Where Machine Learning Fits in HOT


1 min read

We’ll be at the Humanitarian OpenStreetMap Team (HOT) Summit to discuss how machine learning techniques can help improve the mapping and field data collection work that HOT provides to disaster aid workers.

There’s tremendous potential for this. In the peak moments after a disaster, speed and accuracy are both crucial elements of a response. This is why we’ve worked on tools to automatically map regions using open data and machine learning, and why we’re working with the World Bank to build a more user-friendly, offline-ready field data collection app.

On Friday at 10am ET, Ian Schuler will facilitate a community discussion on how we can integrate similar machine learning approaches into the HOT workflow.

We’ll also be participating in panels and discussions on providing a source of open drone and satellite imagery to first responders, and improving the design and interface of the HOT tasking manager, a crucial tool that allows the organization to quickly and collaboratively map affected areas.

We’ll be posting thoughts and reflections on Twitter. If you’re attending, ping @ianschuler, @ascalamogna, and @felskia, to talk about machine learning, field data collection, and humanitarian response. Or just to grab a coffee.

What we're doing.