GIS and mapping

This paper explores the use of machine learning (ML) techniques to map flood hazards based on readily available geo-environmental variables.

This study produced spatial multi-criteria-based flood susceptibility, vulnerability, and risk index maps for the Kathmandu Valley (KV) watershed in Nepal using an Analytical Hierarchy Process (AHP) approach and Geographical Information System (GIS).

A new method provides highly accurate continental-scale landslide susceptibility maps that are being used in the aftermath of Hurricane Helene.

Climate Central has released a new mapping resource showing the potential for climate change to alter nations' coastlines, illustrating the stakes highlighted by a group of experts calling for a sea level rise upper limit.

Tuvalu is testing a new high-resolution mapping initiative which is a critical component of its digital transformation, cataloguing the nation's physical assets, including trees, houses, significant cultural sites and infrastructure.

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.