Application of Image Processing and Convolutional Neural Networks for Flood Image Classification and Semantic Segmentation

Author

Jaku Rabinder Rakshit PallyFollow

Date of Award

12-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

Committee Chair/Advisor

Dr Nina Christine Hubig

Committee Member

Dr Vidya Samadi

Committee Member

Dr Amy Apon

Abstract

Floods are among the most destructive natural hazards that affect millions of people across the world leading to severe loss of life and damage to property, critical infrastructure, and the environment. Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights and biases to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed cameras, image processing, CNNs, and data connectivity capabilities for flood label detection. We built a training database service of >9000 images (image annotation service) including the image geolocation information by streaming relevant images from social media platforms, South Carolina Department of Transportation (SCDOT) 511 traffic cameras, the US geological Survey (USGS) live river cameras, and images downloaded from search engines. All these images were manually annotated to train the different models and detect a total of eight different object categories. We then developed a new python package called “FloodImageClassifier” to classify and detect objects within the collected flood images. “FloodImageClassifier” includes various 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 included in the package for flood water level estimation and classification. 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. “FloodImageClassifier” can be embedded to the USGS live river cameras or 511 traffic cameras to monitor river and road flooding conditions and provide early intelligence to decision makers and emergency response authorities in real-time.

Recommended Citation

Pally, Jaku Rabinder Rakshit, "Application of Image Processing and Convolutional Neural Networks for Flood Image Classification and Semantic Segmentation" (2021). All Theses. 3662.
https://open.clemson.edu/all_theses/3662