Deploy curated change-detection demo (FastAPI + React/MapLibre, Docker :7860); bundles pulled from the Model repo at startup

.dockerignore

@@ -0,0 +1,8 @@
+frontend/node_modules
+frontend/dist
+backend/__pycache__
+**/__pycache__
+**/*.pyc
+.venv
+venv
+*.log

.gitattributes

@@ -33,3 +33,17 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_00_test77/after.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_00_test77/before.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_01_test45/after.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_01_test45/before.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_02_test20/after.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_02_test20/before.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_03_test118/after.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_03_test118/before.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_04_test34/after.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_04_test34/before.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_05_test08/after.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_05_test08/before.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_06_test32/after.png filter=lfs diff=lfs merge=lfs -text
+backend/data/curated/levir_06_test32/before.png filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,32 @@
+# Curated change-detection Space — single container, FastAPI serves the React/MapLibre build +
+# the inference API on :7860 (PRD §10). Multi-stage: node builds the static frontend, then a slim
+# python runtime serves it alongside onnxruntime (CPU) inference.
+
+# ---- stage 1: build the frontend ------------------------------------------------------------
+FROM node:20-slim AS frontend
+WORKDIR /frontend
+COPY frontend/package.json frontend/package-lock.json* ./
+RUN npm ci || npm install
+COPY frontend/ ./
+RUN npm run build
+
+# ---- stage 2: python runtime ----------------------------------------------------------------
+FROM python:3.11-slim AS runtime
+WORKDIR /app
+ENV PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1 \
+    HF_HOME=/tmp/hf \
+    BUNDLES_DIR=/app/models \
+    CURATED_DIR=/app/data/curated \
+    FRONTEND_DIST=/app/frontend_dist
+
+COPY backend/requirements.txt ./requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY backend/ ./
+COPY --from=frontend /frontend/dist ./frontend_dist
+
+# Model bundles: either baked into ./models at build time, or pulled at startup from a companion
+# HF Model repo by setting HF_BUNDLE_REPO (PRD §3/§9 — the Space pulls weights, never trains).
+EXPOSE 7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,10 +1,66 @@
 ---
-title: Geospatial1
-emoji: 🌖
-colorFrom: yellow
-colorTo: purple
+title: Satellite Change Detection (Curated)
+emoji: 🛰️
+colorFrom: blue
+colorTo: gray
 sdk: docker
+app_port: 7860
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Satellite Change Detection — curated demo (Track A)
+
+A single-container [Hugging Face Docker Space](https://huggingface.co/docs/hub/spaces-sdks-docker):
+**FastAPI** (uvicorn, `:7860`) serves a **React + MapLibre GL** build and a CPU **onnxruntime**
+inference API. This is the **curated** before/after mode (PRD §10.1) — the always-fast demo path;
+the live Sentinel-2 AOI mode is a later milestone.
+
+## What it does
+
+- Pick a curated before/after scene pair and a model bundle.
+- The backend runs the pair through the exported **ONNX** change-detection model (CPU) and returns a
+  change-mask overlay + stats (% area changed, mean confidence, inference time).
+- A MapLibre **swipe slider** compares before vs after; the detected change is a colored overlay with
+  an **opacity** control. A **model-card** page carries the real LEVIR-CD results and limitations.
+
+## Model bundles (the contract)
+
+The app consumes only **artifact bundles** produced by `src/export.py` (PRD §3/§9), never the
+training code. Each bundle is a directory with:
+
+```
+model.onnx           # exported graph (parity-checked against PyTorch)
+preprocessing.json   # normalization, input size, band order, tiling, recommended threshold
+config.yaml          # provenance
+metrics_card.md      # headline metrics
+parity.json          # recorded PyTorch↔ONNXRuntime parity
+```
+
+Bundles are either baked into `./models` at build time or pulled at startup from a companion HF
+**Model repo** by setting the `HF_BUNDLE_REPO` env var — this keeps the Space lean and separates
+weights from the app.
+
+## Run locally
+
+```bash
+# 1. export at least one bundle (from the repo root, with the train env):
+python -m src.export --config configs/levircd_segformer.yaml   # or --random-init to smoke it
+cp -r bundles/* app/backend/models/
+
+# 2. synthesize curated pairs (or drop real LEVIR-CD tiles into app/backend/data/curated/):
+python app/backend/gen_sample_pairs.py --out app/backend/data/curated
+
+# 3. build the frontend + run the API (serves the build at http://localhost:7860):
+cd app/frontend && npm ci && npm run build && cd ..
+BUNDLES_DIR=backend/models CURATED_DIR=backend/data/curated FRONTEND_DIST=frontend/dist \
+  uvicorn backend.app:app --host 0.0.0.0 --port 7860
+```
+
+Or build the whole thing with Docker: `docker build -t sat-cd app/ && docker run -p 7860:7860 sat-cd`.
+
+## Notes / honesty
+
+- Change-class metrics only — overall pixel accuracy is meaningless when change is a tiny pixel
+  fraction (see the model card).
+- Trained weights inherit LEVIR-CD research/non-commercial terms — showcase use only.

backend/app.py

@@ -0,0 +1,198 @@
+"""FastAPI app for the curated change-detection Space (PRD §10).
+
+Single container (HF Spaces Docker SDK): this process serves the built React/MapLibre frontend as
+static files AND the inference API on port 7860. CPU-only ``onnxruntime``.
+
+Endpoints
+    GET  /api/health            liveness + what's loaded
+    GET  /api/models            available model bundles (id, input size, threshold, ...)
+    GET  /api/models/{id}/card  the bundle's metrics_card.md (markdown)
+    GET  /api/curated           curated before/after pairs (id, title, size, ...)
+    GET  /api/curated/{id}/{which}.png   the before/after image
+    POST /api/predict           {pair_id, model_id} -> change overlay (PNG data URL) + stats
+
+Config via env:
+    BUNDLES_DIR   dir of exported model bundles     (default: app/backend/models)
+    CURATED_DIR   dir of curated pairs + manifest    (default: app/backend/data/curated)
+    FRONTEND_DIST built React app to serve at /      (default: app/frontend/dist)
+    HF_BUNDLE_REPO  optional HF model repo to pull bundles from at startup (id or url)
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import threading
+from pathlib import Path
+from typing import Any
+
+from curated import CuratedRegistry
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse, HTMLResponse, PlainTextResponse
+from fastapi.staticfiles import StaticFiles
+from inference import BundleRegistry
+from pydantic import BaseModel
+
+_HERE = Path(__file__).resolve().parent
+BUNDLES_DIR = Path(os.environ.get("BUNDLES_DIR", _HERE / "models"))
+CURATED_DIR = Path(os.environ.get("CURATED_DIR", _HERE / "data" / "curated"))
+FRONTEND_DIST = Path(os.environ.get("FRONTEND_DIST", _HERE.parent / "frontend" / "dist"))
+
+
+def _maybe_pull_bundles() -> None:
+    """If HF_BUNDLE_REPO is set and BUNDLES_DIR is empty, pull the bundles from the HF Model repo.
+
+    This is the HF-Space networking path (PRD §3/§9): the Space pulls weights from a companion
+    Model repo at startup. It is a no-op locally (repo unset) and never runs on Leonardo.
+    """
+    repo = os.environ.get("HF_BUNDLE_REPO", "").strip()
+    if not repo or (BUNDLES_DIR.exists() and any(BUNDLES_DIR.iterdir())):
+        return
+    try:
+        from huggingface_hub import snapshot_download
+
+        BUNDLES_DIR.mkdir(parents=True, exist_ok=True)
+        snapshot_download(repo_id=repo, repo_type="model", local_dir=str(BUNDLES_DIR))
+    except Exception as exc:  # noqa: BLE001 — startup pull is best-effort; app still serves UI
+        print(f"[startup] bundle pull from {repo!r} failed: {exc}")
+
+
+_maybe_pull_bundles()
+models = BundleRegistry(BUNDLES_DIR)
+curated = CuratedRegistry(CURATED_DIR)
+_predict_cache: dict[tuple[str, str], dict[str, Any]] = {}
+# Curated pairs + models are fixed, so predictions are deterministic. tile+stitch inference is a few
+# seconds of CPU per scene (DINOv2), so we bake predictions to disk (the deploy plan): the file is
+# loaded instantly at startup and any gaps are filled by a background prewarm.
+_CACHE_FILE = CURATED_DIR / "_predictions.json"
+
+
+def _load_prediction_cache() -> None:
+    if not _CACHE_FILE.exists():
+        return
+    try:
+        data = json.loads(_CACHE_FILE.read_text())
+    except Exception as exc:  # noqa: BLE001
+        print(f"[cache] could not read {_CACHE_FILE}: {exc}")
+        return
+    valid = set(curated.pairs)
+    for key, result in data.items():
+        pair_id, _, model_id = key.partition("||")
+        if pair_id in valid and model_id in models.ids():
+            _predict_cache[(pair_id, model_id)] = result
+
+
+def _save_prediction_cache() -> None:
+    try:
+        data = {f"{p}||{m}": r for (p, m), r in _predict_cache.items()}
+        _CACHE_FILE.write_text(json.dumps(data))
+    except Exception as exc:  # noqa: BLE001
+        print(f"[cache] could not write {_CACHE_FILE}: {exc}")
+
+
+def _compute_prediction(pair_id: str, model_id: str) -> dict[str, Any]:
+    """Cached tile+stitch prediction for a (pair, model) — populated on demand and by prewarm."""
+    key = (pair_id, model_id)
+    cached = _predict_cache.get(key)
+    if cached is not None:
+        return cached
+    before, after = curated.get_pair(pair_id)
+    result = models.predict(model_id, before, after)
+    result.update({"pair_id": pair_id, "model_id": model_id})
+    _predict_cache[key] = result
+    return result
+
+
+def _prewarm() -> None:
+    """Fill any (pair, model) predictions missing from the on-disk cache, then persist them so the
+    next startup is instant. On-demand requests still work while this runs."""
+    changed = False
+    for pair_id in list(curated.pairs):
+        for model_id in models.ids():
+            if (pair_id, model_id) in _predict_cache:
+                continue
+            try:
+                _compute_prediction(pair_id, model_id)
+                changed = True
+            except Exception as exc:  # noqa: BLE001 — best-effort warm; on-demand path still serves
+                print(f"[prewarm] {pair_id}/{model_id} failed: {exc}")
+    if changed:
+        _save_prediction_cache()
+
+
+_load_prediction_cache()
+threading.Thread(target=_prewarm, daemon=True).start()
+
+app = FastAPI(title="Satellite Change Detection — curated demo", version="1.0")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # single-origin in prod; permissive so `vite dev` can call the API too
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class PredictRequest(BaseModel):
+    pair_id: str
+    model_id: str
+
+
+@app.get("/api/health")
+def health() -> dict[str, Any]:
+    return {
+        "status": "ok",
+        "models": models.ids(),
+        "n_curated": len(curated.pairs),
+        "bundles_dir": str(BUNDLES_DIR),
+    }
+
+
+@app.get("/api/models")
+def list_models() -> list[dict[str, Any]]:
+    return models.summaries()
+
+
+@app.get("/api/models/{model_id}/card", response_class=PlainTextResponse)
+def model_card(model_id: str) -> str:
+    try:
+        return models.get(model_id).metrics_card or "_No metrics card in bundle._"
+    except KeyError as exc:
+        raise HTTPException(404, f"unknown model {model_id!r}") from exc
+
+
+@app.get("/api/curated")
+def list_curated() -> list[dict[str, Any]]:
+    return curated.list()
+
+
+@app.get("/api/curated/{pair_id}/{which}.png")
+def curated_image(pair_id: str, which: str) -> FileResponse:
+    try:
+        path = curated.image_path(pair_id, which)
+    except (KeyError, ValueError) as exc:
+        raise HTTPException(404, f"no {which} image for pair {pair_id!r}") from exc
+    return FileResponse(path, media_type="image/png")
+
+
+@app.post("/api/predict")
+def predict(req: PredictRequest) -> dict[str, Any]:
+    if req.model_id not in models.ids():
+        raise HTTPException(404, f"unknown model {req.model_id!r}")
+    if req.pair_id not in curated.pairs:
+        raise HTTPException(404, f"unknown pair {req.pair_id!r}")
+    return _compute_prediction(req.pair_id, req.model_id)
+
+
+# --- static frontend (mounted LAST so the /api/* routes above take precedence) ---------------
+# html=True serves index.html for "/" and correctly serves hashed JS/CSS + binary assets.
+if FRONTEND_DIST.exists():
+    app.mount("/", StaticFiles(directory=FRONTEND_DIST, html=True), name="frontend")
+else:
+
+    @app.get("/", response_class=HTMLResponse)
+    def index_missing() -> str:
+        return (
+            "<h1>Frontend not built</h1><p>Run <code>npm ci &amp;&amp; npm run build</code> in "
+            "<code>app/frontend</code>, or use the multi-stage Dockerfile.</p>"
+        )

