Analytics for Environmental Management Using the Vegetative Analysis Software Toolkit (VAST), a Codeless Artificial Intelligence (AI) Search Engine for the Physical World

S. Vaiyapuri
Robotic Services, Inc.,
United States

Keywords: UAS, remote sensing, object classification, environmental management, AI data analysis


Under a Strategic Financing contract with the US Air Force (USAF), Robotic Services, Inc. (RSI) is developing machine learning (ML) solutions to the USAF’s environmental management challenges. To comply with regulations, the USAF needs to identify habitats for the golden-cheeked warbler (GCWA) at Joint Base San Antonio. RSI created the Vegetative Analysis Software Toolkit (VAST), a dual-use data analysis platform that produces advanced analytics using ML and sensor fusion techniques to meet this need. Using lidar and multispectral imagery, VAST successfully identified high-quality GCWA habitats. To accomplish this, RSI created a pipeline of algorithms, including data augmentation, tree detection, individual tree height measurement, prediction of tree diameter at breast height using a regression model, and crown delineation. These results were key inputs to a USAF-designed habitat quality model. RSI envisions VAST as a comprehensive platform for geospatial data analysis. RSI has already adapted VAST to calculate wildfire fuel biomass, informing the USAF’s prescribed burn planning. We will further extend VAST’s identification and classification capabilities to additional use cases, such as situational awareness, installation maintenance, and construction management. With drag-and-drop features, users will manage their own data pipelines and gather insights in a low- to no-code environment friendly for non-experts.