Earth Observation Big Data and Deep Learning for Change Detection &Environmental Impact Assessment

In this video, Yifang Ban (Professor of Geoinformatics) at KTH Royal Institute of Technology gives a summary of her research project within Digital Futures.

The overall objective of this pilot 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).

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.

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).