GIS and mapping

This paper describes an AI‑based method for mapping landslide risk using Slide3 slope analysis, field tests like ERT and SPT, and GIS tools to create a clear, reliable picture of where landslides are most likely to occur.

This study examines the factors influencing landslides in the Benighat-Rorang RM by analyzing soil structure, geology, land cover, geomorphology, fault lines, weather data, and road density to generate a comprehensive landslide Susceptibility Mapping.

Satellite and reanalysis data show aerosol changes in the Northern and Southern Hemispheres largely cancel out, shifting attention to cloud changes due to surface warming and natural climate variability.

This study employed comparative analysis, spatial interpolation, and random forest regression to develop an approach for the rapid assessment of severely affected areas using multiple mobile signaling indicators and multi-temporal analysis.

This study develops a probabilistic GIS-based flood risk assessment framework that integrates the Analytic Hierarchy Process (AHP) with its Monte Carlo extension (MC-AHP) for the Chengdu, China, metro network.

Human activities - such as mining, oil and gas extraction, dam-building and tapping into geothermal energy - have set off unexpected quakes in these stable regions.

With the GlobalBuildingAtlas, a research team at the Technical University of Munich (TUM) has created the first high-resolution 3D map of all buildings worldwide.

CalHeatScore creates heat wave warnings for every zip code in California, using temperature data, socioeconomic indicators, and the history of emergency room visits, to predict heat-related health risk.