README.md

metadata

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

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

# 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

@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.