Artificial intelligence in environmental remote sensing: Progress,way forward and key considerations

Abstract

Environmental remote sensing (ERS) has been a cornerstone technology for decades, allowing for comprehensive monitoring and management of ecosystems. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have transformed the field of ERS by offering robust, data-driven solutions for analyzing complex, large-scale remote sensing datasets and answering important scientific questions. This intervention explores the diverse applications of AI in ERS, focusing on key domains such as water, agriculture, urban, and wetland monitoring. This work also includes discussions on crucial aspects such as the impact of foundation models in ERS and outlines key points on the ethical and responsible use of AI in ERS. The intervention is concluded by identifying promising next steps for future research.

Keywords

Environment monitoring remote sensing machine learning deep learning foundation models responsible AI

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