Responsible Artificial Intelligence for Earth Observation: Achievable and realistic paths to serve the collective good
(2025) IEEE Geoscience and Remote Sensing Magazine — Vol. 13, n° 3, p. 72-96 (2025)
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Responsible_Artificial_Intelligence_for_Earth_Observation_Achievable_and_realistic_paths_to_serve_the_collective_good.pdf
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- Authors
Ghamisi, Pedram 
Yu, Weikang Marinoni, Andrea Gevaert, Caroline M. Atkinson, Peter M.
- Abstract
- The convergence of artificial intelligence (AI) and Earth observation (EO) technologies has brought geoscience and remote sensing into an era of unparalleled capabilities. AI’s transformative impact on data analysis, particularly derived from EO platforms, holds great promise in addressing global challenges, such as environmental monitoring, disaster response, and climate change analysis. However, the rapid integration of AI necessitates a careful examination of the dimensions of responsibility inherent in its application within these domains. In this article, we represent a pioneering effort to systematically review the intersection of AI and EO, with a central focus on responsible AI practices. Specifically, we identify several critical components guiding this exploration from both academia and industry perspectives within the EO field: AI and EO for social good, mitigating unfair biases, AI security in EO, geoprivacy and privacy-preserving measures, and also maintaining scientific excellence, open data, and guiding AI usage based on ethical principles. Furthermore, the article explores potential opportunities and emerging trends, providing valuable insights for future research endeavors.
- Affiliations
Citations
Ghamisi, P., Yu, W., Marinoni, A., Gevaert, C. M., Persello, C., Selvakumaran, S., Girotto, M., Horton, B. P., Rufin, M. P., Hostert, P., Pacifici, F., & Atkinson, P. M. (2025). Responsible Artificial Intelligence for Earth Observation: Achievable and realistic paths to serve the collective good. IEEE Geoscience and Remote Sensing Magazine, 13(3), 72-96. https://doi.org/10.1109/mgrs.2025.3529726 (Original work published 2025)