Behaviour Analytics For Time Series of Multi-Sensor Geospatial Data
We develop a statistical framework that integrates machine learning (deep learning) and classical change-detection techniques for monitoring changes in stationary objects and fusing multi-sensor geospatial data.
The framework consist of several analytical components that form part of the projects:
- Persistent multi-sensor land surveillance and change monitoring funded by the Defence Innovation Research Program (DIRP) and MDA.
- Detecting object behaviour of interests using deep learning funded by the Canadian Space Agency SmartEarth Program and MDA. Link to funding anouncement.
- Maritime insigths platform. Link to feature article about project.
Luz Angélica Caudillo Mata
Computational Scientist & Community Builder
Passionate about driving innovation and developing cutting-edge technology at the intersection of GeoAI and computational geosciences, while fostering collaborative communities.