Hierarchical GNN Framework for Earth’s Surface Anomaly Detection in Single Satellite Imagery

Sudden-onset Earth’s surface anomalies, such as natural disasters and man-made incidents, pose severe threats to human life and property security, emphasizing the crucial role of accurate detection and rapid response in Humanitarian Assistance and Disaster Response (HADR). In this work, we propose a hierarchical graph neural network (GNN)-based framework for Earth’s surface anomaly detection, called L2S-Net, to integrate from local-to-semantic (L2S) information for rapid and accurate detection of multiclass anomalies. Specifically, L2S-Net only uses a single very high-resolution (VHR) image as input to expedite processing speed, while using a hierarchical graph representation for better image understanding. Meanwhile, drawing from brain-inspired research and graph theory, we design an L2S fusion network, called L2S-GNN, to explicitly learn relationships between nodes at different levels facilitating accurate detection of Earth’s surface anomaly. L2S-Net significantly reduces data requirements while capturing valuable higher order information from images, achieving a superior balance between accuracy and efficiency. Furthermore, due to the lack of a public dataset for Earth’s surface anomaly detection, we create a novel and large-scale benchmark dataset ESADv2. Extensive experiments on the ESADv2 dataset and two real-world cases demonstrate that the proposed L2S-Net outperforms many state-of-the-art methods in both model size and performance while exhibiting exceptional generalizability and robustness.