Seeing the World in Real-Time with Automated Land Use Monitoring

Impact Observatory recently demonstrated production of the world’s first fully automated time series of annual global land use land cover (LULC) maps at 10m resolution using deep learning algorithms applied to Sentinel-2 satellite imagery. We propose to develop an AI-powered prototype system for automated mapping and change monitoring of the world that can be applied continuously, in near real-time, at a resolution of 10m per pixel. We will extend our existing, state-of-the-art global 9-class LULC annual maps by land use change (LULUC) classes, using imagery time series with greater spatial resolution and temporal frequency. We will enhance our change detection geospatial data products using deep learning techniques which we will select to address shortcomings in standard pixel differencing and other traditional geospatial approaches   Fusing imagery from public and commercial Earth observation constellations enables global coverage in near real time, and all weather global coverage (using SAR). Automated analysis will be based on Impact Observatory’s deep learning image classification and analysis algorithms leveraging the cloud compute and imagery archives of Microsoft Azure Planetary Computer, and demonstrating easy, open distribution of results via cloud-based SpatioTemporal Asset Catalog (“STAC”) services, open data formats, and APIs. The prototype system will demonstrate automated processing over large test areas of interest (~1 million km2 total), and can be scaled globally after the R&D pilot is complete. Being a cloud-based capability, versus an on-premises solution, will enable further integration with new sources of data as they become available, for future resolution and temporal enhancements. The results of this work will be further developed and offered as a commercial service to US Government and users in industry and finance.   We will develop a prototype system for mapping and monitoring in near real-time with several key outcomes:  Rapid detection of important changes; Improve resource management; Understand the sources and context of changes; Support for stakeholders across the US Government and Allies/Partner Nations; and Monitor climate/environmental risks and resource depletion driving future global conflict.