backend/curated.py

@@ -0,0 +1,64 @@
+"""Curated before/after pair registry for the ``/curated`` demo mode (PRD §10.1).
+
+A curated *pair* is two co-registered RGB tiles of the same place at two times. On the real Space
+these are LEVIR-CD test samples (plus a couple of well-known change events); the registry reads a
+``manifest.json`` describing each pair and serves the images from disk. ``scripts/gen_sample_pairs``
+can synthesize placeholder pairs so the pipeline is demonstrable before the real tiles are staged.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+from PIL import Image
+
+
+class CuratedRegistry:
+    """Reads ``<data_dir>/manifest.json`` -> pairs, each with ``before.png`` / ``after.png``."""
+
+    def __init__(self, data_dir: str | Path) -> None:
+        self.data_dir = Path(data_dir)
+        self.pairs: dict[str, dict[str, Any]] = {}
+        self.reload()
+
+    def reload(self) -> None:
+        self.pairs.clear()
+        manifest = self.data_dir / "manifest.json"
+        if not manifest.exists():
+            return
+        for entry in json.loads(manifest.read_text()).get("pairs", []):
+            pid = str(entry["id"])
+            if (self.data_dir / pid / "before.png").exists():
+                self.pairs[pid] = entry
+
+    def list(self) -> list[dict[str, Any]]:
+        out = []
+        for pid, entry in self.pairs.items():
+            before = self.data_dir / pid / "before.png"
+            with Image.open(before) as im:
+                w, h = im.size
+            out.append(
+                {
+                    "id": pid,
+                    "title": entry.get("title", pid),
+                    "description": entry.get("description", ""),
+                    "source": entry.get("source", ""),
+                    "width": w,
+                    "height": h,
+                }
+            )
+        return out
+
+    def image_path(self, pair_id: str, which: str) -> Path:
+        if pair_id not in self.pairs:
+            raise KeyError(pair_id)
+        if which not in ("before", "after"):
+            raise ValueError(which)
+        return self.data_dir / pair_id / f"{which}.png"
+
+    def get_pair(self, pair_id: str) -> tuple[Image.Image, Image.Image]:
+        before = Image.open(self.image_path(pair_id, "before")).convert("RGB")
+        after = Image.open(self.image_path(pair_id, "after")).convert("RGB")
+        return before, after

backend/data/curated/manifest.json

@@ -0,0 +1,46 @@
+{
+  "pairs": [
+    {
+      "id": "levir_00_test77",
+      "title": "Building change",
+      "description": "Real LEVIR-CD test crop; ~15.0% of pixels are annotated change.",
+      "source": "LEVIR-CD test_77"
+    },
+    {
+      "id": "levir_01_test45",
+      "title": "Building change",
+      "description": "Real LEVIR-CD test crop; ~25.1% of pixels are annotated change.",
+      "source": "LEVIR-CD test_45"
+    },
+    {
+      "id": "levir_02_test20",
+      "title": "Building change",
+      "description": "Real LEVIR-CD test crop; ~20.6% of pixels are annotated change.",
+      "source": "LEVIR-CD test_20"
+    },
+    {
+      "id": "levir_03_test118",
+      "title": "Building change",
+      "description": "Real LEVIR-CD test crop; ~14.3% of pixels are annotated change.",
+      "source": "LEVIR-CD test_118"
+    },
+    {
+      "id": "levir_04_test34",
+      "title": "Building change",
+      "description": "Real LEVIR-CD test crop; ~13.4% of pixels are annotated change.",
+      "source": "LEVIR-CD test_34"
+    },
+    {
+      "id": "levir_05_test08",
+      "title": "Building change",
+      "description": "Real LEVIR-CD test crop; ~8.1% of pixels are annotated change.",
+      "source": "LEVIR-CD test_8"
+    },
+    {
+      "id": "levir_06_test32",
+      "title": "Stable scene",
+      "description": "Sharpest LEVIR-CD test tile; ~1.5% annotated change \u2014 false-positive check.",
+      "source": "LEVIR-CD test_32"
+    }
+  ]
+}

backend/gen_sample_pairs.py

@@ -0,0 +1,133 @@
+"""Generate synthetic before/after curated pairs so the Space is demonstrable offline.
+
+These are **placeholders**, not LEVIR-CD: textured pseudo-aerial tiles where the "after" image
+adds/removes a few bright rectangles ("buildings"). They exercise the full curated pipeline (slider,
+overlay, stats) end to end. Replace with real LEVIR-CD test tiles for the shipped Space.
+
+    python app/backend/gen_sample_pairs.py --out app/backend/data/curated
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+SIZE = 256
+
+
+def _texture(rng: np.random.Generator, base: tuple[int, int, int]) -> np.ndarray:
+    """Low-frequency muted tonal ground texture around a base RGB colour (pseudo-aerial)."""
+    # a single smooth grayscale field (upsampled low-res noise) drives tonal variation on all
+    # channels so the result reads like ground/vegetation, not colour noise.
+    small = rng.normal(0, 1, (12, 12)).astype(np.float32)
+    norm = (small - small.min()) / (float(small.max() - small.min()) + 1e-6)
+    field_img = Image.fromarray((norm * 255).astype("uint8")).resize((SIZE, SIZE), Image.BICUBIC)
+    field = (np.asarray(field_img, dtype=np.float32) - 128.0) * 0.42  # signed, ~[-54, 54]
+    img = np.asarray(base, dtype=np.float32)[None, None, :] + field[:, :, None]
+    img += rng.normal(0, 4, (SIZE, SIZE, 3))  # fine grain
+    return np.clip(img, 0, 255)
+
+
+def _add_building(img: np.ndarray, rng: np.random.Generator) -> None:
+    """Paint one bright rectangular 'building' with a slight roof-colour jitter (in place)."""
+    h = int(rng.integers(14, 40))
+    w = int(rng.integers(14, 40))
+    y = int(rng.integers(0, SIZE - h))
+    x = int(rng.integers(0, SIZE - w))
+    roof = np.asarray([210, 205, 195], dtype=np.float32) + rng.normal(0, 12, 3)
+    img[y : y + h, x : x + w] = np.clip(roof, 60, 255)
+    img[y : y + 2, x : x + w] = np.clip(roof - 45, 0, 255)  # a little shadow/eave
+
+
+def make_pair(
+    seed: int, n_common: int, n_added: int, n_removed: int
+) -> tuple[Image.Image, Image.Image]:
+    rng = np.random.default_rng(seed)
+    base = (95 + int(rng.integers(-10, 10)), 110 + int(rng.integers(-10, 10)), 80)
+    ground = _texture(rng, base)
+    before = ground.copy()
+    after = ground.copy()
+
+    for _ in range(n_common):  # unchanged buildings — present in both dates
+        r2 = np.random.default_rng(int(rng.integers(0, 1 << 31)))
+        h = int(r2.integers(14, 40))
+        w = int(r2.integers(14, 40))
+        y = int(r2.integers(0, SIZE - h))
+        x = int(r2.integers(0, SIZE - w))
+        roof = np.clip(np.asarray([205, 200, 190], np.float32) + r2.normal(0, 10, 3), 60, 255)
+        before[y : y + h, x : x + w] = roof
+        after[y : y + h, x : x + w] = roof
+
+    for _ in range(n_removed):  # in before only (demolished)
+        _add_building(before, rng)
+    for _ in range(n_added):  # in after only (constructed) — the real "change"
+        _add_building(after, rng)
+
+    return (
+        Image.fromarray(before.astype("uint8"), "RGB"),
+        Image.fromarray(after.astype("uint8"), "RGB"),
+    )
+
+
+SPECS = [
+    (
+        "suburban_growth",
+        "Suburban growth",
+        "Several new houses appear on former open ground.",
+        5,
+        4,
+        0,
+    ),
+    (
+        "infill_and_demo",
+        "Infill + demolition",
+        "Two buildings removed, three added between dates.",
+        6,
+        3,
+        2,
+    ),
+    (
+        "dense_block",
+        "Dense urban block",
+        "Mostly stable; one new structure on the block edge.",
+        9,
+        1,
+        0,
+    ),
+    (
+        "no_change",
+        "Stable scene (control)",
+        "No construction — tests the false-positive rate.",
+        7,
+        0,
+        0,
+    ),
+]
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--out", default="app/backend/data/curated")
+    args = ap.parse_args()
+    out = Path(args.out)
+    out.mkdir(parents=True, exist_ok=True)
+
+    pairs = []
+    for i, (pid, title, desc, common, added, removed) in enumerate(SPECS):
+        before, after = make_pair(seed=100 + i, n_common=common, n_added=added, n_removed=removed)
+        (out / pid).mkdir(exist_ok=True)
+        before.save(out / pid / "before.png")
+        after.save(out / pid / "after.png")
+        pairs.append(
+            {"id": pid, "title": title, "description": desc, "source": "synthetic placeholder"}
+        )
+    (out / "manifest.json").write_text(json.dumps({"pairs": pairs}, indent=2))
+    print(f"wrote {len(pairs)} synthetic pairs to {out}")
+
+
+if __name__ == "__main__":
+    main()

