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.

The increasing demand for advanced technologies in dual use scientific exploration and military operations has led to significant advancements in unmanned underwater vehicles (UUVs). These versatile systems have become indispensable for conducting a variety of maritime missions, including environmental monitoring, resource exploration and defense-related applications. (Roh et al 2025)

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.

A message from the Chair of the RSPSoc

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.