The Replicable AI for Microplanning (ramp) project is an open-source deep learning model that accurately digitizes buildings in low-and-middle-income countries using satellite imagery and enables in-country users to build their own deep learning models for their regions of interest. 

The model and resulting buildings data can support a wide variety of humanitarian use cases. For this project, the team focused on responding to global health emergencies and designed our approach in partnership with the World Health Organization’s GIS Centre for Health and our Advisory Council. 

The primary outputs are the model codebase and documentation. Beyond the model, the team will release our data labels, label review tools, training data quality check methods, user personas, signed distance transform masks, and other geospatial tools.  

The approach of using satellite imagery and artificial intelligence to extract building footprints is not new. Ramp’s contribution is to open and democratize access to the deep learning model itself. The project leverages many other open-source efforts (all of which will be documented in detail in our publications and releases) like Maxar’s Open Data Program and very high-resolution imagery and SpaceNet’s open building labels for various cities globally. 

Our mission is all about democratizing access to technologies like artificial intelligence and high-resolution imagery. Since our business model is based on delivering best-in-class services with the most impact, open-source projects like ramp are right in our wheelhouse.  

Read more about our progress here on the DevGlobal blog.