backend/inference.py

@@ -0,0 +1,242 @@
+"""ONNX bundle inference for the curated Space (Track A, CPU onnxruntime).
+
+A *bundle* is exactly what ``src/export.py`` emits (PRD §3/§9): a directory with ``model.onnx``
+and ``preprocessing.json`` (normalization, input size, band order, tiling, recommended threshold).
+The demo consumes only the bundle — it never imports the training code. This module discovers
+bundles under ``BUNDLES_DIR``, lazily builds an ``onnxruntime`` session per model, and runs a
+before/after RGB pair through the documented preprocessing to produce a change-mask overlay + stats.
+
+The preprocessing here mirrors ``src/data/levircd.py`` + the export contract: resize each date to
+the bundle's ``input_size`` (DINOv2 needs the fixed 448 grid; the CNN tiers accept it too), scale to
+[0, 1], standardize with the bundle's mean/std, stack the two dates to ``(1, 2, 3, S, S)``.
+"""
+
+from __future__ import annotations
+
+import base64
+import io
+import json
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import onnxruntime as ort
+from PIL import Image, ImageFilter
+
+# Amber "thermal" change signal, painted directly into the overlay PNG (the frontend no longer
+# hue-rotates). Rendered as a translucent fill with a brighter, crisp 1-2px outline so the buildings
+# underneath stay visible. The opacity slider (CSS) scales the whole overlay.
+FILL_RGB = (255, 141, 52)
+EDGE_RGB = (255, 201, 112)
+FILL_ALPHA = 130  # ~51% in-PNG; slider default 0.75 -> ~0.38 effective fill
+EDGE_ALPHA = 255
+
+
+@dataclass
+class Bundle:
+    """A loaded model bundle: its preprocessing contract + a lazily-created ORT session."""
+
+    model_id: str
+    root: Path
+    preprocessing: dict[str, Any]
+    metrics_card: str
+    _session: ort.InferenceSession | None = field(default=None, repr=False)
+
+    @property
+    def input_size(self) -> int:
+        return int(self.preprocessing.get("input_size", 256))
+
+    @property
+    def threshold(self) -> float:
+        return float(self.preprocessing.get("output", {}).get("recommended_threshold", 0.5))
+
+    @property
+    def tile_size(self) -> int:
+        """Native tile the model was trained on (0.5 m/px). The full scene is tiled into these,
+        each inferred at ``input_size`` and stitched — the bundle's documented preprocessing."""
+        return int(self.preprocessing.get("tiling", {}).get("tile_size", self.input_size))
+
+    @property
+    def mean(self) -> np.ndarray:
+        m = self.preprocessing.get("normalization", {}).get("mean", [0.485, 0.456, 0.406])
+        return np.asarray(m, dtype=np.float32).reshape(3, 1, 1)
+
+    @property
+    def std(self) -> np.ndarray:
+        s = self.preprocessing.get("normalization", {}).get("std", [0.229, 0.224, 0.225])
+        return np.asarray(s, dtype=np.float32).reshape(3, 1, 1)
+
+    @property
+    def is_placeholder(self) -> bool:
+        return "RANDOM-INIT" in str(self.preprocessing.get("weights", ""))
+
+    def session(self) -> ort.InferenceSession:
+        if self._session is None:
+            so = ort.SessionOptions()
+            so.intra_op_num_threads = 2  # HF free tier is ~2 vCPU; ORT clamps to the host anyway
+            self._session = ort.InferenceSession(
+                str(self.root / "model.onnx"),
+                sess_options=so,
+                providers=["CPUExecutionProvider"],
+            )
+        return self._session
+
+    def summary(self) -> dict[str, Any]:
+        cfg_name = self.preprocessing.get("dinov2_note")
+        return {
+            "id": self.model_id,
+            "input_size": self.input_size,
+            "dynamic_hw": bool(self.preprocessing.get("dynamic_hw", False)),
+            "threshold": self.threshold,
+            "band_order": self.preprocessing.get("band_order", ["R", "G", "B"]),
+            "is_placeholder": self.is_placeholder,
+            "fixed_grid": cfg_name is not None,
+        }
+
+
+def _to_input(img: Image.Image, size: int, mean: np.ndarray, std: np.ndarray) -> np.ndarray:
+    """PIL RGB -> normalized ``(3, size, size)`` float32 (resize bilinear, /255, standardize)."""
+    arr = np.asarray(img.convert("RGB").resize((size, size), Image.BILINEAR), dtype=np.float32)
+    chw = arr.transpose(2, 0, 1) / 255.0
+    return (chw - mean) / std
+
+
+def _overlay_png(mask: np.ndarray, out_size: tuple[int, int]) -> str:
+    """Render the change mask as a translucent amber fill + a crisp outline, at display resolution.
+
+    Display-only: the ``mask`` is the model's thresholded output (stats are computed from it
+    upstream, unchanged). Here we only (a) lightly smooth the contour to drop stair-step edges,
+    (b) fill each changed region at a low, uniform alpha so buildings stay visible, and (c) trace a
+    brighter 1-2px outline around each region. Returned as a PNG data URL.
+    """
+    binary = Image.fromarray((mask.astype(np.uint8) * 255), mode="L")
+    # smooth the boundary (blur then re-binarize) — removes the stair-stepping of a hard mask
+    smoothed = np.asarray(binary.filter(ImageFilter.GaussianBlur(1.2))) >= 128
+    sm_img = Image.fromarray((smoothed.astype(np.uint8) * 255), mode="L")
+    dil = np.asarray(sm_img.filter(ImageFilter.MaxFilter(3))) >= 128  # ~1px grow
+    ero = np.asarray(sm_img.filter(ImageFilter.MinFilter(3))) >= 128  # ~1px shrink
+    edge = dil & ~ero  # ~2px band straddling the boundary
+
+    h, w = mask.shape
+    rgba = np.zeros((h, w, 4), dtype=np.uint8)
+    for c in range(3):
+        rgba[..., c] = np.where(smoothed, FILL_RGB[c], 0)
+    rgba[..., 3] = np.where(smoothed, FILL_ALPHA, 0).astype(np.uint8)
+    for c in range(3):
+        rgba[edge, c] = EDGE_RGB[c]
+    rgba[edge, 3] = EDGE_ALPHA
+
+    im = Image.fromarray(rgba, mode="RGBA")
+    if im.size != out_size:
+        im = im.resize(out_size, Image.BILINEAR)
+    buf = io.BytesIO()
+    im.save(buf, format="PNG")
+    return "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode("ascii")
+
+
+class BundleRegistry:
+    """Discovers and caches model bundles under ``bundles_dir``."""
+
+    def __init__(self, bundles_dir: str | Path) -> None:
+        self.bundles_dir = Path(bundles_dir)
+        self._bundles: dict[str, Bundle] = {}
+        self.reload()
+
+    def reload(self) -> None:
+        self._bundles.clear()
+        if not self.bundles_dir.exists():
+            return
+        for child in sorted(self.bundles_dir.iterdir()):
+            pre = child / "preprocessing.json"
+            onnx = child / "model.onnx"
+            if not (pre.exists() and onnx.exists()):
+                continue
+            card = child / "metrics_card.md"
+            self._bundles[child.name] = Bundle(
+                model_id=child.name,
+                root=child,
+                preprocessing=json.loads(pre.read_text()),
+                metrics_card=card.read_text() if card.exists() else "",
+            )
+
+    def ids(self) -> list[str]:
+        return list(self._bundles)
+
+    def get(self, model_id: str) -> Bundle:
+        if model_id not in self._bundles:
+            raise KeyError(model_id)
+        return self._bundles[model_id]
+
+    def summaries(self) -> list[dict[str, Any]]:
+        return [b.summary() for b in self._bundles.values()]
+
+    def _infer_tile(
+        self, bundle: Bundle, before_t: Image.Image, after_t: Image.Image
+    ) -> np.ndarray:
+        """One native tile through the model -> per-pixel change probability (model output grid)."""
+        size = bundle.input_size
+        x = np.stack(
+            [
+                _to_input(before_t, size, bundle.mean, bundle.std),
+                _to_input(after_t, size, bundle.mean, bundle.std),
+            ],
+            axis=0,
+        )[None].astype(np.float32)  # (1, 2, 3, S, S)
+        logits = bundle.session().run(["logits"], {"input": x})[0]
+        return 1.0 / (1.0 + np.exp(-logits[0, 0]))  # (S, S)
+
+    def predict(self, model_id: str, before: Image.Image, after: Image.Image) -> dict[str, Any]:
+        """Tile the full scene into native tiles, infer each, stitch the probability map, then
+        threshold + render. This is the bundle's documented preprocessing (``tiling.tile_size``);
+        the model, threshold and per-pixel metric are unchanged — they are just applied per tile,
+        exactly as the evaluation harness does. Feeding the whole scene in one pass would break the
+        model (a 0.5 m/px model at ~4x the trained field of view detects almost nothing)."""
+        bundle = self.get(model_id)
+        before = before.convert("RGB")
+        after = after.convert("RGB")
+        w, h = before.size
+        tile = bundle.tile_size
+
+        prob = np.zeros((h, w), dtype=np.float32)
+        n_tiles = 0
+        t0 = time.perf_counter()
+        for y0 in range(0, h, tile):
+            for x0 in range(0, w, tile):
+                x1, y1 = min(x0 + tile, w), min(y0 + tile, h)
+                box = (x0, y0, x1, y1)
+                p = self._infer_tile(bundle, before.crop(box), after.crop(box))  # (S, S)
+                # resize this tile's probability back to its native footprint, then place it
+                p_tile = np.asarray(
+                    Image.fromarray(p.astype(np.float32), mode="F").resize(
+                        (x1 - x0, y1 - y0), Image.BILINEAR
+                    ),
+                    dtype=np.float32,
+                )
+                prob[y0:y1, x0:x1] = p_tile
+                n_tiles += 1
+        elapsed_ms = (time.perf_counter() - t0) * 1000.0
+
+        thr = bundle.threshold
+        mask = prob >= thr  # stitched native-resolution mask; stats derive from this
+
+        overlay = _overlay_png(mask, (w, h))
+        changed_frac = float(mask.mean())
+        mean_conf_changed = float(prob[mask].mean()) if mask.any() else 0.0
+        return {
+            "overlay_png": overlay,
+            "threshold": thr,
+            "is_placeholder": bundle.is_placeholder,
+            "stats": {
+                "changed_fraction": changed_frac,
+                "changed_percent": round(100.0 * changed_frac, 2),
+                "mean_confidence_changed": round(mean_conf_changed, 4),
+                "mean_confidence_overall": round(float(prob.mean()), 4),
+                "changed_pixels": int(mask.sum()),
+                "total_pixels": int(mask.size),
+            },
+            "elapsed_ms": round(elapsed_ms, 1),
+            "input_size": bundle.input_size,
+            "n_tiles": n_tiles,
+        }

backend/requirements.txt

@@ -0,0 +1,8 @@
+# Curated HF Space runtime (CPU). Pinned loosely; the Space image resolves at build time.
+fastapi>=0.111
+uvicorn[standard]>=0.30
+onnxruntime>=1.18
+numpy>=1.26,<2.1
+pillow>=10.0
+pyyaml>=6.0
+huggingface-hub>=0.24

frontend/index.html

@@ -0,0 +1,23 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Satellite Change Detection — curated demo</title>
+    <meta
+      name="description"
+      content="Before/after satellite change detection with ONNX models (LEVIR-CD Track A)."
+    />
+    <meta name="theme-color" content="#080b10" />
+    <style>
+      html,
+      body {
+        background: #080b10;
+      }
+    </style>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.tsx"></script>
+  </body>
+</html>

