<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/7EA7907F-2C1C-473A-ADE4-DF3F731D9366" ns1:id="7EA7907F-2C1C-473A-ADE4-DF3F731D9366"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/BF49F4F4-2AE8-4D35-8D30-81CCC0F98372" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/D303BE93-E2E4-4ED5-B8E7-B453F7103E85" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/D303BE93-E2E4-4ED5-B8E7-B453F7103E85" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2026-10-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/84A38B78-D504-4105-B229-6E15E056CC21" ns1:rel="FUND" ns1:start="2024-11-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10120034</ns2:identifier></ns2:identifiers><ns2:title>4EI and Ageospatial Joint Collaboration: SpaceLink (SL) - Community Intelligence for Geospatial Decisions</ns2:title><ns2:status>Active</ns2:status><ns2:grantCategory>CR&amp;D Bilateral</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>4EI and Ageospatial collaborate to offer near real-time geospatial data on floods and heatwaves, aiding local authorities and governments as well as sectors like insurance, finance, and engineering. By utilising AI, and Earth Observation, this partnership offers an accessible web-platform with state of the art algorithms for monitoring and decision support to create this 'Space-Link' service.

The unique selling proposition is its ability for any data friendly specialist in various organisations to get insights on extreme weather events in near real-time through GeoAI, reducing costs, time and increasing insights and accessibility. Unlike competitors, that focus purely on Earth Observation (EO) to detect those phenomena, this project will innovatively leverage both EO and Geodata supported by geospatial language models (GeoLLMs) to make the analysis, so that any non-geospatial technician is able access the valuable information in times of need. Despite the presence of slow-adapting industry giants like ESRI or Hexagon, the project focuses on open-data, user-friendly, and AI-powered geospatial analysis to position itself in a market ready for disruption.

Despite plenty of early warnings, the coronavirus disease 2019 (COVID-19) pandemic devastated the social fabric of many countries worldwide and triggered the largest global economic crisis in more than a century. Based on current projections, humankind faces comparable socio economic threats from impacts of global climate change -- in particular, increased frequency and magnitude of extreme weather events such as flash flooding, wildfires, droughts, heatwaves and smog. With higher concentration of population, economic activities, assets and critical infrastructure, risks posed by climate change to urban settlements is significantly greater than rural areas - over 60% of world population will reside in towns and cities by 2030\. Improving the resilience of towns and cities to climate change is a key priority recognised in the Paris Agreement (UNFCCC, 2015) and the United Nations (UN) 2030 Agenda for Sustainable Development (UN, 2015).

Neighbourhood-based risk and vulnerability assessments are often impeded by a lack of detailed spatial information mapping community-level exposure to current and projected climate change-related risk factors. It is also important to note that risk and vulnerability are typically dynamic - varying in both space and time across urban areas due to the seasonal nature of extreme weather events. Earth Observation (EO) technologies offer a potential solution to overcome these problems through acquisition of spatially dense, synoptic observations capturing surface and atmospheric conditions at regular temporal intervals across large urban areas.</ns2:abstractText></ns2:project>