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Near-Real-Time Flood Mapping Using Off-the-Shelf Models with SAR Imagery and Deep Learning
- Source :
- Remote Sensing; Volume 13; Issue 12; Pages: 2334, Remote Sensing, Vol 13, Iss 2334, p 2334 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- Timely detection of flooding is paramount for saving lives as well as evaluating levels of damage. Floods generally occur under specific weather conditions, such as excessive precipitation, which makes the presence of clouds very likely. For this reason, radar-based sensors are most suitable for near-real-time flood mapping. The public dataset Sen1Floods11 recently released by the Cloud to Street is one example of ongoing beneficial initiatives to employ deep learning for flood detection with synthetic aperture radar. The present study used this dataset to improve flood detection using well-known segmentation architectures, such as SegNet and UNet, as networks. In addition, this study provided a deeper understanding of which set of polarized band combination is more suitable for distinguishing permanent water, as well as flooded areas from the SAR image. The overall performance of the models with various kinds of labels and a combination of bands to detect all surface water areas were also assessed. Finally, the trained models were tested on a completely different location at Kerala, India, during the 2018 flood for verifying their performance in the real-world situation of a flood event outside of the given test set in the dataset. The results prove that trained models can be used as off-the-shelf models to achieve an intersection over union (IoU) as high as 0.88 in comparison with optical images. The omission and commission error were less than 6%. However, the most important result is that the processing time for the whole satellite image was less than 1 min. This will help significantly for providing analysis and near-real-time flood mapping services to first responder organizations during flooding disasters.
- Subjects :
- Synthetic aperture radar
010504 meteorology & atmospheric sciences
Computer science
Sen1Floods11
Science
0211 other engineering and technologies
near-real-time flood mapping
Cloud computing
02 engineering and technology
transfer learning
01 natural sciences
disaster response
synthetic aperture radar (SAR)
deep learning
law.invention
law
Radar
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Flood myth
Intersection (set theory)
business.industry
Deep learning
Flooding (computer networking)
Test set
General Earth and Planetary Sciences
Artificial intelligence
business
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Database :
- OpenAIRE
- Journal :
- Remote Sensing; Volume 13; Issue 12; Pages: 2334
- Accession number :
- edsair.doi.dedup.....64ce4b31431f5ad47802c52d6e868d87
- Full Text :
- https://doi.org/10.3390/rs13122334