Sebastian Gerard

Sebastian Gerard is a Researcher and PhD student at the Division of Robotics, Perception and Learning, KTH Royal Institute of Technology. His work focuses on applying machine learning and computer vision to address environmental challenges, particularly wildfire prediction and disaster response through remote sensing. He has contributed to the development of datasets like WildfireSpreadTS and TS-Satfire, advancing multimodal time-series analysis for wildfire spread prediction and disaster management.

Education details: While specific academic credentials are not explicitly listed, his role as a PhD student indicates ongoing advanced studies in Robotics, Perception, or related fields.

Research interests include wildfire prediction using satellite imagery, climate change mitigation via machine learning, and improving geospatial data analysis for disaster response. His work bridges computer vision techniques with environmental science, aiming to create actionable insights from remote sensing data.

Publications reflect contributions to wildfire modeling, semantic segmentation robustness, and smart grid automation. Collaborations include work with Josephine Sullivan and Paul Borne-Pons, addressing challenges in domain-specific pretraining and dataset validation.

Labs/Teams: Active in KTH's Robotics and Perception research groups, contributing to projects involving wildfire datasets and remote sensing applications.