Abstract
Abstract. Flood extent delineation from RADAR images usually entails manual thresholding per scene, which is not feasible when tackling large-scale floods that often covers multiple RADAR scenes. It is also computationally intensive when processed through traditional remote sensing techniques that limit its use during emergency situations. To hasten the production of flood maps from RADAR images during flooding incidents, a deep learning model using Fully connected Convolutional Neural Network (FCNN) has been developed to delineate flooded areas with minimal human intervention. The model was formulated from the data gathered during a flooding event captured by both Sentinel-1A SAR satellite and Planet’s Dove optical satellites. Two pre-flood and one post-flood SAR scenes were used to detect the occurrence of water by analysing drops in backscatter values. The potential flood extents were verified using optical images which were then used to train the AI model. The model is currently being used operationally to map flood extent across the Philippines with no human intervention from data download to detection of flooded areas. The technique can detect floods across five Sentinel 1 scenes in less than four hours upon download of new satellite data.
Highlights
1.1 Background of the StudyFlood maps in the Philippines are available in different types and accuracies
This study aims to create an automated flood delineation technique with minimal human intervention that is operationally viable on a national scale
To quantify the actual ground detection accuracy, the AIpredicted flood maps was compared to the flood waters captured by Sentinel-2 optical satellite during a flooding event brought by Typhoon Mangkhut that battered the Philippines with 145 to 165 km/hr of winds last September 2018
Summary
1.1 Background of the StudyFlood maps in the Philippines are available in different types and accuracies. The Mines and Geosciences Bureau of the Department of Environment and Natural Resources (DENR-MGB) produces flood susceptibility maps for the entire country through their National Geohazard Assessment Program (Nieves, n.d). These maps were released in 1:10,000 scale for critical areas and 1:50,000 for other areas. The project produces high-resolution flood hazard maps from one-meter resolution LiDAR data (Lagmay, et al, 2017) through hydrologic modelling. The flood hazard maps were initially processed for the Philippines’ 18 major river basins, covering around 200 principal river basins across the country with LiDAR data
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