Mapping Nationwide Essential Urban Land Use Categories by Integrating Multimodal Deep Learning and Multi-source Geospatial Data

Detailed and up-to-date information on urban land use plays a key role in understanding urban environment, evaluating urban planning and promoting the development of sustainable cities and communities. Recent years have witnessed many efforts dedicated to developing effective land use classification methods and generating products at different scales. Nevertheless, an accurate and fine-grained delineation of parcel-level urban land use for the entire China is still lacking. In this study, we developed a novel urban land use mapping framework to identify accurate land use categories by integrating multimodal deep learning model and multisource geospatial data. With complete and precise land parcels generated by road networks from two public source as minimum classification units, we produced a nationwide Urban Essential Land Use Categories (EULUC) map covering all cities in China for 2022, named as EULUC 2.0. The mapping results show that residential, industrial and park and greenspaces are the dominant land use categories across China, collectively accounting for nearly 80% of the urban area. The spatially explicit information provided by EULUC 2.0 can reveal distinct spatial patterns of the heterogeneous land use landscape in each city. The evaluation results found the overall accuracies of Level-I and Level-II classification could be as high as 72% and 79%, with substantial improvements across all categories over previous product. The advancements can be mainly attributed to the effectiveness of deep learning for multi-modal input, especially the graph modeling of Point-of-interest (POI) data. The free-access product and insights in this study can potentially help researcher and practitioners to investigate and address the pressing urban challenges in the process of urbanization.