TM Project Centroids
TM Project Boundaries
Project Coverage Area
Mappers Involvedⓘ Total number of Mappers involved in the project
Countriesⓘ Total number of Countries covered in the project
Area Covered (Sq.KM)ⓘ Total area covered by the project
Total Map Editsⓘ Total number of OSM edits
Increasing the resilience of countries to natural hazards through the integration of open exposure data derived through satellite imagery and open protocols.
The escalating impacts of natural hazards are caused mostly by increasing exposure of populations and assets. It is estimated that the world will see the construction of 1 billion new dwellings by 2050 and this growth may lead to rapid increase in risk.
.: UK Space Agency :.
HOT is working together with the British Geological Survey, ImageCat, and the Global Earthquake Model on a UK Space Agency International Partnership Programme, focused on developing innovative applications of earth observation (EO) technologies to improve understanding of exposure to help minimise these risks.
The project aims to build and strengthen local and global resilience through complete, up-to-date, accurate exposure data in order to better identify risk and enable more effective decision-making:
Deliver exposure data for...
Recent news from Modelling Exposure Through Earth Observation Routines: METEOR (View all news)
An approach to field data collection in Kathmandu
Guest blog by Gaurav Thapa from Kathmandu Living Labs. Covering the collection process carried out by the team for surveying exposure data in Kathmandu Valley, Nepal as part of the METEOR project.
Digitising Kathmandu from above
Guest blog by Gaurav Thapa from Kathmandu Living Labs. Covering the digitisation process carried out by the team for mapping building footprints in Kathmandu Valley, Nepal as part of the METEOR project.
METEOR: Site Visits with Kathmandu Living Labs in Nepal
HOT and KLL set out on site visits across Kathmandu to assess the homogenous zones identified for mapping in OpenStreetMap. These seven zones have been identified as rural, residential, dense residential, urban, industrial, informal, high urban and new industrial. KLL will remotely digitise all the building footprints within the homogenous zones, before collecting detailed attribute information on the ground for a select sample of these buildings.