Lyon | Medium | High | Use GPU machine with SSD; process per-arrondissement |
+
+## Appendix B: Timeline
+
+| Week | Milestone |
+|---|---|
+| Week 1 | Environment setup, data download, preprocessing |
+| Week 2 | Benchmark construction, baseline experiments |
+| Week 3 | Full experiments, result analysis |
+| Week 4 | Paper writing, final uploads |
+
+## Appendix C: Lyon Zenodo Data Details
+
+- **Zenodo Record**: https://zenodo.org/record/3611354
+- **File**: `Lyon_2009-2012-2015_Splitted_Stripped.zip` (394.9 MB compressed, ~50.9 GB uncompressed)
+- **MD5**: `74b4610a11d9ca37951f5de2b747794f`
+- **Content**: CityGML LOD2 buildings for Lyon, split by arrondissement, across 3 epochs (2009, 2012, 2015)
+- **Preprocessing**: Original data had structural issues resolved; texture coordinates removed
+- **2018 data**: Separate download from Grand Lyon Open Data Portal
+
+## Appendix D: Hague Dataset Details
+
+- **Google Drive Folder**: https://drive.google.com/drive/folders/11dC2jn9fajcn2W0LWKBCgyuBxabkz4WU
+- **Structure**:
+  - `RawCitiesData/The Hague/Source A/` — CityJSON files covering all wijks (36 files, ~309 MB)
+  - `RawCitiesData/The Hague/Source B/` — CityJSON files (3+ files, ~21 MB so far)
+  - `dataset_partitions/` — Pre-computed train/val/test splits (3 seed files)
+- **CRS**: EPSG:7415 (Amersfoort / RD New + NAP height)
+- **Format**: CityJSON v1.0/v1.1
+- **LOD**: Mixed LOD1/LOD2

experiments/results/FinalResults_Hague_allmodels_v1_Operator=division_Blocking=bkafi_matching_small_neg_samples=2_vector_normalization=True.csv

@@ -0,0 +1,5 @@
+,precision,recall,f1,training_time,inference_time
+XGBClassifier,0.997,1.0,0.998,0.22,0.0
+RandomForestClassifier,0.997,1.0,0.998,0.43,0.0
+AdaBoostClassifier,0.991,1.0,0.995,1.43,0.02
+MLPClassifier,0.958,0.979,0.968,4.92,0.0

experiments/results/Results_Hague_allmodels_v1_Operator=division_Blocking=bkafi.log

