Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

Abstract

Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities.

Automated Water Segmentation and River Level Detection on Camera Images Using Transfer Learning

“…A water level estimation model based on voluntary geographic information (VGI), LIDAR data and river camera images is developed in [ 13 ]. Notably, random forests are used to develop a waterline detection algorithm [ 2 ].…”

Deep Sensing of Urban Waterlogging

“…Witherow et al [34] proposed a flood detection and water level determination methodology that uses image processing and photogrammetric methods to process photos from smartphone cameras with volunteered geographic information (VGI) that is then linked to a local terrain model. Ning et al [35] implemented a prototype screening system to identify flooding-related photos from social media (e.g., Twitter, Facebook, and Instagram).…”

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