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.

With an increasing world population and accelerated urbanization, the development of landscape sustainability remains a challenge for scientists, designers, and multiple stakeholders. Landscape sustainability science (LSS) studies dynamic relationships among landscape pattern, ecosystem services, and human well-being with spatially explicit methods. The design of a sustainable landscape needs both landscape sustainability–related disciplines and digital technologies that have been rapidly developing. GeoDesign is a new design method based on a new generation of information technology, especially spatial information technology, to design land systems. This paper discusses the suitability of GeoDesign for LSS to help design sustainable landscapes. Building on a review of LSS and GeoDesign, we conclude that LSS can utilize GeoDesign as a research method and the designed landscape as a research object to enrich and empower the spatially explicit methodology of LSS. To move forward, we suggest to integrate GeoDesign with LSS from six perspectives: strong/weak sustainability, multiple scales, ecosystem services, sustainability indicators, big data application, and the sense of place. Toward this end, we propose a LSS-based GeoDesign framework that links the six perspectives. We expect that this integration between GeoDesign and LSS will help advance the science and practice of sustainability and bring together many disciplines across natural, social, and design sciences.

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.