Spatial–Temporal Modeling for Satellite Typhoon Monitoring: YOLO11s-ASFF Detection and DeepSORT Tracking

Accurate typhoon monitoring and tracking are indispensable for disaster mitigation in coastal regions. However, existing methods are hindered by significant spatio-temporal limitations: in the spatial domain, conventional detectors struggle to distinguish weak typhoon vortices amidst severe cloud noise interference; in the temporal domain, standard tracking algorithms often suffer from target loss due to the highly nonlinear and erratic motion of typhoons. To address these challenges, we propose an integrated spatio-temporal framework that combines YOLO11s-ASFF for typhoon detection with DeepSORT for trajectory tracking. YOLO11s provides a lightweight, anchor-free detection architecture, with an adaptive spatial feature fusion (ASFF) stage enhancing multi-scale feature aggregation across detection heads while preserving the pretrained mapping. DeepSORT associates detections using motion-based Kalman filtering and appearance-independent matching, which is suitable for the single-object typhoon tracking scenario. Experiments are conducted on FY-4B Channel13 full-disk satellite imagery using an event-level split, with four storms for training, one for validation, and two for testing. On the held-out Lan and Saola storms, YOLO11s-ASFF achieves F1 0.782 at confidence 0.4, with precision 0.927 and recall 0.677, compared with 0.694 for official YOLO11s. With those detections, DeepSORT achieves MOTA 63.9%. The median center error of true-positive ASFF detections is 56.6 km against interpolated CMA positions, close to 56.3 km for the annotated windows. These results demonstrate the effectiveness of the proposed framework for robust typhoon monitoring and tracking from geostationary satellite imagery.