A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric
Quick Answer
This article details an end-to-end spatial graph learning pipeline using city2graph, OSMnx, and PyTorch Geometric to infer urban functions.
Quick Take
This article details an end-to-end spatial graph learning pipeline using city2graph, OSMnx, and PyTorch Geometric to infer urban functions. By collecting urban POI and street network data from OpenStreetMap, the authors compare various proximity graph families and train a GraphSAGE model to predict POI categories based on spatial structure.
Key Points
- Utilizes city2graph for building a spatial graph learning pipeline.
- Collects urban POI and street network data from OpenStreetMap.
- Compares multiple proximity graph families for urban representation.
- Constructs heterogeneous and homogeneous graphs for analysis.
- Trains a GraphSAGE model to predict POI categories.
Article Excerpt
From source RSS / original summaryWe build an end-to-end spatial graph learning pipeline using city2graph. We collect urban POI and street network data from OpenStreetMap, with a synthetic fallback for reliability. We engineer spatial features, construct several proximity graph families, and compare how each represents the same urban environment. We then build heterogeneous and homogeneous graphs, convert them to PyTorch Geometric, and train a GraphSAGE model to predict POI categories from spatial structure.
The post A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric appeared first on MarkTechPost.
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