frontend/package-lock.json

@@ -0,0 +1,2130 @@
+{
+  "name": "sat-change-detection-frontend",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "sat-change-detection-frontend",
+      "version": "1.0.0",
+      "dependencies": {
+        "@fontsource/ibm-plex-mono": "^5.2.7",
+        "@fontsource/ibm-plex-sans": "^5.2.8",
+        "maplibre-gl": "^4.7.1",
+        "react": "^18.3.1",
+        "react-dom": "^18.3.1"
+      },
+      "devDependencies": {
+        "@types/react": "^18.3.12",
+        "@types/react-dom": "^18.3.1",
+        "@vitejs/plugin-react": "^4.3.4",
+        "typescript": "^5.6.3",
+        "vite": "^5.4.11"
+      }
+    },
+    "node_modules/@babel/code-frame": {
+      "version": "7.29.7",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.7.tgz",
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+      "license": "MIT",
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+        "js-tokens": "^4.0.0",
+        "picocolors": "^1.1.1"
+      },
+      "engines": {
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+    },
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+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "esbuild": "^0.21.3",
+        "postcss": "^8.4.43",
+        "rollup": "^4.20.0"
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+        "sugarss": "*",
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+        },
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+          "optional": true
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+    "node_modules/vt-pbf": {
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+      "license": "MIT",
+      "dependencies": {
+        "@mapbox/point-geometry": "0.1.0",
+        "@mapbox/vector-tile": "^1.3.1",
+        "pbf": "^3.2.1"
+      }
+    },
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+      "license": "ISC",
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+        "isexe": "^3.1.1"
+      },
+      "bin": {
+        "node-which": "bin/which.js"
+      },
+      "engines": {
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+    "node_modules/yallist": {
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+      "dev": true,
+      "license": "ISC"
+    }
+  }
+}

frontend/package.json

@@ -0,0 +1,25 @@
+{
+  "name": "sat-change-detection-frontend",
+  "private": true,
+  "version": "1.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "tsc --noEmit && vite build",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "@fontsource/ibm-plex-mono": "^5.2.7",
+    "@fontsource/ibm-plex-sans": "^5.2.8",
+    "maplibre-gl": "^4.7.1",
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1"
+  },
+  "devDependencies": {
+    "@types/react": "^18.3.12",
+    "@types/react-dom": "^18.3.1",
+    "@vitejs/plugin-react": "^4.3.4",
+    "typescript": "^5.6.3",
+    "vite": "^5.4.11"
+  }
+}

frontend/src/App.tsx

@@ -0,0 +1,335 @@
+import { useEffect, useState } from "react";
+import { api } from "./api";
+import CompareView from "./components/CompareView";
+import ModelCard from "./components/ModelCard";
+import type { CuratedPair, ModelSummary, PredictResult } from "./types";
+
+type Tab = "curated" | "card";
+
+// friendly labels for the served bundle ids (falls back to the raw id)
+const MODEL_LABELS: Record<string, string> = {
+  levircd_dinov2: "DINOv2 + LoRA",
+  levircd_segformer: "SegFormer MiT-b2",
+};
+const modelLabel = (id: string) => MODEL_LABELS[id] ?? id;
+
+// short scene tag from a source string like "LEVIR-CD test_10" -> "T10"
+const sceneTag = (source: string) => {
+  const m = source.match(/(\d+)\s*$/);
+  return m ? `T${m[1]}` : source.slice(0, 3).toUpperCase();
+};
+// strip the "(test N)" suffix from a pair title for a tighter label
+const sceneTitle = (title: string) => title.replace(/\s*\(test[^)]*\)\s*$/i, "");
+// annotated ground-truth change % from the description, if present
+const annotatedPct = (desc: string): number | null => {
+  const m = desc.match(/([\d.]+)\s*%/);
+  return m ? parseFloat(m[1]) : null;
+};
+// full-scene tile+stitch inference is seconds on CPU (baked once, then served from cache) — format
+// the real model cost sensibly instead of a raw millisecond count.
+const fmtDuration = (ms: number): { v: string; u: string } =>
+  ms >= 1000 ? { v: (ms / 1000).toFixed(ms >= 10000 ? 0 : 1), u: "s" } : { v: String(Math.round(ms)), u: "ms" };
+const fmtDurStr = (ms: number): string => {
+  const d = fmtDuration(ms);
+  return `${d.v} ${d.u}`;
+};
+
+export default function App() {
+  const [tab, setTab] = useState<Tab>("curated");
+  const [models, setModels] = useState<ModelSummary[]>([]);
+  const [pairs, setPairs] = useState<CuratedPair[]>([]);
+  const [modelId, setModelId] = useState<string>("");
+  const [pairId, setPairId] = useState<string>("");
+  const [result, setResult] = useState<PredictResult | null>(null);
+  const [busy, setBusy] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+
+  const [showOverlay, setShowOverlay] = useState(true);
+  const [opacity, setOpacity] = useState(0.75);
+
+  useEffect(() => {
+    Promise.all([api.models(), api.curated()])
+      .then(([m, p]) => {
+        setModels(m);
+        setPairs(p);
+        if (m.length) setModelId(m[0].id);
+        if (p.length) setPairId(p[0].id);
+      })
+      .catch((e) => setError(String(e)));
+  }, []);
+
+  useEffect(() => {
+    if (!modelId || !pairId) return;
+    let cancelled = false;
+    setBusy(true);
+    setError(null);
+    setResult(null); // clear stale overlay/stats so the new imagery never shows the old mask
+    api
+      .predict(pairId, modelId)
+      .then((r) => {
+        if (!cancelled) setResult(r);
+      })
+      .catch((e) => !cancelled && setError(String(e)))
+      .finally(() => !cancelled && setBusy(false));
+    return () => {
+      cancelled = true;
+    };
+  }, [modelId, pairId]);
+
+  const model = models.find((m) => m.id === modelId);
+  const pair = pairs.find((p) => p.id === pairId);
+  const placeholder = result?.is_placeholder ?? model?.is_placeholder ?? false;
+  const healthy = models.length > 0 && !error;
+  const activeThreshold = result?.threshold ?? model?.threshold;
+
+  return (
+    <div className="app">
+      <header className="topbar">
+        <div className="brand">
+          <svg className="mark" viewBox="0 0 32 32" fill="none" aria-hidden="true">
+            <circle cx="16" cy="16" r="11" stroke="currentColor" strokeWidth="1.3" opacity=".5" />
+            <circle cx="16" cy="16" r="4.4" stroke="currentColor" strokeWidth="1.3" />
+            <path d="M16 1v7M16 24v7M1 16h7M24 16h7" stroke="currentColor" strokeWidth="1.3" />
+            <rect x="13.4" y="13.4" width="5.2" height="5.2" fill="currentColor" />
+          </svg>
+          <div>
+            <h1>Satellite Change Detection</h1>
+            <div className="sub">
+              TRACK&nbsp;A · AERIAL 0.5&nbsp;M/PX · <b>ONNX · CPU</b>
+            </div>
+          </div>
+        </div>
+        <nav className="nav">
+          <button className={`tab ${tab === "curated" ? "on" : ""}`} onClick={() => setTab("curated")}>
+            Curated
+          </button>
+          <button className={`tab ${tab === "card" ? "on" : ""}`} onClick={() => setTab("card")}>
+            Model card
+          </button>
+          <div className="status">
+            <span className={`dot ${healthy ? "" : "down"}`} />
+            {healthy ? "RUNTIME NOMINAL" : "RUNTIME OFFLINE"}
+          </div>
+        </nav>
+      </header>
+
+      {placeholder && tab === "curated" && (
+        <div className="banner">
+          <span className="bicon">⚠</span> Serving <strong>placeholder (random-init) weights</strong> — the
+          pipeline is real, the predictions are not. Swap in the trained bundle to ship.
+        </div>
+      )}
+      {error && (
+        <div className="banner err">
+          <span className="bicon">✕</span> {error}
+        </div>
+      )}
+
+      {tab === "card" ? (
+        <ModelCard />
+      ) : (
+        <div className="body">
+          <aside className="rail">
+            <section className="sec">
+              <h2>
+                <span className="idx">01</span> Scene pair <span className="rule" />
+              </h2>
+              {pairs.map((p) => {
+                const pct = annotatedPct(p.description);
+                return (
+                  <button
+                    key={p.id}
+                    className={`scene ${p.id === pairId ? "on" : ""}`}
+                    onClick={() => setPairId(p.id)}
+                    title={p.description}
+                  >
+                    <span className="tag">{sceneTag(p.source || p.id)}</span>
+                    <span className="meta">
+                      <span className="t">{sceneTitle(p.title)}</span>
+                      <span className="bar">
+                        <i style={{ width: `${pct ?? 2}%` }} />
+                      </span>
+                    </span>
+                    <span className="pct">{pct != null ? `${pct}%` : "ctrl"}</span>
+                  </button>
+                );
+              })}
+              <p className="scene-note">
+                Real LEVIR-CD test crops · the bar is <b>annotated</b> change (ground truth) · 0.5 m/px
+                aerial.
+              </p>
+            </section>
+
+            <section className="sec">
+              <h2>
+                <span className="idx">02</span> Model <span className="rule" />
+              </h2>
+              <div className="select">
+                <select value={modelId} onChange={(e) => setModelId(e.target.value)}>
+                  {models.map((m) => (
+                    <option key={m.id} value={m.id}>
+                      {modelLabel(m.id)}
+                      {m.is_placeholder ? " (placeholder)" : ""}
+                    </option>
+                  ))}
+                </select>
+              </div>
+              {model && (
+                <div className="kv">
+                  <span className="chip">
+                    grid <b>{model.input_size}²</b>
+                  </span>
+                  <span className="chip">{model.fixed_grid ? "fixed grid" : "dynamic H/W"}</span>
+                  <span className="chip">
+                    thr <b>{model.threshold.toFixed(3)}</b>
+                  </span>
+                </div>
+              )}
+            </section>
+
+            <section className="sec">
+              <h2>
+                <span className="idx">03</span> Change overlay <span className="rule" />
+              </h2>
+              <div className="toggle">
+                <span className="lbl">Show detected change</span>
+                <div
+                  className={`sw ${showOverlay ? "on" : ""}`}
+                  role="switch"
+                  aria-checked={showOverlay}
+                  onClick={() => setShowOverlay((v) => !v)}
+                />
+              </div>
+              <div className="slabel">
+                Opacity <b>{Math.round(opacity * 100)}%</b>
+              </div>
+              <input
+                type="range"
+                min={0}
+                max={1}
+                step={0.01}
+                value={opacity}
+                style={{ ["--fill" as string]: `${opacity * 100}%` }}
+                onChange={(e) => setOpacity(Number(e.target.value))}
+              />
+              <div className="legend">
+                <span className="sq" /> Detected building change
+                {activeThreshold != null && <> · thr {activeThreshold.toFixed(3)}</>}
+              </div>
+            </section>
+
+            <section className="sec">
+              <h2>
+                <span className="idx">04</span> Analysis <span className="rule" />
+              </h2>
+              {result ? (
+                <>
+                  <div className="readout">
+                    <div className="row">
+                      <span className="k">Area changed</span>
+                      <span className="v">
+                        {result.stats.changed_percent}
+                        <small>%</small>
+                      </span>
+                    </div>
+                    <div className="row">
+                      <span className="k">Mean conf · changed</span>
+                      <span className="v">{result.stats.mean_confidence_changed.toFixed(3)}</span>
+                    </div>
+                    <div className="row">
+                      <span className="k">Changed pixels</span>
+                      <span className="v">{result.stats.changed_pixels.toLocaleString()}</span>
+                    </div>
+                    <div className="row">
+                      <span className="k">Full-scene inference · CPU</span>
+                      <span className="v">
+                        {fmtDuration(result.elapsed_ms).v}
+                        <small>{fmtDuration(result.elapsed_ms).u}</small>
+                      </span>
+                    </div>
+                  </div>
+                  <div className="gauge">
+                    <div className="track">
+                      <i style={{ width: `${Math.min(100, result.stats.changed_percent)}%` }} />
+                    </div>
+                    <div className="cap">
+                      <span>0%</span>
+                      <span>AREA CHANGED</span>
+                      <span>100%</span>
+                    </div>
+                  </div>
+                  <p className="readout-caption">
+                    {result.n_tiles ?? 16}× 256&nbsp;px tiles, stitched · precomputed, served from
+                    cache
+                  </p>
+                </>
+              ) : busy ? (
+                <div className="readout">
+                  {["Area changed", "Mean conf · changed", "Changed pixels", "Inference · CPU"].map(
+                    (k) => (
+                      <div className="row" key={k}>
+                        <span className="k">{k}</span>
+                        <span className="v skeleton" />
+                      </div>
+                    ),
+                  )}
+                </div>
+              ) : (
+                <p className="readout-empty">Select a scene to run inference.</p>
+              )}
+            </section>
+          </aside>
+
+          <main className="stage">
+            <div className="viewer">
+              {pairId && modelId ? (
+                <CompareView
+                  beforeUrl={api.imageUrl(pairId, "before")}
+                  afterUrl={api.imageUrl(pairId, "after")}
+                  overlayUrl={result?.overlay_png ?? null}
+                  overlayOpacity={opacity}
+                  showOverlay={showOverlay}
+                  loading={busy && !result}
+                  legendLabel={modelLabel(modelId)}
+                />
+              ) : (
+                <div className="empty">Initializing…</div>
+              )}
+            </div>
+
+            <footer className="telemetry">
+              <div className="cell accent">
+                <span className="k">MODEL</span> <b>{modelId || "—"}</b>
+              </div>
+              <div className="cell">
+                <span className="k">INPUT</span>{" "}
+                <b>{model ? `${model.input_size}×${model.input_size}` : "—"}</b>
+              </div>
+              <div className="cell hide-narrow">
+                <span className="k">GRID</span> <b>{model?.fixed_grid ? "FIXED" : "DYN H/W"}</b>
+              </div>
+              <div className="cell">
+                <span className="k">THR</span>{" "}
+                <b>{activeThreshold != null ? activeThreshold.toFixed(3) : "—"}</b>
+              </div>
+              <div className="cell">
+                <span className="k">INFER</span>{" "}
+                <b>{result ? fmtDurStr(result.elapsed_ms) : busy ? "…" : "—"}</b>
+              </div>
+              <div className="spacer" />
+              <div className="cell hide-narrow">
+                <span className="k">BANDS</span> <b>{model ? model.band_order.join("") : "—"}</b>
+              </div>
+              <div className="cell hide-narrow">
+                <span className="k">GSD</span> <b>0.5 m/px</b>
+              </div>
+              <div className="cell">
+                <span className="k">SRC</span> <b>{pair?.source || pair?.id || "—"}</b>
+              </div>
+            </footer>
+          </main>
+        </div>
+      )}
+    </div>
+  );
+}

