GIS and mapping

Using Geographic Information Systems (GIS) for mapping disaster risk, hazard, exposure and vulnerability.

Latest GIS and mapping additions in the Knowledge Base

Update
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.
PR Newswire Association LLC.
Tuvalu Coastal Adaptation Project
Update
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.
Pacific Community
Cover
Documents and publications
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.
Cover
Documents and publications
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.
Beach area
Update
Accurate sea-level data, combined with an understanding of the depth and shape of coastal terrain, enables more precise modelling of marine hazards.
United Nations Development Programme (UNDP)
Mobile phone
Update
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.
Connecting Business initiative
Rockfall blocks a road followin heavy rain
Research briefs
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.
Seismological Society of America
Madagascar view of the countryside from the sky - cloudy
Research briefs
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.
Journal of Remote Sensing (AAAS)
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