Segmentation, Instance Tracking, and data Fusion Using multi-SEnsor imagery (SIT-FUSE) utilizes self-supervised machine learning (ML) that allows users to segment instances of objects in single and multi-sensor scenes, with minimal human intervention, even in low- and no-label environments. Can be used with image like and non image-like data. Currently, this technology is being used with remotely sensed earth data to identify objects including:
- Wildfires and smoke plumes
- Harmful algal blooms and their severity
- Palm oil farms
- Dust and volcanic ash plumes
- Inland water bodies
SIT-FUSE’s innovative multi-sensor fire and smoke segmentation precisely detects anomalous observations from instruments with varying spatial, spectra, and temporal resolutions. This capability creates a sensor web by incorporating observations from multiple satellite-based and suborbital missions. The ML framework’s output also facilitates smoke plume and fire front tracking, a task currently under development by the SIT-FUSE team.
Project is currently funded by NASA CSDSA, TEMPO, and DISASTERS programs.
Lister: Nick LaHaye
