Crowd detection using Very-Fine-Resolution satellite imagery

Xiao, T. and Wang, Q. and Lu, P. and Huang, T. and Tong, X. and Atkinson, P.M. (2026) Crowd detection using Very-Fine-Resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 232. pp. 787-809. ISSN 0924-2716

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Abstract

Accurate crowd detection (CD) is critical for public safety and historical pattern analysis, yet existing methods relying on ground and aerial imagery suffer from limited spatio-temporal coverage. The development of very-fine-resolution (VFR) satellite sensor imagery (e.g., ∼0.3 m spatial resolution) provides unprecedented opportunities for large-scale crowd activity analysis, but it has never been considered for this task. To address this gap, we proposed CrowdSat-Net, a novel point-based convolutional neural network, which features two innovative components: Dual-Context Progressive Attention Network (DCPAN) to improve feature representation of individuals by aggregating scene context and local individual characteristics, and High-Frequency Guided Deformable Upsampler (HFGDU) that recovers high-frequency information during upsampling through frequency-domain guided deformable convolutions. To validate the effectiveness of CrowdSat-Net, we developed CrowdSat, the first VFR satellite imagery dataset designed specifically for CD tasks, comprising over 120 k manually labeled individuals from multi-source satellite platforms (Beijing-3 N, Jilin-1 Gaofen-04A and Google Earth) across China. In the experiments, CrowdSat-Net was compared with eight state-of-the-art point-based CD methods (originally designed for ground or aerial imagery and satellite-based animal detection) using CrowdSat and achieved the largest F1-score of 66.12 % and Precision of 73.23 %, surpassing the second-best method by 0.80 % and 6.83 %, respectively. Moreover, extensive ablation experiments validated the importance of the DCPAN and HFGDU modules. Furthermore, cross-regional evaluation further demonstrated the spatial generalizability of CrowdSat-Net. This research advances CD capability by providing both a newly developed network architecture for CD and a pioneering benchmark dataset to facilitate future CD development. The source code is available at https://github.com/Tong-777777/CrowdSat-Net.