frontend/src/api.ts

@@ -0,0 +1,31 @@
+import type { CuratedPair, ModelSummary, PredictResult } from "./types";
+
+// Same-origin in production (FastAPI serves this build); vite dev proxies /api to :7860.
+const BASE = "";
+
+async function getJSON<T>(path: string): Promise<T> {
+  const res = await fetch(`${BASE}${path}`);
+  if (!res.ok) throw new Error(`${path} -> ${res.status}`);
+  return (await res.json()) as T;
+}
+
+export const api = {
+  models: () => getJSON<ModelSummary[]>("/api/models"),
+  curated: () => getJSON<CuratedPair[]>("/api/curated"),
+  card: async (modelId: string): Promise<string> => {
+    const res = await fetch(`${BASE}/api/models/${modelId}/card`);
+    if (!res.ok) throw new Error(`card ${modelId} -> ${res.status}`);
+    return res.text();
+  },
+  predict: async (pairId: string, modelId: string): Promise<PredictResult> => {
+    const res = await fetch(`${BASE}/api/predict`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ pair_id: pairId, model_id: modelId }),
+    });
+    if (!res.ok) throw new Error(`predict -> ${res.status}`);
+    return (await res.json()) as PredictResult;
+  },
+  imageUrl: (pairId: string, which: "before" | "after") =>
+    `${BASE}/api/curated/${pairId}/${which}.png`,
+};

frontend/src/components/CompareView.tsx

@@ -0,0 +1,245 @@
+import maplibregl from "maplibre-gl";
+import { useEffect, useRef, useState } from "react";
+
+// A fixed geographic quad to place the (non-geo) image tiles into MapLibre. Square so 256² tiles
+// keep their aspect ratio. TL, TR, BR, BL as [lng, lat].
+const COORDS: [
+  [number, number],
+  [number, number],
+  [number, number],
+  [number, number],
+] = [
+  [-0.02, 0.02],
+  [0.02, 0.02],
+  [0.02, -0.02],
+  [-0.02, -0.02],
+];
+const BOUNDS: [[number, number], [number, number]] = [
+  [-0.02, -0.02],
+  [0.02, 0.02],
+];
+
+interface Props {
+  beforeUrl: string;
+  afterUrl: string;
+  overlayUrl: string | null;
+  overlayOpacity: number;
+  showOverlay: boolean;
+  loading?: boolean;
+  legendLabel?: string;
+}
+
+// Cover-fit: fill the whole stage edge-to-edge (no letterbox voids), cropping any overflow. The
+// square image quad is grown until it covers the viewport's LONGER axis; overflow is clipped by
+// .compare-wrap, pan/zoom reaches the cropped areas, and the overlay tracks via map.project().
+// On an ultra-wide stage a 1024px tile can't fill without upscaling — MapLibre's linear resampling
+// keeps that smooth (never blocky), and zooming recovers native pixels.
+function fitCover(map: maplibregl.Map): void {
+  const cam = map.cameraForBounds(BOUNDS, { padding: 0 }); // "contain" zoom (fits the shorter axis)
+  if (!cam || cam.zoom == null) return;
+  const c = map.getContainer();
+  const vw = c.clientWidth;
+  const vh = c.clientHeight;
+  if (!vw || !vh) return;
+  // bump the contain zoom by log2(longer/shorter) so the square also covers the longer axis
+  const coverBump = Math.log2(Math.max(vw, vh) / Math.min(vw, vh));
+  map.jumpTo({ center: cam.center, zoom: cam.zoom + coverBump });
+}
+
+function makeMap(container: HTMLDivElement): maplibregl.Map {
+  const map = new maplibregl.Map({
+    container,
+    style: { version: 8, sources: {}, layers: [] },
+    center: [0, 0],
+    zoom: 12,
+    attributionControl: false,
+    dragRotate: false,
+    pitchWithRotate: false,
+    renderWorldCopies: false,
+    // pan + zoom are on by default (dragPan / scrollZoom / dblclick / touch); rotate/pitch stay off
+  });
+  map.on("load", () => fitCover(map));
+  return map;
+}
+
+// Base imagery as a MapLibre raster image layer. (The change overlay is *not* a MapLibre layer — a
+// second stacked image-source raster proved unreliable to paint; it is an aligned HTML <img>
+// positioned over the map via map.project(), which always renders and gives smooth CSS opacity.)
+function setImage(map: maplibregl.Map, url: string): void {
+  const src = map.getSource("img") as maplibregl.ImageSource | undefined;
+  if (src) {
+    src.updateImage({ url, coordinates: COORDS });
+    return;
+  }
+  map.addSource("img", { type: "image", url, coordinates: COORDS });
+  map.addLayer({
+    id: "img",
+    type: "raster",
+    source: "img",
+    // explicit linear resampling: smooth photographic interpolation, never nearest-neighbor blocks
+    paint: { "raster-opacity": 1, "raster-fade-duration": 0, "raster-resampling": "linear" },
+  });
+}
+
+export default function CompareView({
+  beforeUrl,
+  afterUrl,
+  overlayUrl,
+  overlayOpacity,
+  showOverlay,
+  loading = false,
+  legendLabel,
+}: Props) {
+  const beforeDiv = useRef<HTMLDivElement>(null);
+  const afterDiv = useRef<HTMLDivElement>(null);
+  const beforeMap = useRef<maplibregl.Map | null>(null);
+  const afterMap = useRef<maplibregl.Map | null>(null);
+  const overlayImg = useRef<HTMLImageElement>(null);
+  const syncing = useRef(false);
+  const [ready, setReady] = useState(false);
+  const [split, setSplit] = useState(50); // divider position, %
+  const dragging = useRef(false);
+  const wrap = useRef<HTMLDivElement>(null);
+
+  // Position the overlay <img> to exactly cover the image quad, in the before map's screen space
+  // (the two maps are view-synced, so this also lines up under the clipped after map).
+  const alignOverlay = () => {
+    const map = beforeMap.current;
+    const el = overlayImg.current;
+    if (!map || !el) return;
+    const tl = map.project(COORDS[0]);
+    const br = map.project(COORDS[2]);
+    el.style.left = `${tl.x}px`;
+    el.style.top = `${tl.y}px`;
+    el.style.width = `${br.x - tl.x}px`;
+    el.style.height = `${br.y - tl.y}px`;
+  };
+
+  // create the two maps once, keep their views in sync, and keep the overlay aligned
+  useEffect(() => {
+    if (!beforeDiv.current || !afterDiv.current || beforeMap.current) return;
+    const bm = makeMap(beforeDiv.current);
+    const am = makeMap(afterDiv.current);
+    beforeMap.current = bm;
+    afterMap.current = am;
+    bm.on("error", (e) => console.warn("map error (before):", e.error?.message ?? e));
+    am.on("error", (e) => console.warn("map error (after):", e.error?.message ?? e));
+
+    const sync = (src: maplibregl.Map, dst: maplibregl.Map) => () => {
+      if (syncing.current) return;
+      syncing.current = true;
+      dst.jumpTo({
+        center: src.getCenter(),
+        zoom: src.getZoom(),
+        bearing: src.getBearing(),
+        pitch: src.getPitch(),
+      });
+      syncing.current = false;
+      alignOverlay();
+    };
+    bm.on("move", sync(bm, am));
+    am.on("move", sync(am, bm));
+    // on container resize, re-cover the stage from the before map; its jumpTo syncs the after map
+    bm.on("resize", () => {
+      fitCover(bm);
+      alignOverlay();
+    });
+    bm.on("idle", alignOverlay);
+
+    let loaded = 0;
+    const onLoad = () => {
+      loaded += 1;
+      if (loaded === 2) {
+        setReady(true);
+        alignOverlay();
+      }
+    };
+    bm.on("load", onLoad);
+    am.on("load", onLoad);
+
+    return () => {
+      bm.remove();
+      am.remove();
+      beforeMap.current = null;
+      afterMap.current = null;
+      setReady(false);
+    };
+  }, []);
+
+  // update imagery when the pair changes
+  useEffect(() => {
+    if (!ready || !beforeMap.current || !afterMap.current) return;
+    setImage(beforeMap.current, beforeUrl);
+    setImage(afterMap.current, afterUrl);
+    alignOverlay();
+  }, [ready, beforeUrl, afterUrl]);
+
+  // keep the overlay aligned whenever it (re)appears
+  useEffect(() => {
+    alignOverlay();
+  }, [ready, overlayUrl]);
+
+  // clip the top (after) map to the right of the divider
+  useEffect(() => {
+    if (afterDiv.current) afterDiv.current.style.clipPath = `inset(0 0 0 ${split}%)`;
+  }, [split]);
+
+  const onPointerMove = (e: React.PointerEvent) => {
+    if (!dragging.current || !wrap.current) return;
+    const rect = wrap.current.getBoundingClientRect();
+    const pct = ((e.clientX - rect.left) / rect.width) * 100;
+    setSplit(Math.max(4, Math.min(96, pct)));
+  };
+
+  return (
+    <div
+      className="compare-wrap"
+      ref={wrap}
+      onPointerMove={onPointerMove}
+      onPointerUp={() => (dragging.current = false)}
+      onPointerLeave={() => (dragging.current = false)}
+    >
+      <div ref={beforeDiv} className="map-layer" />
+      <div ref={afterDiv} className="map-layer map-top" />
+      <img
+        ref={overlayImg}
+        className="change-overlay"
+        alt=""
+        src={overlayUrl ?? ""}
+        style={{ opacity: showOverlay && overlayUrl ? overlayOpacity : 0 }}
+      />
+
+      <div className="frame" aria-hidden="true">
+        <span className="tl" />
+        <span className="tr" />
+        <span className="bl" />
+        <span className="br" />
+      </div>
+
+      <div className="hud chip before">BEFORE</div>
+      <div className="hud chip after">AFTER</div>
+      <div className="hud chip legendchip">
+        <span className="sq" /> CHANGE MASK{legendLabel ? ` · ${legendLabel}` : ""}
+      </div>
+      <div className="hud chip gsd">SOURCE GSD 0.5&nbsp;m/px</div>
+
+      <div
+        className="swipe-divider"
+        style={{ left: `${split}%` }}
+        onPointerDown={(e) => {
+          dragging.current = true;
+          (e.target as HTMLElement).setPointerCapture?.(e.pointerId);
+        }}
+      >
+        <div className="swipe-handle">⇄</div>
+      </div>
+
+      {loading && (
+        <div className="scanning">
+          <div className="beam" />
+          <span className="tagword">Analyzing</span>
+        </div>
+      )}
+    </div>
+  );
+}

