文献类型：期刊文献

英文题名：An Improved Geospatial Object Detection Framework for Complex Urban and Environmental Remote Sensing Scenes

作者：Zhu, Yueying[1];Chen, Aidong[1];Li, Xiang[2];Pan, Yu[1];Yuan, Yanwei[1];Yang, Ning[3];Chen, Wenwen[1,4];Huang, Jiawang[1,4];Cai, Jun[1];Fu, Hui[1]

第一作者：Zhu, Yueying

通讯作者：Fu, H[1]

机构：[1]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[2]Chinese Acad Sci, Inst Semicond, Beijing 100083, Peoples R China;[3]Nanjing Vocat Coll Finance & Econ, Nanjing 211121, Peoples R China;[4]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China

第一机构：北京联合大学机器人学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2026

卷号：16

期号：3

外文期刊名：APPLIED SCIENCES-BASEL

收录：;EI(收录号：20260720090163);Scopus(收录号：2-s2.0-105030096089);WOS:【SCI-EXPANDED(收录号:WOS:001687504500001)】；

基金：This research was supported by the National key research and development plan " Multidimensional visual information edge intelligent processor chip" (2022YFB2804402).

语种：英文

外文关键词：Geospatial Artificial Intelligence (GeoAI); urban monitoring; land use analysis; object detection; remote sensing images (RSIs); adaptive receptive fields; multi-path feature attention

摘要：Featured Application The RS-YOLO framework can be used in urban infrastructure monitoring, land use change detection, and transportation facility management. It offers a powerful GeoAI instrument for sustainable urban planning and environmental governance.Abstract The development of Geospatial Artificial Intelligence (GeoAI), combining deep learning and remote sensing imagery, is of great interest for automated spatial inference and decision-making support. In this paper, a GeoAI-based efficient object detection framework named RS-YOLO is introduced by adopting the YOLOv11 architecture. The model integrates Dynamic Convolution for adaptive receptive field adjustment, Selective Kernel Attention for multi-path feature aggregation, and the MPDIoU loss function for geometry-aware localization. The proposed approach outperforms in experimental results on the TGRS-HRRSD dataset of 13 scenes from different geospatial scenarios, giving an 89.0% mAP and an 87 F1-score. Beyond algorithmic advancement, RS-YOLO provides a GeoAI-based analytical tool for applications such as urban infrastructure monitoring, land use management, and transportation facility recognition, enabling spatially informed and sustainable decision-making in complex remote sensing environments.