An open source library and framework for deep learning on satellite and aerial imagery.
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Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
- satellite-imagery
- aerial-imagery
- machine-learning
- segmentation
Transform geospatial relations into graphs for Graph Neural Networks and spatial network analysis
- network-analysis
- spatial-analysis
- geographical-information-system
- graph-neural-networks
Train a deep learning net with OpenStreetMap features and satellite imagery.
Python package for graph neural networks in chemistry and biology
- deep-learning
- graph-neural-networks
- dgl
- cheminformatics
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
- deep-learning
- pytorch
- deep-chemistry
- graph-neural-network
Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.
- alphafold3
- docking
- drug-design
- drug-discovery
3D shape analysis using deep learning
- cancer-biology
- cell-biology
- deep-learning
- geometric-deep-learning
gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions