Earth Imagery Segmentation on Terrain Surface with Limited Training Labels: A Semi-supervised Approach based on Physics-Guided Graph Co-Training

Given earth imagery with spectral features on a terrain surface, this paper studies surface segmentation based on both explanatory features and surface topology. The problem is important in many spatial and spatiotemporal applications such as flood extent mapping in hydrology. The problem is uniquely challenging for several reasons: first, the size of earth imagery on a terrain surface is often much larger than the input of popular deep convolutional neural networks; second, there exists topological structure dependency between pixel classes on the surface, and such dependency can follow an unknown and non-linear distribution; third, there are often limited training labels. Existing methods for earth imagery segmentation often divide the imagery into patches and consider the elevation as an additional feature channel. These methods do not fully incorporate the spatial topological structural constraint within and across surface patches and thus often show poor results, especially when training labels are limited. Existing methods on semi-supervised and unsupervised learning for earth imagery often focus on learning representation without explicitly incorporating surface topology. In contrast, we propose a novel framework that explicitly models the topological skeleton of a terrain surface with a contour tree from computational topology, which is guided by the physical constraint (e.g., water flow direction on terrains). Our framework consists of two neural networks: a convolutional neural network (CNN) to learn spatial contextual features on a 2D image grid, and a graph neural network (GNN) to learn the statistical distribution of physics-guided spatial topological dependency on the contour tree. The two models are co-trained via variational EM. Evaluations on the real-world flood mapping datasets show that the proposed models outperform baseline methods in classification accuracy, especially when training labels are limited.

Automatic Reconstruction of Building Façade Model from Photogrammetric Mesh Model

Three-dimensional (3D) building façade model reconstruction is of great significance in urban applications and real-world visualization. This paper presents a newly developed method for automatically generating a 3D regular building façade model from the photogrammetric mesh model. To this end, the contour is tracked on irregular triangulation, and then the local contour tree method based on the topological relationship is employed to represent the topological structure of the photogrammetric mesh model. Subsequently, the segmented contour groups are found by analyzing the topological relationship of the contours, and the original mesh model is divided into various components from bottom to top through the iteration process. After that, each component is iteratively and robustly abstracted into cuboids. Finally, the parameters of each cuboid are adjusted to be close to the original mesh model, and a lightweight polygonal mesh model is taken from the adjusted cuboid. Typical buildings and a whole scene of photogrammetric mesh models are exploited to assess the proposed method quantitatively and qualitatively. The obtained results reveal that the proposed method can derive a regular façade model from a photogrammetric mesh model with a certain accuracy.

Detecting and Analyzing Urban Centers Based on the Localized Contour Tree Method Using Taxi Trajectory Data: A Case Study of Shanghai

Urban structure is of vital importance to urban planning, transportation, economics and other applications. Since detecting and analyzing urban centers is crucial for understanding urban structure, a large number of studies on urban center extraction have been performed. In this paper, we propose an analysis framework to identify urban centers by using taxi trajectory data. The proposed approach differs from previous methods by employing a novel way to simulate taxi trajectory data with the topographic surface. We extracted pick-up and drop-off spots from taxi trajectory data and employed the localized contour tree method to delineate the boundaries and hierarchies of urban centers. The experiments show that the proposed method can successfully detect urban centers and analyze their temporal patterns in different periods in Shanghai, China.

Study on Optimization Method of Spatial Structure Characteristics of Peak Forest Landform in Wulingyuan Scenic Area

To protect the ecological environment of peak forest landform and maintain its integrity and stability, the optimization method of spatial structure characteristics of peak forest landform in Wulingyuan Scenic Area was studied. By using GIS and digital topographic analysis to study the basic features of sandstone peak forest landform, Wulingyuan peak forest landform and karst peak forest have great differences in lithological composition, weathering resistance is better than Cheltenham Badland landform; by using tree theory to analyze the features of Wulingyuan peak forest landform, according to area weight serialization of contour tree nodes, we can know the depression area. The karstification is stronger than that of the peak forest area, and the surface is relatively fragmented. Based on different landscape indices, the landscape pattern of Wulingyuan peak forest is analyzed. The fragmentation degree of vegetation is lower, and the fragmentation degree of building landscape is first increased, then decreased, and finally intensified. The proportion of artificial landscape decreases year by year and the trend of fragmentation is obvious. Based on the present situation of spatial structure characteristics of Wulingyuan peak forest landform, optimization methods such as combining centralization with decentralization and improving the quality of artificial landscape ecosystem were put forward.

Detecting Urban Polycentric Structure from POI Data

It is meaningful to analyze urban spatial structure by identifying urban subcenters, and many methods of doing so have been proposed in the published literature. Although these methods are widely applied, they exhibit obvious shortcomings that limit their further application. Therefore, it is of great value to propose a new urban subcenter identification method that can overcome these shortcomings. In this paper, we propose the density contour tree (DCT) method for detecting urban polycentric structures and their spatial distributions. Conceptually, this method is based on an analogy between urban spatial structure and terrain. The point-of-interest (POI) density is visualized as a continuous mathematical surface representing the urban terrain. Peaks represent the regions of the most frequent human activity, valleys represent regions with small population densities in the city, and slopes represent spatial changes in urban land-use intensity. Using this method, we have detected the urban “polycentric” structure of Beijing and determined the corresponding spatial relationships. In addition, several important properties of the urban centers have been identified. For example, Beijing has a typical urban polycentric structure with an urban center area accounting for 5.9% of the total urban area, and most of the urban centers in Beijing serve comprehensive functions. In general, the method and the results can serve as references for the later research on analyzing urban structure.