ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution

arXiv:2403.01738v1 Announce Type: new
Abstract: Spatiotemporal (ST) learning has become a crucial technique to enable smart cities and sustainable urban development. Current ST learning models capture the heterogeneity via various spatial convolution and temporal evolution blocks. However, rapid urbanization leads to fluctuating distributions in urban data and city structures over short periods, resulting in existing methods suffering generalization and data adaptation issues. Despite efforts, existing methods fail to deal with newly arrived observations and those methods with generalization capacity are …

abstract arxiv become cities city convolution cs.lg current data development evolution leads smart smart cities spatial sustainable temporal type urban via