Topology-Informed Neural Networks Enhance Flood Detection Accuracy and Interpretability

Rapid and accurate flood detection is critical for emergency response and mitigating losses, but challenges persist, especially with cloud cover obscuring optical satellite data. While AI advancements have improved environmental hazard monitoring, many models remain "black boxes," hindering interpretation in safety-critical domains. This new work explores the integration of topological data analysis (TDA) with neural networks to address these limitations. The researchers systematically evaluated topological descriptors for flood detection using the SEN12-FLOOD dataset. By extracting and incorporating these global structural features into neural networks, they demonstrated that TDA provides meaningful flood signals independently and complements existing networks. This approach yields more robust and interpretable flood detection systems, offering a significant step forward for remote sensing applications.