Identifying False Negative Flood Events Using Interpretable Deep Learning Framework
Αναστάσιος Τέμενος, Nikos Temenos, Ioannis Rallis, Margarita Skamantzari, Anastasios D. Doulamis, Nikolaos D. Doulamis · 2024
An explainable AI framework for flood detection in SAR images is proposed. Compact encoder-decoder CNNs are used within the framework to achieve flood segmentation, with their output results fed to a Grad-CAM explainer so as to introduce trustworthiness to a stakeholder from naive thresholding selection during post-processing steps. The proposed framework is evaluated on the ETCi 2021 dataset using three different CNNs, resulting in more than 97% accuracy, while descriptive statistics on the Jaccard score are used to indicate the CNNs improper generalization towards the dataset. Edge cases highlight the importance of using Grad-CAM in complement with the CNN when the latter struggles to segment small regions due to thresholding.