ClimateCV: The Potential of Computer Vision Applications in Climate Change Mitigation and Adaptation

Monitoring climate change requires the analysis of vast Earth observation datasets, which exceed human capacity. Although computer vision (CV) offers transformative potential through automated image analysis, there is a critical gap between laboratory performance and operational reliability. Models struggle with domain shifts, lack uncertainty estimates, and lack workflow integration. This paper explores CV applications in key climate-related areas such as disaster assessment, agriculture and deforestation monitoring. We trace the evolution from convolutional neural networks (CNNs) to modern foundation models, and demonstrate how architectural innovations such as self-attention mechanisms, multimodal learning, and vision-language models (VLMs) enable environmental analysis at scales and speeds that were previously impractical for operational deployment. This is achieved through a systematic examination of operational systems and datasets. We propose five research directions that could maximise climate impact: real-time global monitoring systems, geospatial foundation models, multi-modal sensor fusion, uncertainty quantification for decision support, and addressing critical domain gaps.