@@ -0,0 +1,103 @@
+INFO: dataset: Hague
+INFO: seeds_num: 1
+INFO: evaluation_mode: matching
+INFO: blocking_method: bkafi
+INFO: dataset_size_version: small
+INFO: vector_normalization: True
+INFO: sdr_factor: False
+INFO: neg_samples_num: 2
+INFO: bkafi_criterion: feature_importance
+INFO: 
+INFO: ==============================================================================================
+INFO: 
+INFO: Seed: 1
+INFO: ------------------------------------------------------------------------------
+INFO: Partition file missing — generating for seed 1...
+INFO: Loading dataset_partition_dict from data/dataset_partitions/Hague_seed1.pkl
+INFO: dataset_partition_dict was loaded successfully
+INFO: Generating test object dict and train object dict
+INFO: Loading preprocessed cache: data/object_dicts/Hague_raw.joblib
+INFO:   → 15545 cands, 15545 index buildings
+INFO: [grid filter] 20 shared cells → 9596 cands, 9596 index buildings
+INFO: Filtered to 1557 train pairs, 764 test pairs (available cands=9596, index=9596)
+INFO: Saving test object dict
+INFO: Saving train object dict to data/object_dicts/Hague/train_matching_small_neg_samples_num=2_seed_1.joblib
+INFO: Saving test object dict to data/object_dicts/Hague/test_matching_negative_sampling_small_neg_samples_num=2_seed_1.joblib
+INFO: _extract_pairs[train]: dropped 1446/3003 pairs with IDs not in object_dict (kept 1557)
+INFO: Generating train property dictionary
+INFO: Property dictionary generation time: 22.3
+
+INFO: train_property_dict was saved successfully
+INFO: 
+INFO: _extract_pairs[test]: dropped 895/1659 pairs with IDs not in object_dict (kept 764)
+INFO: Generating test property dictionary
+INFO: Property dictionary generation time: 11.16
+
+INFO: test_property_dict was saved successfully
+INFO: 
+INFO: Generating train feature vectors
+INFO: Generating test feature vectors
+INFO: dataset_dict was saved successfully
+INFO: 
+INFO: Training for matching
+INFO: Training model XGBClassifier...
+INFO: Model XGBClassifier was trained successfully in 0.22 seconds
+INFO: Model XGBClassifier was saved successfully (matching)
+INFO: 
+INFO: Model XGBClassifier was evaluated successfully in 0.0 seconds
+INFO: Training model RandomForestClassifier...
+INFO: Model RandomForestClassifier was trained successfully in 0.43 seconds
+INFO: Model RandomForestClassifier was saved successfully (matching)
+INFO: 
+INFO: Model RandomForestClassifier was evaluated successfully in 0.0 seconds
+INFO: Training model AdaBoostClassifier...
+INFO: Model AdaBoostClassifier was trained successfully in 1.43 seconds
+INFO: Model AdaBoostClassifier was saved successfully (matching)
+INFO: 
+INFO: Model AdaBoostClassifier was evaluated successfully in 0.02 seconds
+INFO: Training model MLPClassifier...
+INFO: Model MLPClassifier was trained successfully in 4.92 seconds
+INFO: Model MLPClassifier was saved successfully (matching)
+INFO: 
+INFO: Model MLPClassifier was evaluated successfully in 0.0 seconds
+INFO: 
+INFO: Results for model XGBClassifier:
+INFO: Precision: 0.997
+INFO: Recall: 1.0
+INFO: F1 score: 0.998
+INFO: ------------------------------------------------------------------------------
+INFO: 
+INFO: 
+INFO: Results for model RandomForestClassifier:
+INFO: Precision: 0.997
+INFO: Recall: 1.0
+INFO: F1 score: 0.998
+INFO: ------------------------------------------------------------------------------
+INFO: 
+INFO: 
+INFO: Results for model AdaBoostClassifier:
+INFO: Precision: 0.991
+INFO: Recall: 1.0
+INFO: F1 score: 0.995
+INFO: ------------------------------------------------------------------------------
+INFO: 
+INFO: 
+INFO: Results for model MLPClassifier:
+INFO: Precision: 0.958
+INFO: Recall: 0.979
+INFO: F1 score: 0.968
+INFO: ------------------------------------------------------------------------------
+INFO: 
+INFO: [_run_alignment] 764 test pairs | model=XGBClassifier | anchors with score>=0.8: 325
+INFO: [RigidAligner] RANSAC refit on 325/325 inliers (threshold=10.0 m, iterations=1000)
+INFO: [RigidAligner] RANSAC: 325 anchors | 325 inliers | inlier mean residual = 0.84 m (all-anchor mean = 0.84 m)
+INFO: [RigidAligner] Ground-truth comparison: rotation error = 0.03°, translation error = 185.3 m
+INFO: [RigidAligner] Score re-scaling: geometric mean=0.426 → final mean=0.414 (alpha=0.5)
+INFO: [RigidAligner] Aligned CityJSON written: results/aligned_candidates_seed1.json (326 buildings, 2.3 MB)
+INFO: [_run_alignment] Top-10 mean geometric score: 0.999 → final score: 0.999
+INFO: [Metrics] Before alignment (score≥0.5) — Precision: 0.997  Recall: 1.0  F1: 0.998
+INFO: [Metrics] After alignment (distance-based, n=326 matched buildings) — mean dist: 0.8 m  |  median dist: 0.7 m  |  max dist: 8.0 m
+INFO: [Metrics] After alignment  distance recall@10m: 1.0 (326/326 buildings within 10 m of true match)
+INFO: [Metrics] After alignment  distance recall@25m: 1.0 (326/326 buildings within 25 m of true match)
+INFO: [Metrics] After alignment  distance recall@50m: 1.0 (326/326 buildings within 50 m of true match)
+INFO: Done!

