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EO-AI4GlobalChange

About the project
The overall objective of this project is to develop innovative and robust methods for monitoring global environmental changes using Earth Observation big data and deep learning. The main application areas of the project are urbanization and wildfire monitoring. Timely and reliable information that the project generates can be used by fire-fighting authorities to quickly put in the right resources, estimate how fires will develop and continuously assess the damages. Automatic and continuous mapping of urban changes can be used to support sustainable urban planning and contribute to monitoring the UN 2030 Urban Sustainable Development Goal (SDG 11).

Background
In recent years, the world has experienced many devastating wildfires due to human-induced climate change, most recently in Australia around the turn of the year 2019/20. Wildfires kill and displace people, damage property and infrastructure, burn vegetation and harm wildlife, and cost billions of euros to fight. Up-to-date and reliable information on fire risk, active fires, fire extent, progression and damage assessment is critical for effective emergency management and decision support.

The pace of urbanization has been unprecedented. Rapid urbanization poses significant social and environmental challenges, including sprawling informal settlements, increased pollution, urban heat island, loss of biodiversity and ecosystem services, and making cities more vulnerable to disasters. Therefore, accurate and consistent information on urbanchanging patterns is essential to support sustainable urban development and UN’s New Urban Agenda.

Photo of Fraser Island Bushfire

Monitoring of Fraser Island Bushfire with Sentinel-1 SAR & Deep Learning. Fraser Island, Australia is the world’s largest sand island & listed as a UNESCO World Heritage site.

Cross-disciplinary collaboration
The researchers in the team represent the School of Architecture and the Built Environment (ABE, KTH) and the School of Electrical Engineering and Computer Science (EECS, KTH).

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Contacts

Photo of Yifang Ban

Yifang Ban

Professor and Head of Division Geoinformatics at KTH, PI of research project EO-AI4GlobalChange at Digital Futures, Supervisor for Postdoc project Unraveling the potential of AI and Earth Observation for accurate population predictions in urban regions (POPAI), Supervisor for Postdoc project Fusion of Radar and Optical Remote Sensing Time Series for Wildfire Monitoring with Deep Learning at Digital Futures

+46 8 790 86 48
yifang@kth.se

Andrea Nascetti

Researcher, Geoinformatics at KTH, Co-PI of research project EO-AI4GlobalChange at Digital Futures

nascetti@kth.se

Hossein Azizpour

Assistant Professor, Robotics, Perception and Learning at KTH, Co-PI of research project EO-AI4GlobalChange at Digital Futures, Supervisor for Postdoc project Deep Learning Approaches for Long-term Future Forecasting at Digital Futures

azizpour@kth.se

Josephine Sullivan

Associate Professor, Divsion of Robotics, Perception and Learning at KTH, Co-PI of research project EO-AI4GlobalChange at Digital Futures, Co-Supervisor for Postdoc project Fusion of Radar and Optical Remote Sensing Time Series for Wildfire Monitoring with Deep Learning at Digital Futures

+46 8 790 61 36
sullivan@csc.kth.se

Ulla Mörtberg

Professor and Docent, Sustainable Development, Environmental Science and and Engineering at KTH, Co-PI of research project EO-AI4GlobalChange at Digital Futures

+46 8 790 86 08
mortberg@kth.se