Recent Trends Challenges and Limitations of Explainable AI in Remote Sensing

Adrian Höhl, Ivica Obadic, Miguel-Ángel Fernández-Torres, Dário A. B. Oliveira, Xiao Xiang Zhu

Abstract: Training deep learning models on remote sensing imagery is an increasingly popular approach for addressing pressing challenges related to urbanization extreme weather events food security deforestation or poverty reduction. Although explainable AI is getting more frequently utilized to uncover the workings of these models a comprehensive summary of how the fundamental challenges in remote sensing are tackled by explainable AI is still missing. By conducting a scoping review we identify the current works and key trends in the field. Next we relate them to recent developments and challenges in remote sensing and explainable AI. By doing so we also point to novel strategies and promising research directions such as the work on self-interpretable deep learning models and explanation evaluation.