A graph-based multimodal data fusion framework for identifying urban functional zone

作者：Tao Yuan, Wanzeng Liu, Jun Chen, Jingxiang Gao, Ran Li, Xinpeng Wang, Ye Zhang, Jiaxin Ren, Shunxi Yin, Xiuli Zhu, Tingting Zhao, Xi Zhai, Yunlu Peng · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2025 · DOI：10.1016/j.jag.2024.104353 · 被引用次数：22 · 研究领域：Human Mobility and Location-Based Analysis、Data-Driven Disease Surveillance、Video Surveillance and Tracking Methods

• A graph-based multi-modal data fusion framework is proposed for identifying urban functional zone. • A flexible feature-level fusion based on attention mechanism is proposed for processing heterogeneous geospatial data. • Knowledge-guided POI reclassification is beneficial to the UFZ identification. • The proposed method can correctly identify UFZ even if the POI data is incomplete. Accurately mapping urban functional zone (UFZ) provides crucial foundational geographic information services for urban sustainable development, territorial spatial planning, and public resource allocation. UFZs are blocks within urban environments that serve specific functions, typically comprising physical objects with specific spatial distribution patterns and semantic objects of various types. However, previous studies for identifying UFZs have focused on physical or semantic aspects of UFZs, overlooking the spatial relationships and connectivity among objects. Furthermore, few have leveraged the constructed graphs by heterogeneous geospatial data to identify functional zones by street block-based mapping units. To bridge this gap, we developed a graph-based multimodal data fusion framework (G2MF) to identify UFZs. It is a fully graph-based identification framework with a feature-level fusion strategy that integrates very high-resolution remote sensing images and point of interest data. Firstly, physical objects within a UFZ unit are classified using semantic segmentation technology; then, th...