GIS and mapping

This study leverages remote sensing and machine learning to classify building age and integrate this information into a comprehensive flood hazard map for Al Ain City.

The purpose of this paper is to present a novel algorithm that significantly improves the efficiency and effectiveness of evacuation planning in areas affected by forest fires.

Accurate sea-level data, combined with an understanding of the depth and shape of coastal terrain, enables more precise modelling of marine hazards.

Launched in 2020, “Sayuru” (which means “Oceans”) is a trilingual service available free of charge for its subscribers. It provides vital weather-related information and early warnings to fishermen, based on their locations.

Using data collected from a 2022 magnitude 6.8 earthquake in Luding County in China's Sichuan Province, researchers have tested whether Global Navigation Satellite System observations could be used for rapid prediction of earthquake-triggered landslides.

Clouds have for decades been a bugbear for remote sensing of land surface temperature. A new approach incorporating machine learning appears to have solved this challenge.

The new model could create maps that predict urban area flooding, which traditionally have not been accessible to urban planners. This could help identify potentially flood-prone areas in urban settings.

The impacts of climate change are escalating, compounded by urban expansion into fire-prone areas. Eight cutting-edge technologies have the potential to revolutionize wildfire risk reduction strategies.