STARCOP: Automated and self-improving follow-up verification of detrimental human activity from LEO
Start Date
End Date
Status
Closed
Country
United Kingdom
![STARCOP: Automated and self-improving follow-up verification of detrimental human activity from LEO](/sites/default/files/styles/nebula_node_image/public/neb_study/2675/C4000138110PIC.jpg?itok=uMSAvzxC)
Description
‘STARCOP’ is an initiative to use machine learning and multiple satellites with diverse detection capabilities to quickly detect methane leaks and send notifications in near real-time. In this project Trillium Technologies developed machine learning models to automatically detect methane in hyperspectral and multispectral imagery. Their hyperspectral model (HyperSTARCOP) is able to capture more than 90% of plumes in test data while reducing the false positive rate by 39% when compared to state-of-the-art models. In addition, the activity shows for the first time automatic multispectral approaches that are able to capture large plumes in Copernicus Sentinel-2 data and WorldView-3 data.
Technology Domain
Competence Domain
Keywords
Executive summary