frontend/src/components/ModelCard.tsx

@@ -0,0 +1,113 @@
+interface Row {
+  model: string;
+  params: string;
+  f1: string;
+  iou: string;
+  ap: string;
+  note?: string;
+}
+
+// Real LEVIR-CD test results (threshold selected on val, applied to test) — from the committed
+// comparison in docs/results/. This is the genuine project story; the served ONNX may be a
+// placeholder until the trained bundles are staged.
+const ROWS: Row[] = [
+  { model: "FC-Siam-diff (baseline)", params: "0.83M", f1: "0.886", iou: "0.796", ap: "0.932" },
+  {
+    model: "Siamese-SegFormer / MiT-b2 (diff)",
+    params: "24.72M",
+    f1: "0.911",
+    iou: "0.836",
+    ap: "0.943",
+    note: "ImageNet-pretrained strong model",
+  },
+  {
+    model: "DINOv2-base frozen linear-probe",
+    params: "1.64M",
+    f1: "0.889",
+    iou: "0.800",
+    ap: "0.924",
+    note: "frozen FM features only",
+  },
+  {
+    model: "DINOv2-base + LoRA",
+    params: "2.82M",
+    f1: "0.913",
+    iou: "0.839",
+    ap: "0.946",
+    note: "foundation-model tier — headline",
+  },
+];
+
+export default function ModelCard() {
+  return (
+    <div className="card-page">
+      <div className="card-inner">
+        <p className="card-eyebrow">Model card · Track A · high-res aerial</p>
+        <h2>Siamese change detection on LEVIR-CD</h2>
+        <p className="card-lead">
+          Weight-shared Siamese change-detection models on 0.5&nbsp;m RGB aerial imagery. Two dates of
+          the same place go in; a per-pixel building-change map comes out. Three tiers are compared on
+          the identical LEVIR-CD test split through one evaluation harness.
+        </p>
+
+        <table className="metrics-table">
+          <thead>
+            <tr>
+              <th>Model</th>
+              <th>Trainable</th>
+              <th>F1</th>
+              <th>IoU</th>
+              <th>AP</th>
+            </tr>
+          </thead>
+          <tbody>
+            {ROWS.map((r) => (
+              <tr key={r.model} className={r.note?.includes("headline") ? "row-headline" : ""}>
+                <td>
+                  {r.model}
+                  {r.note && <span className="row-note">{r.note}</span>}
+                </td>
+                <td className="num">{r.params}</td>
+                <td className="num">{r.f1}</td>
+                <td className="num">{r.iou}</td>
+                <td className="num">{r.ap}</td>
+              </tr>
+            ))}
+          </tbody>
+        </table>
+
+        <h3>The defensible claim — parameter efficiency</h3>
+        <p className="muted">
+          DINOv2-base + LoRA reaches F1&nbsp;0.913 with only <strong>2.82M</strong> trainable
+          parameters versus the SegFormer strong model&apos;s <strong>24.72M</strong> at F1&nbsp;0.911
+          — a statistical tie on accuracy (within noise) at ~9× fewer trainable params. The frozen
+          linear-probe (1.64M, decoder only) already ≈ the baseline, so the self-supervised
+          representation carries most of the change signal and LoRA supplies the adaptation lift.
+        </p>
+
+        <h3>Honest limitations</h3>
+        <ul className="muted">
+          <li>
+            Per-scene F1 has high variance (mean ≈ 0.77, std ≈ 0.31, min 0.00). The foundation model
+            lifts the mean but does <em>not</em> fix the hardest small/subtle-change tiles.
+          </li>
+          <li>
+            Overall pixel accuracy is deliberately not reported: change is a tiny pixel fraction, so
+            &quot;predict no change&quot; scores ~99% and is meaningless. Metrics are change-class only.
+          </li>
+          <li>Trained weights inherit LEVIR-CD research/non-commercial terms — showcase use only.</li>
+        </ul>
+
+        <h3>Domain gap — why the live mode is a different model</h3>
+        <div className="callout">
+          <p className="muted">
+            These models are trained on 0.5&nbsp;m aerial imagery and do <strong>not</strong> transfer
+            to 10&nbsp;m Sentinel-2. The live-AOI mode (a later milestone) uses a Sentinel-2-native
+            model so it produces meaningful output on satellite scenes — the domain split is a design
+            decision, not an afterthought.
+          </p>
+        </div>
+      </div>
+    </div>
+  );
+}

frontend/src/main.tsx

@@ -0,0 +1,20 @@
+import React from "react";
+import ReactDOM from "react-dom/client";
+import App from "./App";
+// Self-hosted fonts (vendored by @fontsource, bundled by Vite) — no external CDN at runtime, so
+// the Docker Space renders identically offline. IBM Plex Sans (UI) + IBM Plex Mono (data/labels).
+import "@fontsource/ibm-plex-sans/300.css";
+import "@fontsource/ibm-plex-sans/400.css";
+import "@fontsource/ibm-plex-sans/500.css";
+import "@fontsource/ibm-plex-sans/600.css";
+import "@fontsource/ibm-plex-mono/400.css";
+import "@fontsource/ibm-plex-mono/500.css";
+import "@fontsource/ibm-plex-mono/600.css";
+import "maplibre-gl/dist/maplibre-gl.css";
+import "./styles.css";
+
+ReactDOM.createRoot(document.getElementById("root")!).render(
+  <React.StrictMode>
+    <App />
+  </React.StrictMode>,
+);