experiments/run_experiments.sh

@@ -0,0 +1,31 @@
+#!/bin/bash
+
+
+bkafi_criterion=("feature_importance")
+dataset_sizes=("small")
+normalizations=("True")
+sdr_factors=("False")
+eval_mode="matching"
+
+for criterion in "${bkafi_criterion[@]}"; do
+  for size in "${dataset_sizes[@]}"; do
+    for normalization in "${normalizations[@]}"; do
+      for sdr in "${sdr_factors[@]}"; do
+        echo "=================================================================================================="
+        # if eval_mode == blocking print the following
+        if [[ $eval_mode == "blocking" ]]; then
+          echo "Running blocking with: bkafi_criterion=$criterion, size=$size, vector_normalization=$normalization, SDR=$sdr"
+        else
+          echo "Running matching with: bkafi_criterion=$criterion, size=$size, vector_normalization=$normalization, SDR=$sdr"
+        fi
+
+        python main.py \
+          --bkafi_criterion $criterion \
+          --evaluation_mode $eval_mode \
+          --dataset_size_version $size \
+          --vector_normalization $normalization \
+          --sdr_factor $sdr
+      done
+    done
+  done
+done

experiments/run_full_pipeline.sh

@@ -0,0 +1,101 @@
+#!/bin/bash
+# =============================================================================
+# run_full_pipeline.sh
+# Full end-to-end 3dSAGER pipeline — run from scratch on a clean machine.
+#
+# Roles (after swap):
+#   cands  = Source B  (the set we try to match / may be damaged/rotated)
+#   index  = Source A  (the reference set)
+#
+# Usage:
+#   bash run_full_pipeline.sh [small|medium|large]
+#   Default dataset size: medium
+#
+# Requirements:
+#   - conda environment '3dsager' installed
+#   - CityJSON files present at paths in dataset_configs.json
+# =============================================================================
+
+set -e   # exit immediately on any error
+
+DATASET_SIZE=${1:-medium}
+DATASET_NAME="Hague"
+SEEDS=1
+CONDA_ENV="3dsager"
+
+echo "============================================================"
+echo "  3dSAGER Full Pipeline"
+echo "  Dataset: ${DATASET_NAME} | Size: ${DATASET_SIZE} | Seeds: ${SEEDS}"
+echo "============================================================"
+
+# ---------------------------------------------------------------------------
+# 0. Clean stale caches so nothing from a previous run bleeds through
+# ---------------------------------------------------------------------------
+echo ""
+echo "[0/4] Cleaning stale cache files..."
+
+rm -f  data/object_dicts/Hague_raw.joblib
+rm -f  data/object_dicts/Hague/*.joblib
+rm -f  data/property_dicts/Hague*.joblib
+rm -f  data/property_dicts/features.parquet
+rm -f  data/dataset_dicts/Hague*.joblib
+rm -f  data/dataset_partitions/Hague*.pkl
+rm -rf results/demo_inference/
+
+echo "  Done."
+
+# ---------------------------------------------------------------------------
+# 1. Preprocess raw CityJSON files → Hague_raw.joblib
+# ---------------------------------------------------------------------------
+echo ""
+echo "[1/4] Preprocessing CityJSON files (preprocess_hague.py)..."
+
+conda run -n ${CONDA_ENV} python preprocess_hague.py
+
+echo "  Done — Hague_raw.joblib created."
+
+# ---------------------------------------------------------------------------
+# 2. Train all models + blocking + alignment
+# ---------------------------------------------------------------------------
+echo ""
+echo "[2/4] Running main pipeline (train + block + test + align)..."
+
+conda run -n ${CONDA_ENV} python main.py \
+    --dataset_name       ${DATASET_NAME} \
+    --evaluation_mode    matching \
+    --dataset_size_version ${DATASET_SIZE} \
+    --seeds_num          ${SEEDS} \
+    --bkafi_criterion    feature_importance \
+    --vector_normalization True \
+    --sdr_factor         False
+
+echo "  Done — models saved, results CSV written."
+
+# ---------------------------------------------------------------------------
+# 3. Generate demo files for all 6 models
+# ---------------------------------------------------------------------------
+echo ""
+echo "[3/4] Generating demo inference files..."
+
+for MODEL in XGBClassifier GradientBoostingClassifier BaggingClassifier \
+             RandomForestClassifier AdaBoostClassifier MLPClassifier; do
+    echo "  → ${MODEL}"
+    conda run -n ${CONDA_ENV} python generate_demo_files.py \
+        --model ${MODEL} \
+        --seed  1
+done
+
+echo "  Done — demo files written to results/demo_inference/"
+
+# ---------------------------------------------------------------------------
+# 4. Summary
+# ---------------------------------------------------------------------------
+echo ""
+echo "============================================================"
+echo "  Pipeline complete."
+echo ""
+echo "  Results CSV:   results/matching csv files/"
+echo "  Aligned JSON:  results/aligned_candidates_seed1.json"
+echo "  Demo files:    results/demo_inference/"
+echo "  Log:           results/Results_${DATASET_NAME}_allmodels_v1_*.log"
+echo "============================================================"