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This tutorial demonstrates a practical application of Graph Neural Networks (GNNs) using PyTorch Geometric for urban function inference via spatial graph learning pipelines. By integrating OpenStreetMap data with synthetic fallbacks, it offers reproducible workflows for constructing heterogeneous graphs to predict Point-of-Interest categories from structural patterns. The implementation highlights specific techniques in feature engineering and model training relevant to researchers working on geospatial machine learning architectures.

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A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric

MarkTechPost13 iunie 20264 min de cititArticolul și perspectiva sunt în engleză.

We 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.

In this tutorial, we build an end-to-end spatial graph learning pipeline using city2graph. We start by collecting real urban POI data and street network information from OpenStreetMap, with a synthetic fallback to ensure the workflow remains reliable. We then engineer spatial features, construct multiple proximity graph families, and compare how different graph-building strategies represent the same urban environment. After that, we create both heterogeneous and homogeneous graph structures, convert them…

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