Advances in GeoAI for Environmental Monitoring and Earth Observation Data Analysis

Short Communication

Abstract

The last five years have witnessed a rapid transformation in environmental monitoring driven by the convergence of
geospatial science and artificial intelligence, commonly referred to as GeoAI. The exponential growth of Earth Observation
(EO) data from satellite constellations, unmanned aerial systems, and in-situ sensors has created unprecedented opportunities
for large-scale, high-frequency environmental analysis, while simultaneously exposing the limitations of traditional analytical
methods. Recent advances in deep learning architectures—particularly transformer-based models—multimodal data fusion,
self-supervised learning, and privacy-preserving collaborative frameworks have significantly enhanced the accuracy,
scalability, and operational relevance of environmental monitoring systems. This paper presents a comprehensive review and
discussion of GeoAI advances between 2020 and 2025, focusing on their application to land-cover dynamics, disaster
monitoring, hydrological systems, biodiversity assessment, and atmospheric observation. Emphasis is placed on
methodological innovations, data challenges, operational deployment, and the emerging role of explainable and trustworthy AI
in environmental decision-making. While GeoAI has demonstrated transformative potential, persistent challenges remain in
data bias, generalization across regions, model interpretability, and energy-efficient deployment. The paper concludes with
recommendations for advancing GeoAI research and strengthening its adoption in operational environmental monitoring
frameworks.

Keywords: GeoAI, geospatial, artificial intelligence, earth observation, environmental monitoring.