Fusion of Radar and Optical Remote Sensing Time Series for Wildfire Monitoring with Deep Learning
November 2020 – October 2022
Wildfire monitoring involves two main problems, i.e., active fire detection and burnt area mapping. Active fire detection aims to find the ongoing wildfire hotspots, while burnt area mapping is expected to detect the areas affected by wildfire.
This project mainly focuses on large-scale wildfire burnt area mapping and near real-time wildfire monitoring. In view of the limited transfering performance of the existing wildfire monitoring algorithms on a larger scale and various climate zones, this project aims to develop large-scale or even globally applicable models by exploiting global coverage, multi-source satellite remote sensing data and advanced machine learning/deep learning techniques.
Wildﬁre has coexisted with human societies for more than 350 million years, always playing an important role in shaping the Earth’s surface and climate. Across the globe, wildﬁres are becoming larger, more frequent, longer-duration, and tend to be more destructive in terms of lives lost and economic costs because of climate change and human activities.
To reduce the damages from such destructive wildﬁres, it is critical to track wildﬁre progressions in near real-time, or even in real-time, to support fire-fighting and keep everything under control. Satellite remote sensing enables cost-eﬀective, accurate, and timely monitoring of the wildﬁre progressions over vast geographic areas. The free availability of global coverage Landsat-8, and Sentinel-1/2 satellite data opens a new era for global land surface monitoring, providing an opportunity to analyse wildﬁre impacts around the globe.
About the Digital Futures Postdoc Fellow
Puzhao Zhang is a research fellow at the Division of Geoinformatics, Royal Institute of Technology. He received his PhD in Pattern Recognition and Intelligent Systems from Xidian University, China, at the end of 2019. His PhD research was focused on remote sensing and deep learning for change detection.
Since fall of 2017, he has worked on wildfire monitoring with radar and optical remote sensing and deep learning as a joint PhD student at KTH. His research interests include satellite imagery analysis, change detection, machine learning, deep learning, and spatio-temporal modelling for monitoring environmental change and biomass carbon dynamics.
Yifang Ban, Professor, Division of Geoinformatics, KTH
Josephine Sullivan, Associate professor, Division of Robotics, Perception and Learning, KTH
Watch the recorded presentation at Digitalize in Stockholm 2022 event:
Former Digital Futures Postdoctoral Fellow, Postdoc project: Fusion of Radar and Optical Remote Sensing Time Series for Wildfire Monitoring with Deep Learningpuzhao@kth.se
Professor and Head of Division Geoinformatics at KTH, Member of the Executive Committee, Associate Director for Dissemination & Impact, PI: EO-AI4GlobalChange, Former Main supervisor: Unraveling the potential of AI and Earth Observation for accurate population predictions in urban regions (POPAI), Former Main supervisor: Fusion of Radar and Optical Remote Sensing Time Series for Wildfire Monitoring with Deep Learning, Digital Futures Faculty+46 8 790 86 48
Associate Professor, Division of Robotics, Perception and Learning at KTH, Co-PI: EO-AI4GlobalChange, Former Co-Supervisor: Fusion of Radar and Optical Remote Sensing Time Series for Wildfire Monitoring with Deep Learning, Digital Futures Faculty+46 8 790 61 36