Abstract
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.
Highlights
1.1 BackgroundDuring the past decade, worlds water related disasters became frequent and severe as an adverse effect of changing pattern in global climate
As mentioned in the previous section model training was carried out until validation accuracy obtained more than 90%
ResNet101 obtained above 91% accuracy whereas MobileNetV2 able to obtained above 90% validation accuracy
Summary
Worlds water related disasters became frequent and severe as an adverse effect of changing pattern in global climate. With the advancement of the satellite imagining technologies during the last few decades and capabilities of commercial satellites to capture images on demand within hours, remote sensing rewarded as highly demanding surveying option for disaster mapping in near real time. Existing studies on this regard, were mostly focused on manual methods such as change detection from pre and post disaster event using image algebra (band differencing, band rationing), post classification comparison and object-based change detection (Amit and Aoki, 2018). Remote sensing community is always committed to develop technologies for better performance in near real time. Its image handling capability and integration ability of automatic feature extraction attracted majority of the researchers towards DL from conventional image processing technologies (Yang and Cervone, 2019)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
