Geospatial Semantic Sensing of Urban Functional Zones from VHR Images and Geographical Entities

Urban function mapping serves as a vital role in urban management and planning tasks. To generate fine-grained recognition at spatial and semantic scale, a contrastive manner integrating comprehensive representation from multi-modal descriptions of urban functional zones (UFZs)is proposed. Abstract physical features from VHR images are obtained from the founder deep convolutional model. Spatial pattern and semantic features are extracted from geographical entities including urban buildings and POIs, respectively. The proposed model is validated in the downtown Wuhan, China, where resources and sensing data citywide are concentrated. Rich information is provided but also the challenges due to the high complexity are posed for urban functions recognition. The superior performances demonstrate that the multidimensional, especially the integration with spatial pattern of primary urban materials, enhances the exact and robust recognition on sophisticated functions of urban land.