Datasets:
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license: mit
task_categories:
- tabular-classification
- graph-ml
- other
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
[](https://huggingface.co/datasets/eduzrh/STEM)
[](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.
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