The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 97–104, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-97-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 97–104, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-97-2020

  21 Aug 2020

21 Aug 2020

TOWARDS AN AUTOMATED FLOOD AREA EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES

R. G. C. J. Kapilaratne and S. Kaneta R. G. C. J. Kapilaratne and S. Kaneta
  • Asia Air Survey Co. Ltd., 215-0004, Asao Ku, Kawazaki City, Kanagawa, Japan

Keywords: Disaster mapping, Remote Sensing, Optical Satellite Images, Deep learning, Convolutional Neural Networks, Emergency Response Systems

Abstract. Flooding is considered as one of the most devastated natural disasters due to its adverse effect on human lives as well as economy. Since more population concentrate towards flood prone areas and frequent occurrence of flood events due to global climate change, there is an urgent need in remote sensing community for faster and reliable inundation mapping technologies to increase the preparedness of population and reduce the catastrophic impact. With the recent advancement in remote sensing technologies and integration capability of deep learning algorithms with remote sensing data makes faster mapping of large area is feasible. Therefore, this study attempted to explore a faster and low cost solution for flood area extraction by integrating convolution neural networks (CNNs) with high resolution (1.5 m) SPOT satellite images. By consider the system requirement as a measure of cost, capabilities (speed and accuracy) of a deeper (ResNet101) and a shallower (MobileNetV2) CNNs on flood mapping were examined and compared. The models were trained and tested with satellite images captured during several flood events occurred in Japan. It is observed from the results that ResNet101 obtained better flood area mapping accuracy than MobileNetV2. Whereas, MobileNetV2 is having much higher capabilities in faster mapping in 0.3 s/km2 with a competitive accuracy and minimal system requirements than ResNet101.