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  Investigating the role of globally available labeled dataset for flood detection using Sentinel-1 imagery and deep learning

Xiao, T., Motagh, M., Garg, S. (2021): Investigating the role of globally available labeled dataset for flood detection using Sentinel-1 imagery and deep learning - Abstract Book, Helmoltz AI virtual conference 2021 (online 2021).

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Item Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5006953 Version Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5006953_1
Genre: Conference Paper

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 Creators:
Xiao, Tianqi1, Author              
Motagh, M.1, Author              
Garg, Shagun1, Author              
Affiliations:
11.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146028              

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 Abstract: Flood is one of the most widespread and frequent natural disasters. Deriving accurate and rapid cartographic information on flood extent is essential to help manage the situation. Satellite remote sensing is now widely used for near real-time flood monitoring as it provides large scale detection in a time- and cost-efficient manner. Optical satellite imagery is employed as important tools for flood mapping due to easier interpretability and high spatial resolution. However, cloudy weather associated with floods are a great obstacle to optical sensors for flood monitoring. In contrast, Synthetic Aperture Radar (SAR) allows observation of wide areas across near all-weather conditions and plays a significant role in operational services for flood management. Although in many cases smooth water surfaces can be easily extracted from SAR imagery, it is subjected to overestimation of flooded areas especially in the arid and semi-arid regions since the complex interactions between SAR characteristics and environmental conditions. Advanced machine learning and deep learning approaches have demonstrated large potential to overcome the problem by learning features directly from images which requires a large number of labeled samples for training and validation. Therefore, some public georeferenced dataset to train and test deep learning flood algorithms are being produced. To investigate the role of globally available label datasets in obtaining reliable flood maps using SAR data and deep learning approaches, we tried one of the open access dataset, Sen1Floods11, which is a surface water dataset. We trained, validated and tested a ResNet50 model to segment flood water using a subset of this dataset.The classification results of flood water have obtained an overall accuracy of 89.5% for the test dataset in India and 78.9% for the test dataset in Pakistan. Results show the potential of the flood water dataset to better detect the flooded area.

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 Dates: 20212021
 Publication Status: Finally published
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 Identifiers: GFZPOF: p4 T3 Restless Earth
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Title: Helmoltz AI virtual conference 2021
Place of Event: online
Start-/End Date: 2021-04-14 - 2021-04-15

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Title: Abstract Book
Source Genre: Proceedings
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