Artificial Intelligence and Explainability in Earth Observation: Enhancing Transparency, Trust, and Decision-Making in Remote Sensing Applications

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology in Earth Observation (EO), enabling automated analysis of vast datasets obtained from satellites, drones, and sensors. These AI-driven systems contribute significantly to environmental monitoring, disaster management, urban planning, and climate modeling. However, the rapid adoption of AI in EO introduces a critical challenge—explainability. Explainable AI (XAI) ensures that model outputs are interpretable, trustworthy, and scientifically valid, especially when decisions impact environmental policy, safety, and sustainability. This paper explores the convergence of AI and explainability in Earth Observation, emphasizing methodologies, challenges, and future directions for transparent and interpretable models. The study provides a comprehensive view of the evolution, techniques, applications, and prospects of XAI in the EO domain.

KEYWORDS: Artificial Intelligence, Explainable AI, Earth Observation, Remote Sensing, Interpretability, Machine Learning, Deep Learning, Environmental Monitoring, Transparency, Trust.