proof of concept
STATUS: Just started
This page updates you on our progress
Training City Tegucigalpa
"caminante, no hay camino: se hace camino al andar"
Artificial intelligence tool at the service of
vulnerable communities for climate resiliency
in secondary fast urbanizing urban regions of the Global South
Product is a public flooding risk heat map for Resiliency Planning
location prediction of vulnerable communities
provides economical & automatic updates
communities complement mapping on the ground (COVID open)
what WE are doing
Location of our Proof of Concept is Honduras: our training city is Tegucigalpa and our Testing City is TBD. Although Tegucigalpa is not fast urbanizing, it presents myriad climate issues and poverty
Additionally, given that Hurricane Mitch struck in 1998, Tegucigalpa was center o f several studies that will be used to train for and to validate results
We started developing this AI tool concept pre COVID. Now, COVID highlights the importance of using remote sensing tools
More with less
Can we, with 20% of info, achieve 80% of certainty using Machine Learning?
Can we provide a userfriendly system that delivers fast and economical risk-heat- maps and its updates for secondary/tertiary cities?
What does it take to adapt algorithms to other regions?
Deep neural networks
Training City: Tegucigalpa, Honduras
Testing City: TBD
Data Sources: geospatial imagery (satellite, DEM) and open georeferenced datasets. We derived analytics-ready layers from these datasets such as topography, and distance to point of interest.
Methods: Convolutional neural networks (U-net architecture) for classification and prediction for vulnerable communities and hybrid models for flooding susceptibility.