frontend/src/styles.css

@@ -0,0 +1,1016 @@
+/* ============================================================================
+   Satellite Change Detection — "Observatory Console"
+   Cool, instrument-grade GEOINT theme. Imagery is the hero; chrome recedes.
+   Type: IBM Plex Sans (UI) + IBM Plex Mono (labels, coordinates, numbers).
+   ========================================================================== */
+:root {
+  --bg: #080b10;
+  --bg-2: #0b0f16;
+  --panel: #0c1119;
+  --panel-2: #0f151f;
+  --line: #182231;
+  --line-2: #101823;
+  --text: #dfe7f0;
+  --text-dim: #8494a6;
+  --text-faint: #54637a;
+  --accent: #3fd0e6; /* controlled instrument cyan */
+  --accent-deep: #0f3a45;
+  --accent-glow: rgba(63, 208, 230, 0.15);
+  --signal: #ffb454; /* amber change signal / legend */
+  --ok: #5fd39a;
+  --warn: #ffcf6b;
+  --err: #ff6b6b;
+  --radius: 8px;
+  --mono: "IBM Plex Mono", ui-monospace, SFMono-Regular, Menlo, monospace;
+  --sans: "IBM Plex Sans", -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
+}
+
+* {
+  box-sizing: border-box;
+}
+html,
+body,
+#root {
+  height: 100%;
+  margin: 0;
+}
+body {
+  background: var(--bg);
+  color: var(--text);
+  font-family: var(--sans);
+  font-size: 14px;
+  -webkit-font-smoothing: antialiased;
+  text-rendering: optimizeLegibility;
+  overflow: hidden;
+}
+::selection {
+  background: var(--accent-deep);
+  color: #fff;
+}
+.app {
+  display: flex;
+  flex-direction: column;
+  height: 100%;
+}
+
+/* ---------- top bar ---------- */
+.topbar {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  height: 54px;
+  flex: none;
+  padding: 0 18px;
+  border-bottom: 1px solid var(--line);
+  background: linear-gradient(180deg, #0c1119, #080b10);
+}
+.brand {
+  display: flex;
+  align-items: center;
+  gap: 13px;
+}
+.mark {
+  width: 30px;
+  height: 30px;
+  flex: none;
+  color: var(--accent);
+}
+.brand h1 {
+  font-size: 13px;
+  font-weight: 600;
+  margin: 0;
+  letter-spacing: 0.14em;
+  text-transform: uppercase;
+  color: var(--text);
+}
+.brand .sub {
+  font-family: var(--mono);
+  font-size: 10.5px;
+  letter-spacing: 0.08em;
+  color: var(--text-faint);
+  margin-top: 3px;
+}
+.brand .sub b {
+  color: var(--accent);
+  font-weight: 500;
+}
+.nav {
+  display: flex;
+  align-items: center;
+  gap: 4px;
+}
+.tab {
+  font-family: var(--mono);
+  font-size: 11px;
+  letter-spacing: 0.06em;
+  text-transform: uppercase;
+  color: var(--text-dim);
+  background: transparent;
+  border: 1px solid transparent;
+  padding: 8px 13px;
+  border-radius: 6px;
+  cursor: pointer;
+  transition: color 0.15s, background 0.15s;
+}
+.tab:hover {
+  color: var(--text);
+}
+.tab.on {
+  color: var(--bg);
+  background: var(--accent);
+  border-color: var(--accent);
+  font-weight: 600;
+}
+.status {
+  display: flex;
+  align-items: center;
+  gap: 7px;
+  margin-left: 10px;
+  padding-left: 14px;
+  border-left: 1px solid var(--line);
+  font-family: var(--mono);
+  font-size: 10.5px;
+  letter-spacing: 0.06em;
+  color: var(--text-dim);
+}
+.dot {
+  width: 7px;
+  height: 7px;
+  border-radius: 50%;
+  background: var(--ok);
+  box-shadow: 0 0 8px var(--ok);
+  animation: pulse 2.6s ease-in-out infinite;
+}
+.dot.down {
+  background: var(--err);
+  box-shadow: 0 0 8px var(--err);
+}
+@keyframes pulse {
+  0%,
+  100% {
+    opacity: 1;
+  }
+  50% {
+    opacity: 0.4;
+  }
+}
+
+/* ---------- banners ---------- */
+.banner {
+  flex: none;
+  padding: 9px 20px;
+  font-size: 12.5px;
+  font-family: var(--mono);
+  letter-spacing: 0.01em;
+  display: flex;
+  align-items: center;
+  gap: 9px;
+  background: #2a2410;
+  color: var(--warn);
+  border-bottom: 1px solid #4a3d16;
+}
+.banner strong {
+  color: #fff;
+}
+.banner.err {
+  background: #2a1414;
+  color: var(--err);
+  border-color: #4a1c1c;
+}
+.banner .bicon {
+  font-size: 13px;
+}
+
+/* ---------- body / rail ---------- */
+.body {
+  flex: 1;
+  display: flex;
+  min-height: 0;
+}
+.rail {
+  width: 326px;
+  flex: none;
+  border-right: 1px solid var(--line);
+  background: var(--panel);
+  overflow-y: auto;
+}
+.rail::-webkit-scrollbar {
+  width: 8px;
+}
+.rail::-webkit-scrollbar-thumb {
+  background: #16202e;
+  border-radius: 4px;
+}
+.sec {
+  padding: 16px 18px;
+  border-bottom: 1px solid var(--line-2);
+  animation: rise 0.5s cubic-bezier(0.2, 0.7, 0.2, 1) both;
+}
+.sec:nth-child(1) {
+  animation-delay: 0.02s;
+}
+.sec:nth-child(2) {
+  animation-delay: 0.07s;
+}
+.sec:nth-child(3) {
+  animation-delay: 0.12s;
+}
+.sec:nth-child(4) {
+  animation-delay: 0.17s;
+}
+@keyframes rise {
+  from {
+    opacity: 0;
+    transform: translateY(7px);
+  }
+  to {
+    opacity: 1;
+    transform: none;
+  }
+}
+.sec h2 {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  font-family: var(--mono);
+  font-size: 10.5px;
+  font-weight: 500;
+  letter-spacing: 0.18em;
+  text-transform: uppercase;
+  color: var(--text-faint);
+  margin: 0 0 13px;
+}
+.sec h2 .idx {
+  color: var(--accent);
+  opacity: 0.85;
+}
+.sec h2 .rule {
+  flex: 1;
+  height: 1px;
+  background: var(--line);
+}
+
+/* scene list */
+.scene {
+  display: flex;
+  align-items: center;
+  gap: 11px;
+  width: 100%;
+  text-align: left;
+  padding: 9px 10px;
+  margin-bottom: 5px;
+  border-radius: 7px;
+  cursor: pointer;
+  background: var(--panel-2);
+  border: 1px solid var(--line-2);
+  color: var(--text);
+  transition: border-color 0.15s, background 0.15s;
+}
+.scene:hover {
+  border-color: #22303f;
+}
+.scene.on {
+  border-color: var(--accent);
+  background: linear-gradient(90deg, var(--accent-glow), transparent 72%);
+}
+.scene .tag {
+  font-family: var(--mono);
+  font-size: 10px;
+  letter-spacing: 0.04em;
+  color: var(--text-faint);
+  border: 1px solid var(--line);
+  border-radius: 4px;
+  padding: 3px 5px;
+  flex: none;
+  min-width: 34px;
+  text-align: center;
+}
+.scene.on .tag {
+  color: var(--accent);
+  border-color: var(--accent-deep);
+}
+.scene .meta {
+  flex: 1;
+  min-width: 0;
+}
+.scene .meta .t {
+  font-size: 12.5px;
+  font-weight: 500;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+.scene .meta .bar {
+  height: 3px;
+  border-radius: 2px;
+  background: #13202c;
+  margin-top: 6px;
+  overflow: hidden;
+}
+.scene .meta .bar > i {
+  display: block;
+  height: 100%;
+  background: linear-gradient(90deg, var(--signal), #ff7a59);
+}
+.scene .pct {
+  font-family: var(--mono);
+  font-size: 10.5px;
+  color: var(--text-dim);
+  flex: none;
+}
+.scene-note {
+  font-family: var(--mono);
+  font-size: 10.5px;
+  line-height: 1.55;
+  color: var(--text-faint);
+  margin: 10px 2px 0;
+}
+
+/* model select + chips */
+.select {
+  position: relative;
+}
+.select select {
+  width: 100%;
+  appearance: none;
+  background: var(--panel-2);
+  color: var(--text);
+  border: 1px solid var(--line);
+  border-radius: 7px;
+  padding: 10px 30px 10px 12px;
+  font-family: var(--mono);
+  font-size: 12px;
+  letter-spacing: 0.02em;
+  cursor: pointer;
+}
+.select select:focus {
+  outline: none;
+  border-color: var(--accent);
+  box-shadow: 0 0 0 3px var(--accent-glow);
+}
+.select::after {
+  content: "▾";
+  position: absolute;
+  right: 12px;
+  top: 9px;
+  color: var(--text-dim);
+  pointer-events: none;
+}
+.kv {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 6px;
+  margin-top: 11px;
+}
+.chip {
+  font-family: var(--mono);
+  font-size: 10px;
+  letter-spacing: 0.03em;
+  color: var(--text-dim);
+  border: 1px solid var(--line);
+  border-radius: 5px;
+  padding: 4px 7px;
+  background: var(--bg-2);
+}
+.chip b {
+  color: var(--accent);
+  font-weight: 500;
+}
+
+/* overlay controls */
+.toggle {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  margin-bottom: 14px;
+}
+.toggle .lbl {
+  font-size: 12.5px;
+  color: var(--text);
+}
+.sw {
+  position: relative;
+  width: 38px;
+  height: 20px;
+  border-radius: 20px;
+  background: #182430;
+  border: 1px solid var(--line);
+  cursor: pointer;
+  transition: 0.15s;
+  flex: none;
+}
+.sw::after {
+  content: "";
+  position: absolute;
+  top: 2px;
+  left: 2px;
+  width: 14px;
+  height: 14px;
+  border-radius: 50%;
+  background: var(--text-dim);
+  transition: 0.15s;
+}
+.sw.on {
+  background: var(--accent-deep);
+  border-color: var(--accent);
+}
+.sw.on::after {
+  left: 20px;
+  background: var(--accent);
+}
+.slabel {
+  display: flex;
+  justify-content: space-between;
+  font-family: var(--mono);
+  font-size: 10.5px;
+  letter-spacing: 0.06em;
+  color: var(--text-faint);
+  text-transform: uppercase;
+  margin-bottom: 8px;
+}
+.slabel b {
+  color: var(--text);
+  font-weight: 500;
+}
+input[type="range"] {
+  -webkit-appearance: none;
+  appearance: none;
+  width: 100%;
+  height: 3px;
+  border-radius: 3px;
+  background: linear-gradient(
+    90deg,
+    var(--accent) var(--fill, 70%),
+    #16212e var(--fill, 70%)
+  );
+  outline: none;
+  cursor: pointer;
+}
+input[type="range"]::-webkit-slider-thumb {
+  -webkit-appearance: none;
+  width: 14px;
+  height: 14px;
+  border-radius: 50%;
+  background: var(--text);
+  border: 2px solid var(--accent);
+  box-shadow: 0 0 0 3px var(--accent-glow);
+}
+input[type="range"]::-moz-range-thumb {
+  width: 12px;
+  height: 12px;
+  border-radius: 50%;
+  background: var(--text);
+  border: 2px solid var(--accent);
+}
+.legend {
+  display: flex;
+  align-items: center;
+  gap: 9px;
+  margin-top: 14px;
+  font-family: var(--mono);
+  font-size: 10.5px;
+  color: var(--text-dim);
+}
+.legend .sq {
+  width: 12px;
+  height: 12px;
+  border-radius: 3px;
+  background: var(--signal);
+  box-shadow: 0 0 8px rgba(255, 180, 84, 0.5);
+  flex: none;
+}
+
+/* analysis readout */
+.readout {
+  font-family: var(--mono);
+}
+.readout .row {
+  display: flex;
+  align-items: baseline;
+  justify-content: space-between;
+  padding: 9px 0;
+  border-bottom: 1px solid var(--line-2);
+}
+.readout .row:last-child {
+  border-bottom: 0;
+}
+.readout .k {
+  font-size: 10.5px;
+  letter-spacing: 0.06em;
+  text-transform: uppercase;
+  color: var(--text-faint);
+}
+.readout .v {
+  font-size: 16px;
+  font-weight: 500;
+  color: var(--text);
+  font-variant-numeric: tabular-nums;
+}
+.readout .v small {
+  font-size: 11px;
+  color: var(--text-dim);
+  margin-left: 3px;
+}
+.readout .v.skeleton {
+  color: transparent;
+  border-radius: 4px;
+  background: linear-gradient(90deg, #131c28, #1b2836, #131c28);
+  background-size: 200% 100%;
+  animation: shimmer 1.2s linear infinite;
+  min-width: 46px;
+  display: inline-block;
+  height: 14px;
+}
+@keyframes shimmer {
+  to {
+    background-position: -200% 0;
+  }
+}
+.gauge {
+  margin-top: 14px;
+}
+.gauge .track {
+  height: 6px;
+  border-radius: 4px;
+  background: #13202c;
+  overflow: hidden;
+}
+.gauge .track > i {
+  display: block;
+  height: 100%;
+  background: linear-gradient(90deg, var(--signal), #ff7a59);
+  box-shadow: 0 0 12px rgba(255, 140, 80, 0.4);
+  transition: width 0.5s cubic-bezier(0.2, 0.7, 0.2, 1);
+}
+.gauge .cap {
+  display: flex;
+  justify-content: space-between;
+  font-family: var(--mono);
+  font-size: 9.5px;
+  letter-spacing: 0.05em;
+  color: var(--text-faint);
+  margin-top: 6px;
+}
+.readout-empty {
+  font-family: var(--mono);
+  font-size: 11px;
+  color: var(--text-faint);
+  line-height: 1.6;
+}
+.readout-caption {
+  font-family: var(--mono);
+  font-size: 10px;
+  letter-spacing: 0.03em;
+  color: var(--text-faint);
+  line-height: 1.5;
+  margin: 12px 0 0;
+}
+
+/* ---------- stage ---------- */
+.stage {
+  flex: 1;
+  display: flex;
+  flex-direction: column;
+  min-width: 0;
+  min-height: 0;
+  position: relative;
+}
+.viewer {
+  position: relative;
+  flex: 1;
+  min-height: 0;
+}
+.compare-wrap {
+  position: absolute;
+  inset: 0;
+  overflow: hidden;
+  background: #04070b;
+  touch-action: none;
+}
+.map-layer {
+  position: absolute;
+  inset: 0;
+}
+.map-top {
+  z-index: 2;
+}
+.maplibregl-map {
+  background: #04070b;
+}
+.maplibregl-canvas {
+  outline: none;
+}
+
+/* change overlay — the mask is rendered server-side at native resolution as a translucent amber
+   fill + crisp outline. Smooth interpolation (never blocky) + a faint thermal glow. */
+.change-overlay {
+  position: absolute;
+  z-index: 3;
+  pointer-events: none;
+  image-rendering: auto;
+  filter: drop-shadow(0 0 2px rgba(255, 170, 80, 0.4));
+  transition: opacity 0.14s ease;
+}
+
+/* HUD */
+.hud {
+  position: absolute;
+  pointer-events: none;
+  font-family: var(--mono);
+  z-index: 5;
+}
+.hud.chip {
+  padding: 5px 9px;
+  border-radius: 6px;
+  font-size: 10.5px;
+  letter-spacing: 0.1em;
+  background: rgba(6, 10, 16, 0.72);
+  backdrop-filter: blur(6px);
+  -webkit-backdrop-filter: blur(6px);
+  border: 1px solid rgba(63, 208, 230, 0.18);
+  color: var(--text);
+  display: flex;
+  align-items: center;
+  gap: 8px;
+}
+.hud.chip .muted {
+  color: var(--text-faint);
+  letter-spacing: 0.04em;
+}
+.hud.before {
+  top: 14px;
+  left: 14px;
+}
+.hud.after {
+  top: 14px;
+  right: 14px;
+}
+.hud.legendchip {
+  bottom: 14px;
+  left: 14px;
+  gap: 9px;
+}
+.hud.legendchip .sq {
+  width: 11px;
+  height: 11px;
+  border-radius: 2px;
+  background: var(--signal);
+  box-shadow: 0 0 8px rgba(255, 180, 84, 0.6);
+}
+.hud.gsd {
+  bottom: 14px;
+  right: 14px;
+  color: rgba(223, 231, 240, 0.75);
+  font-size: 10px;
+  letter-spacing: 0.08em;
+  background: rgba(6, 10, 16, 0.6);
+  border-color: rgba(223, 231, 240, 0.14);
+}
+/* frame ticks */
+.frame {
+  position: absolute;
+  inset: 10px;
+  pointer-events: none;
+  z-index: 4;
+}
+.frame span {
+  position: absolute;
+  width: 12px;
+  height: 12px;
+  border: 1px solid rgba(63, 208, 230, 0.4);
+}
+.frame .tl {
+  top: 0;
+  left: 0;
+  border-right: none;
+  border-bottom: none;
+}
+.frame .tr {
+  top: 0;
+  right: 0;
+  border-left: none;
+  border-bottom: none;
+}
+.frame .bl {
+  bottom: 0;
+  left: 0;
+  border-right: none;
+  border-top: none;
+}
+.frame .br {
+  bottom: 0;
+  right: 0;
+  border-left: none;
+  border-top: none;
+}
+
+/* swipe divider */
+.swipe-divider {
+  position: absolute;
+  top: 0;
+  bottom: 0;
+  width: 2px;
+  background: linear-gradient(
+    180deg,
+    rgba(63, 208, 230, 0.2),
+    var(--accent),
+    rgba(63, 208, 230, 0.2)
+  );
+  z-index: 6;
+  transform: translateX(-1px);
+  cursor: ew-resize;
+  box-shadow: 0 0 12px rgba(63, 208, 230, 0.5);
+}
+.swipe-handle {
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%);
+  width: 38px;
+  height: 38px;
+  border-radius: 50%;
+  background: rgba(8, 12, 18, 0.85);
+  backdrop-filter: blur(6px);
+  -webkit-backdrop-filter: blur(6px);
+  border: 1px solid var(--accent);
+  color: var(--accent);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  font-family: var(--mono);
+  font-size: 13px;
+  box-shadow: 0 0 16px rgba(63, 208, 230, 0.35);
+  cursor: ew-resize;
+}
+
+/* loading scan veil (shown while a fresh prediction runs on CPU) */
+.scanning {
+  position: absolute;
+  inset: 0;
+  z-index: 7;
+  pointer-events: none;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  background: rgba(4, 7, 11, 0.35);
+  backdrop-filter: blur(1px);
+}
+.scanning .beam {
+  position: absolute;
+  inset: 0;
+  background: linear-gradient(
+    180deg,
+    transparent,
+    rgba(63, 208, 230, 0.12) 48%,
+    rgba(63, 208, 230, 0.18) 50%,
+    rgba(63, 208, 230, 0.12) 52%,
+    transparent
+  );
+  transform: translateY(-100%);
+  animation: sweep 1.5s cubic-bezier(0.4, 0, 0.2, 1) infinite;
+}
+@keyframes sweep {
+  to {
+    transform: translateY(100%);
+  }
+}
+.scanning .tagword {
+  font-family: var(--mono);
+  font-size: 11px;
+  letter-spacing: 0.3em;
+  color: var(--accent);
+  text-transform: uppercase;
+  padding: 8px 16px;
+  border: 1px solid rgba(63, 208, 230, 0.3);
+  border-radius: 6px;
+  background: rgba(6, 10, 16, 0.7);
+  box-shadow: 0 0 20px rgba(63, 208, 230, 0.2);
+}
+.scanning .tagword::after {
+  content: "";
+  animation: dots 1.2s steps(4, end) infinite;
+}
+@keyframes dots {
+  0% {
+    content: "";
+  }
+  25% {
+    content: "·";
+  }
+  50% {
+    content: "··";
+  }
+  75% {
+    content: "···";
+  }
+}
+
+.empty {
+  position: absolute;
+  inset: 0;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  color: var(--text-faint);
+  font-family: var(--mono);
+  font-size: 12px;
+  letter-spacing: 0.1em;
+  text-transform: uppercase;
+}
+
+/* telemetry footer */
+.telemetry {
+  height: 30px;
+  flex: none;
+  display: flex;
+  align-items: center;
+  border-top: 1px solid var(--line);
+  background: var(--panel);
+  font-family: var(--mono);
+  font-size: 10.5px;
+  letter-spacing: 0.05em;
+  color: var(--text-dim);
+  overflow: hidden;
+}
+.telemetry .cell {
+  padding: 0 14px;
+  height: 100%;
+  display: flex;
+  align-items: center;
+  gap: 7px;
+  border-right: 1px solid var(--line-2);
+  white-space: nowrap;
+}
+.telemetry .cell .k {
+  color: var(--text-faint);
+}
+.telemetry .cell b {
+  color: var(--text);
+  font-weight: 500;
+}
+.telemetry .cell.accent b {
+  color: var(--accent);
+}
+.telemetry .spacer {
+  flex: 1;
+}
+.telemetry .cell.hide-narrow {
+  display: flex;
+}
+
+/* ---------- model card ---------- */
+.card-page {
+  flex: 1;
+  overflow-y: auto;
+  background: radial-gradient(
+      1100px 500px at 80% -10%,
+      rgba(63, 208, 230, 0.05),
+      transparent 60%
+    ),
+    var(--bg);
+}
+.card-inner {
+  max-width: 900px;
+  margin: 0 auto;
+  padding: 42px 40px 64px;
+  animation: rise 0.5s cubic-bezier(0.2, 0.7, 0.2, 1) both;
+}
+.card-eyebrow {
+  font-family: var(--mono);
+  font-size: 10.5px;
+  letter-spacing: 0.2em;
+  text-transform: uppercase;
+  color: var(--accent);
+  margin: 0 0 12px;
+}
+.card-page h2 {
+  font-size: 26px;
+  font-weight: 600;
+  letter-spacing: -0.01em;
+  margin: 0 0 12px;
+}
+.card-lead {
+  color: var(--text-dim);
+  font-size: 14.5px;
+  line-height: 1.65;
+  max-width: 68ch;
+  margin: 0 0 30px;
+}
+.card-page h3 {
+  font-size: 12px;
+  font-family: var(--mono);
+  font-weight: 500;
+  letter-spacing: 0.14em;
+  text-transform: uppercase;
+  color: var(--text-faint);
+  margin: 34px 0 14px;
+  display: flex;
+  align-items: center;
+  gap: 10px;
+}
+.card-page h3::after {
+  content: "";
+  flex: 1;
+  height: 1px;
+  background: var(--line);
+}
+.metrics-table {
+  width: 100%;
+  border-collapse: collapse;
+  margin: 6px 0 8px;
+  font-size: 13.5px;
+  border: 1px solid var(--line);
+  border-radius: 10px;
+  overflow: hidden;
+}
+.metrics-table th,
+.metrics-table td {
+  text-align: left;
+  padding: 12px 14px;
+  border-bottom: 1px solid var(--line-2);
+}
+.metrics-table thead th {
+  color: var(--text-faint);
+  font-weight: 500;
+  font-size: 10px;
+  font-family: var(--mono);
+  text-transform: uppercase;
+  letter-spacing: 0.12em;
+  background: var(--panel);
+}
+.metrics-table td.num {
+  font-family: var(--mono);
+  font-variant-numeric: tabular-nums;
+  color: var(--text);
+}
+.metrics-table tbody tr:last-child td {
+  border-bottom: 0;
+}
+.metrics-table tr.row-headline {
+  background: linear-gradient(90deg, var(--accent-glow), transparent 80%);
+}
+.metrics-table tr.row-headline td:first-child {
+  box-shadow: inset 3px 0 0 var(--accent);
+}
+.metrics-table tr.row-headline td.num {
+  color: var(--accent);
+  font-weight: 600;
+}
+.row-note {
+  display: block;
+  font-size: 11px;
+  color: var(--text-faint);
+  margin-top: 3px;
+}
+.card-page p.muted,
+.card-page ul.muted {
+  color: var(--text-dim);
+  font-size: 13.5px;
+  line-height: 1.7;
+}
+.card-page ul.muted {
+  padding-left: 18px;
+}
+.card-page ul.muted li {
+  margin-bottom: 9px;
+}
+.card-page ul.muted li::marker {
+  color: var(--accent);
+}
+.card-page strong {
+  color: var(--text);
+}
+.callout {
+  border: 1px solid var(--line);
+  border-left: 2px solid var(--signal);
+  background: var(--panel);
+  border-radius: 8px;
+  padding: 16px 18px;
+  margin: 8px 0;
+}
+.callout p {
+  margin: 0;
+}
+
+/* ---------- responsive ---------- */
+@media (max-width: 980px) {
+  .telemetry .cell.hide-narrow {
+    display: none;
+  }
+}
+@media (max-width: 820px) {
+  body {
+    overflow: auto;
+  }
+  .body {
+    flex-direction: column;
+  }
+  .rail {
+    width: 100%;
+    border-right: 0;
+    border-bottom: 1px solid var(--line);
+  }
+  .viewer {
+    min-height: 62vh;
+  }
+}

