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