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.

Reference objects in video images can be used to indicate urban waterlogging depths. The detection of reference objects is the key step to obtain waterlogging depths from video images. Object detection models with convolutional neural networks (CNNs) have been utilized to detect reference objects. These models require a large number of labeled images as the training data to ensure the applicability at a city scale. However, it is hard to collect a sufficient number of urban flooding images containing valuable reference objects, and manually labeling images is time-consuming and expensive. To solve the problem, we present a method to synthesize image data as the training data. Firstly, original images containing reference objects and original images with water surfaces are collected from open data sources, and reference objects and water surfaces are cropped from these original images. Secondly, the reference objects and water surfaces are further enriched via data augmentation techniques to ensure the diversity. Finally, the enriched reference objects and water surfaces are combined to generate a synthetic image dataset with annotations. The synthetic image dataset is further used for training an object detection model with CNN. The waterlogging depths are calculated based on the reference objects detected by the trained model. A real video dataset and an artificial image dataset are used to evaluate the effectiveness of the proposed method. The results show that the detection model trained using the synthetic image dataset can effectively detect reference objects from images, and it can achieve acceptable accuracies of waterlogging depths based on the detected reference objects. The proposed method has the potential to monitor waterlogging depths at a city scale.

Urban flood control requires real-time and spatially detailed information regarding the waterlogging depth over large areas, but such information cannot be effectively obtained by the existing methods. Video supervision equipment, which is readily available in most cities, can record urban waterlogging processes in video form. These video data could be a valuable data source for waterlogging depth extraction. The present paper is aimed at demonstrating a new approach to extract urban waterlogging depths from video images based on transfer learning and lasso regression. First, a transfer learning model is used to extract feature vectors from a video image set of urban waterlogging. Second, a lasso regression model is trained with these feature vectors and employed to calculate the waterlogging depth. Two case studies in China were used to evaluate the proposed method, and the experimental results illustrate the effectiveness of the method. This method can be applied to video images from widespread cameras in cities, so that a powerful urban waterlogging monitoring network can be formed.

In this paper, we study the problem of cross-domain image-to-image transformation from real faces to faces with shadow puppet style. Our aim is to make target images retain the salient features of real faces and capture the artistic style of shadow puppet faces. This task involves dual changes of contents and geometric structures to the source image, and the target distribution is a transitional distribution, that cannot be solved by existing solutions. We propose a new CycleGAN-based scheme that extends the transformation of two image domains to that of three domains. We adopt a two-stage generation strategy in the forward transformation and urge the generator of the first stage to learn the target distribution. To this end, we set up a discriminator for the generator to guide its generation. We also present a multiple contrastive training strategy to address the problem of difficulty in training the discriminator, because there is no way to obtain real samples from the target domain. The experimental results show that our scheme is effective and that the target generator can produce intermediate images that meet the requirements.