Crowd Detection Using Very-Fine-Resolution Satellite Imagery

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 120k manually labeled individuals from multi-source satellite platforms (Beijing-3N, Jilin-1 Gaofen-04A and Google Earth) across China. In the experiments, CrowdSat-Net was compared with five state-of-the-art point-based CD methods (originally designed for ground or aerial imagery) using CrowdSat and achieved the largest F1-score of 66.12% and Precision of 73.23%, surpassing the second-best method by 1.71% and 2.42%, 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.

@article{xiao2025_2504.19546,
  title={ Crowd Detection Using Very-Fine-Resolution Satellite Imagery },
  author={ Tong Xiao and Qunming Wang and Ping Lu and Tenghai Huang and Xiaohua Tong and Peter M. Atkinson },
  journal={arXiv preprint arXiv:2504.19546},
  year={ 2025 }
}