Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning

Urban region profiling is pivotal for smart cities, but mining fine-grained semantics from noisy and incomplete urban data remains challenging. In response, we propose a novel self-supervised graph collaborative filtering model for urban region embedding called EUPAS. Specifically, region heterogeneous graphs containing human mobility data, point of interests (POIs) information, and geographic neighborhood details for each region are fed into the model, which generates region embeddings that preserve intra-region and inter-region dependencies through GCNs and multi-head attention. Meanwhile, we introduce …

adversarial cities collaborative collaborative filtering cs.cv data embedding filtering fine-grained graph graphs human information mining mobility novel pivotal profiling self-supervised learning semantics smart smart cities supervised learning urban