Automatic Estimation of Urban Waterlogging Depths from Video Images Based on Ubiquitous Reference Objects

Abstract

Video supervision equipment, which is readily available in most cities, can record the processes of urban floods in video form. Ubiquitous reference objects, which often appear in videos, can be used to indicate urban waterlogging depths. This makes video images a valuable data source for obtaining waterlogging depths. However, the urban waterlogging information contained in video images has not been effectively mined and utilized. In this paper, we present a method to automatically estimate urban waterlogging depths from video images based on ubiquitous reference objects. First, reference objects from video images are detected during the flooding and non-flooding periods using an object detection model with a convolutional neural network (CNN). Then, waterlogging depths are estimated using the height differences between the detected reference objects in these two periods. A case study is used to evaluate the proposed method. The results show that our proposed method could effectively mine and utilize urban waterlogging depth information from video images. This method has the advantages of low economic cost, acceptable accuracy, high spatiotemporal resolution, and wide coverage. It is feasible to promote this proposed method within cities to monitor urban floods.

A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network

“…Vision-based waterlogging depth measurement is still in the exploratory phase. To the best of our knowledge, only Chaudhary et al and Jiang et al have reported similar approaches [16,29,33]. These approaches are different to those used in traditional studies, which fetch the graduated scale data from images with a water level line, use water level sensors to monitor waterlogging depth, or use a meteorological hydrological model to simulate runoff process with remote sensing and rainfall data.…”

“…Jiang et al [16,29] proposed a measurement approach to extract urban waterlogging depths from video images based on transfer learning and lasso regression. Although the approach could be used to estimate the waterlogging depth, the accuracy of the results was not high because the video images usually shoot from a fixed angle.…”

“…Based on R-CNN, the region-based fully convolutional network (R-FCN) [25], fast region-based convolutional neural network (Fast R-CNN) [26], faster region-based convolutional neural network (Faster R-CNN) [27], and mask region-based convolutional neural network (Mask R-CNN) [28] were proposed. Several common reference objects can be used as "rulers", such as pavement fences, trash bins, and traffic buckets [29]. However, in highly urbanized areas, vehicles are the most ubiquitous reference objects.…”

Author's reply to Referee #2 Comments

“…As shown by the literature suggested by Referee 2, there are different ways of obtaining this information. For example, one can rely on the visibility of ubiquitous objects of known dimensions (Jiang et al 2019). This approach is limited to scenes in which such objects are visible, and fails if the reference object is hidden or partially hidden by mobile objects during a flood.…”

“…We will add additional comparisons to existing methods for urban flood information extraction from surveillance images, showing both advantages and disadvantages of the alternatives. The publication of Jiang et al (2019) proposed in comment # 2.2 will be included.…”