Image Processing and Semantic Segmentation Software for Flood and Inundation Mapping

Technical Summary:

“FloodImageClassifier” can classify and detect objects within the collected flood images. “FloodImageClassifier” includes various convolutional neural networks (CNNs) architectures such as YOLOv3 (You look only once version 3), Fast R-CNN (Region-based CNN), Mask R-CNN, SSD MobileNet (Single Shot MultiBox Detector MobileNet), and EfficientDet (Efficient Object Detection) to perform both object detection and segmentation simultaneously. Canny edge detection and aspect ratio concepts are also programmed in the package for flood water level estimation and inundation area calculation. The pipeline is smartly designed to train a large number of images and calculate flood water levels and inundation areas, which can be used to identify flood depth, severity, and risk. “FloodImageClassifer” can be embedded with the USGS live river cameras and 511 traffic cameras to monitor river and road flooding conditions, as well as provide early intelligence to emergency response authorities in real-time.