{"id":400,"date":"2020-08-27T13:05:11","date_gmt":"2020-08-27T13:05:11","guid":{"rendered":"http:\/\/digfut.demo-server.se\/?page_id=400"},"modified":"2024-08-22T09:09:06","modified_gmt":"2024-08-22T07:09:06","slug":"earth-observation-big-data","status":"publish","type":"page","link":"https:\/\/wpmu-tris.sys.kth.se\/digitalfutures\/research\/collaborative-projects\/on-going-collaborative-projects\/earth-observation-big-data\/","title":{"rendered":"EO-AI4GlobalChange"},"content":{"rendered":"

Objective
\n<\/em>Our planet faces unprecedented environmental challenges, including rapid urbanization, deforestation, pollution, loss of biodiversity, melting glacier, rising sea-level, and climate change. In recent years, the world also witnessed numerous natural disasters, from droughts, heat waves and wildfires to flooding and hurricanes, killing thousands and causing billions in property and infrastructural damages. With its synoptic view and large area coverage at regular revisits, satellite remote sensing has played a crucial role in monitoring our changing planet.<\/p>\n

The overall objective of the EO-AI4GlobalChange project is to develop innovative and robust methods for monitoring global environmental changes using Earth Observation big data and deep learning. This research will focus on three major global environmental challenges: urbanization,\u00a0wildfires and flooding.<\/p>\n

Open and free Earth observation big data such as Sentionel-1 SAR and Sentinel-2 MSI data have been used to demonstrate the novel deep learning-based methods in selected cities worldwide and various wildfire and flooding sites across the globe. For urban mapping, a novel Domain Adaptation (DA) approach using semi-supervised learning has been developed for built-up area extraction. Several novel methods have been developed for urban change detection, including a dual-stream U-Net and a Siamese Difference Dual-Task network. For early detection of active fires, Gated Recurrent Units and transformer networks have been used to improve GOES-R and VIIRS dense time series detections.<\/p>\n

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Figure 1. The Dixie Fire burned 963,309 acres (389,837 ha), was the largest single (i.e. non-complex) wildfire in recorded California history and the second-largest wildfire overall. The burned areas were clearly detected by the Sentinel-2 MSI (Left) and Sentinel-1 SAR (Right) images.<\/p><\/div>\n

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Figure 2. Started on July 13, 2021, and contained on October 25, 2021, the progression of the Dixie Fire was captured by Sentinel-1 and Sentinel-2 images.<\/p><\/div>\n

For wildfire progression monitoring, transfer learning-based models have been evaluated to exploit Sentinel-1 SAR and Sentinel-2 MSI data. Civil contingencies agencies can use the timely and reliable information the project generates to support effective emergency management and decision-making during and after wildfires and flooding. Automatic and continuous mapping of urban areas and their changes can support sustainable and resilient city planning and contribute to monitoring the UN 2030 Urban Sustainable Development Goal (SDG 11).<\/p>\n