Research by Eniola Onatayo (2025) The integration of Artificial Intelligence (AI) with remote sensing technologies presents a transformative approach to disaster monitoring and rapid response. As natural disasters become increasingly frequent and severe due to climate change, the need for effective real-time monitoring systems has never been more critical.

This study by Virtriana et al 2025 looks at changes in the demand for healthcare facilities in West Java by 2030. It uses both static and dynamic data processed at 30×30 metre intervals across West Java. Dynamic parameter extrapolation uses data from 2000 to 2018 using random forest machine learning. The results show changing need and identifies suitable locations for healthcare facility sites.

This study (Rana et al 2025) of land features in Vehari, Pakistan, uses remote sensing satellite imagery and machine learning to classify and analyse changes in land cover and land use between 1990 and 2025. It uses data from multispectral satellite images along with corresponding ground truth data, for training and validation of ML models. The study concludes that ML techniques, when integrated with remote sensing data, provide an effective means for monitoring and analysing land cover and land use.

Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches.