Enhanced mapping of essential urban land use categories in China (EULUC-China 2.0): integrating multimodal deep...

Accurate, detailed, and up-to-date urban land use information plays a key role in understanding the urban environment, enhancing urban planning, and promoting sustainable urban development. Recent advancements have focused on refining urban land use classification methods and generating data products at various scales. However, detailed parcel-level urban land use mapping across China remains insufficient with low accuracy. To address this issue, we propose an enhanced mapping framework of essential urban land use categories by integrating multi-modal deep learning models and multi-source geospatial data. Utilizing complete, accurate land parcels derived from the combined OpenStreetMap and Tianditu road networks as the smallest classification units, we have developed an enhanced Essential Urban Land Use Categories (EULUC) map covering all cities in China for 2022, termed EULUC-China 2.0. The mapping results show that residential, industrial, and park and greenspace are the dominant land use categories, collectively accounting for nearly 78% of the urban area. Compared to its predecessor, EULUC-China 1.0, the updated 2.0 version offers more detailed, spatially explicit information that reveals distinct spatial patterns within diverse land use compositions of each city. Our evaluation demonstrates that the overall accuracies of Level-I and Level-II classification reach up to 79 % and 72 %, respectively, representing substantial enhancements across all categories over the previous product. These improvements are primarily attributed to the effectiveness of deep learning in processing multi-modal inputs, particularly through the graph modeling of Point-of-interest (POI) data. The publicly accessible product (https://zenodo.org/records/15180905) and the insights derived from this study offer a valuable dataset and references for researchers and practitioners addressing critical challenges in urbanization.