GeoAI for climate monitoring – A mini review

GeoAI, the integration of advanced machine learning techniques with geospatial data, has rapidly emerged as a critical tool in climate monitoring and environmental decision-making. Recent developments demonstrate a shift from isolated analytical models toward integrated, multimodal systems capable of processing multispectral, temporal, and sensor-based data for near-real-time applications. Key advancements include spatio-temporal data fusion, self-supervised learning (SSL) for low-label environments, operational deployment of monitoring systems, and the emergence of large geospatial foundation models. These innovations have enabled impactful applications such as methane emission detection, deforestation monitoring, wildfire prediction, and flood mapping. Despite this progress, challenges remain in benchmarking, generalization across heterogeneous data sources, uncertainty quantification, and equitable access to computational resources. Addressing these gaps is essential to ensuring reliable, scalable GeoAI solutions in climate science. To systematically analyze recent advancements in GeoAI for climate monitoring while identifying key methodological and operational gaps, with a focus on improving benchmarking standards and ensuring reproducibility in real-world applications. This review is constrained by the lack of standardized benchmarks and consistent evaluation frameworks across studies, which limits the comparability, reproducibility, and operational validation of GeoAI models.