FloodIMG: Flood image DataBase system

A breakthrough in building models for image processing came with the discovery that a convolutional neural network (CNN) can progressively extract higher-level representations of the image content. Having high-resolution images to train CNN models is a key for optimizing the performance of image segmentation models. This paper presents a new dataset—called Flood Image (FloodIMG) database system—that was developed for flood related image processing and segmentation. We developed various Internet of Things Application Programming Interfaces (IoT API) to gather flood-related images from Twitter, and US federal agencies’ web servers, such as the US Geological Survey (USGS) and the Department of Transportation (DOT). Overall, >9200 images of flooding events were collected, preprocessed, and formatted to make the dataset applicable for CNN training. Bounding boxes and polygon primitives were also labeled on each image to localize and classify an object in the image. Two use cases of FloodIMG are presented in this paper, where the Fast Region-based CNN (R-CNN) algorithm was used to estimate flood severity and depth during recent flooding events in the US. As of >9200 images, 7,400 were categorized as training sets, whereas >1,800 images were used for the R-CNN testing. Users can access the FloodIMG database freely through Kaggle platform to create more accessible, accurate, and optimized image segmentation models. The FloodIMG workflow concludes with a visualization of colors and labels per image that can serve as a benchmark for flood image processing and segmentation

An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks

Abstract The use of automated methods for detecting and classifying different types of labels in flood images have important applications in hydrologic prediction. In this research, we propose a fully automated end‐to‐end image detection system to predict flood stage data using deep neural networks across two US Geological Survey (USGS) gauging stations, that is, the Columbus and the Sweetwater Creek, Georgia, USA. The images were driven from the USGS live river web cameras, which were strategically located nearby the monitoring stations and refreshed roughly every 30 s. To estimate the flood stage, a U‐Net Convolutional Neural Network (U‐Net CNN) was first stacked on top of a segmentation model for noise and feature reduction that diminished the number of images needed for training. A Long Short‐Term Memory (LSTM), a dense model, and a CNN were then trained to predict the flood stage time series data in near real‐time (6, 12, 24, and 48 hr). The results revealed that the U‐Net CNN has a higher accuracy for image segmentation if the algorithm is stacked in front of the network. The absolute error with the U‐Net was 0.0654 feet at the Columbus while it was 0.0035 feet at the Sweetwater Creek, which were practically low for flood stage estimation. For time series prediction, among three models, the LSTM predicted the flood stage values more accurately during both historical (2015–2022) as well as real‐time forecasts, particularly for 24 and 48 hr timescales. We extensively evaluated the proposed flood stage prediction system against current state‐of‐the‐art methodologies partly crowd‐sourced and mined in real‐time