Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities
원문 초록
Deep learning has taken by storm all fields involved in data analysis, including remote sensing for Earth observation. However, despite significant advances in terms of performance, its lack of explainability and interpretability, inherent to neural networks in general since their inception, remains a major source of criticism. Hence it comes as no surprise that the expansion of deep learning methods in remote sensing is being accompanied by increasingly intensive efforts oriented towards addressing this drawback through the exploration of a wide spectrum of Explainable Artificial Intelligence techniques. This chapter, organized according to prominent Earth observation application fields, presents a panorama of the state-of-the-art in explainable remote sensing image analysis.
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Explainable Earth Surface Forecasting under Extreme Events
High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano Things
Video Compression for Spatiotemporal Earth System Data
Scaling-Up the Pretraining of the Earth Observation Foundation Model PhilEO to the MajorTOM Dataset
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
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