frontend/src/types.ts

@@ -0,0 +1,39 @@
+export interface ModelSummary {
+  id: string;
+  input_size: number;
+  dynamic_hw: boolean;
+  threshold: number;
+  band_order: string[];
+  is_placeholder: boolean;
+  fixed_grid: boolean;
+}
+
+export interface CuratedPair {
+  id: string;
+  title: string;
+  description: string;
+  source: string;
+  width: number;
+  height: number;
+}
+
+export interface PredictStats {
+  changed_fraction: number;
+  changed_percent: number;
+  mean_confidence_changed: number;
+  mean_confidence_overall: number;
+  changed_pixels: number;
+  total_pixels: number;
+}
+
+export interface PredictResult {
+  overlay_png: string;
+  threshold: number;
+  is_placeholder: boolean;
+  stats: PredictStats;
+  elapsed_ms: number;
+  input_size: number;
+  pair_id: string;
+  model_id: string;
+  n_tiles?: number;
+}

frontend/tsconfig.json

@@ -0,0 +1,21 @@
+{
+  "compilerOptions": {
+    "target": "ES2021",
+    "useDefineForClassFields": true,
+    "lib": ["ES2021", "DOM", "DOM.Iterable"],
+    "module": "ESNext",
+    "skipLibCheck": true,
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+    "jsx": "react-jsx",
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "noFallthroughCasesInSwitch": true
+  },
+  "include": ["src"]
+}

frontend/vite.config.ts

@@ -0,0 +1,18 @@
+import react from "@vitejs/plugin-react";
+import { defineConfig } from "vite";
+
+// Dev server proxies /api to the FastAPI backend on :7860 so `npm run dev` works against it.
+// In production the same FastAPI process serves this build as static files (single origin).
+export default defineConfig({
+  plugins: [react()],
+  server: {
+    port: 5173,
+    proxy: {
+      "/api": "http://localhost:7860",
+    },
+  },
+  build: {
+    outDir: "dist",
+    sourcemap: false,
+  },
+});