EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Improving near real-time flood extraction pipeline from SAR data using deep learning

Mathieu Turgeon-Pelchat1, Heather McGrath1, Fatemeh Esfahani2, Simon Tolszczuk-Leclerc1, Thomas Rainville1, Nicolas Svacina1, Lingjun Zhou2, Zarrin Langari2, and Hospice Houngbo2
Mathieu Turgeon-Pelchat et al.
  • 1Natural Resources Canada, Canada Centre for Mapping and Earth Observation, Sherbrooke, Canada
  • 2Natural Resources Canada, Innovation Branch, Ottawa, Canada

The Canada Centre for Mapping and Earth Observation (CCMEO) uses Radarsat Constellation Mission (RCM) data for near-real time flood mapping. One of the many advantages of using SAR sensors, is that they are less affected by the cloud coverage and atmospheric conditions, compared to optical sensors. RCM has been used operationally since 2020 and employs 3 satellites, enabling lower revisit times and increased imagery coverage. The team responsible for the production of flood maps in the context of emergency response are able to produce maps within four hours from the data acquisition. Although the results from their automated system are good, there are some limitations to it, requiring manual intervention to correct the data before publication. Main limitations are located in urban and vegetated areas. Work started in 2021 to make use of deep learning algorithms, namely convolutional neural networks (CNN), to improve the performances of the automated production of flood inundation maps. The training dataset make use of the former maps created by the emergency response team and is comprised of over 80 SAR images and corresponding digital elevation model (DEM) in multiple locations in Canada. The training and test images were split in smaller tiles of 256 x 256 pixels, for a total of 22,469 training tiles and 6,821 test tiles. Current implementation uses a U-Net architecture from NRCan geo-deep-learning pipeline ( To measure performance of the model, intersection over union (IoU) metric is used. The model can achieve 83% IoU for extracting water and flood from background areas over the test tiles. Next steps include increasing the number of different geographical contexts in the training set, towards the integration of the model into production.

How to cite: Turgeon-Pelchat, M., McGrath, H., Esfahani, F., Tolszczuk-Leclerc, S., Rainville, T., Svacina, N., Zhou, L., Langari, Z., and Houngbo, H.: Improving near real-time flood extraction pipeline from SAR data using deep learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9091,, 2023.