Machine learning has been incorporated into spatial analysis allowing us to identify features more automatically. It also allows us to improve inferences into spatial data, knowing where we are missing pockets of people with greater significance, and identify trends. One of the benefits is that there are out of the box tools that make machine learning accessible for non-traditional data science staff. This session will focus on examples of how machine learning was used to provide impact in mapping and improve the science of 'where'.
Jacopo Margutti, Data Scientist, Netherlands Red Cross 510
Omran Najjar, AI and Advanced Data Engineer, Humanitarian OpenStreetMap Team
Kathryn M Clifton, PhD, Data Analytics and Reporting Lead, Catholic Relief Services
Bo Percival, Director of Technology Innovation, Humanitarian OpenStreetMaps Team