In the second part of our Automated Building Footprint Extraction series, we review some evaluation metrics for building footprint extraction.
In this blog we demonstrate how an active learning approach can boost machine learning model performance with the human-in-the-loop workflow.
Raster Vision is the interface between the fields of earth observation and deep learning, making it easier to apply novel computer vision techniques to geospatial imagery of all types. Joe lays out how it can be implemented in your organization and give you a competitive advantage.
We partnered with Driven Data and the World Bank to develop the Open Cities AI Challenge. This challenge uses machine learning to extract building footprints in unmapped areas to promote disaster resilience.