Key Laboratory of Urban Land Resources Monitoring and Simulation

Shenzhen, China

Key Laboratory of Urban Land Resources Monitoring and Simulation

Shenzhen, China
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Wang S.,Key Laboratory of Urban Land Resources Monitoring and Simulation | Wang S.,University of Electronic Science and Technology of China | Zheng Z.,University of Electronic Science and Technology of China | Pu C.,University of Electronic Science and Technology of China | And 5 more authors.
Communications in Computer and Information Science | Year: 2017

Vector data contains a lot of important features. Progressive transmission is a key technology to solve the real-time rendering and network transmission of vector data. By studying the traditional progressive transmission method of vector data and considering the spatial position and geometric features of vector data, we proposed an efficient progressive transmission method. We divided the vector data into blocks based on spatial location, then applied a Visvalingam-Whyatt algorithm to build a multi-scale model. Finally the progressive transmission of vector data was achieved. Our method satisfies the viewer’s needs to display data from different rendering scale and has important significance for client users to interact in real time. © Springer Nature Singapore Pte Ltd. 2017.

Wang Z.,Shenzhen University | Wang Z.,Key Laboratory of Urban Land Resources Monitoring and Simulation | Yue Y.,Shenzhen University | Li Q.,Shenzhen University | And 2 more authors.
ISPRS International Journal of Geo-Information | Year: 2017

The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs) for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS) are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research.

Li X.,CAS Shenzhen Institutes of Advanced Technology | Li X.,Key Laboratory of Urban Land Resources Monitoring and Simulation | Lv Z.,CAS Shenzhen Institutes of Advanced Technology | Hu J.,CAS Shenzhen Institutes of Advanced Technology | And 3 more authors.
2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015 | Year: 2015

A 3D Shenzhen city web platform based on WE-BVRGIS is presented. A 3D globe browser is employed to load all kinds of demanded data of the city, such as 3D building model data, residents information, real-time and historical traffic data. Using these data, the 3D analysis and visualization of the concerned city massive information are conducted in the platform. All the presented functions of the platform are extracted from the practical customer demand. The system design has considered some existing Geographic human-computer interaction (GeoHCI) research results. © 2015 IEEE.

Li X.,Key Laboratory of Urban Land Resources Monitoring and Simulation | Li X.,CAS Shenzhen Institutes of Advanced Technology | Lv Z.,CAS Shenzhen Institutes of Advanced Technology | Wang W.,Key Laboratory of Urban Land Resources Monitoring and Simulation | And 2 more authors.
International Conference on Geoinformatics | Year: 2016

City traffic data has several characteristics, such as large scale, diverse, predictable, and real-time, which falls in the range of definition of big data. This paper proposed a cloud service platform, that targets wise transportation in order to carry out unified management and mining analysis of the huge number of the multivariate and heterogeneous dynamic transportation information, provides real-time transportation information, increases the utilization efficiency of transportation, promotes transportation management and service level of travel information, and provides decision support of transportation management by virtual reality as visual. © 2015 IEEE.

Lv Z.,CAS Shenzhen Institutes of Advanced Technology | Li X.,CAS Shenzhen Institutes of Advanced Technology | Li X.,Key Laboratory of Urban Land Resources Monitoring and Simulation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

A virtual reality based enhanced technology for learning primary geography is proposed, which synthesizes several latest information technologies including virtual reality(VR), 3D geographical information system(GIS), 3D visualization and multimodal humancomputer- interaction (HCI). The main functions of the proposed system are introduced, i.e. Buffer analysis, Overlay analysis, Space convex hull calculation, Space convex decomposition, 3D topology analysis and 3D space intersection detection. The multimodal technologies are employed in the system to enhance the immersive perception of the users. © Springer International Publishing Switzerland 2016.

Jiang R.,Shenzhen Urban Planning and Land Resource Research Center | Jiang R.,Key Laboratory of Urban Land Resources Monitoring and Simulation | Wang C.,Shenzhen Urban Planning and Land Resource Research Center | Shen L.,Shenzhen Urban Planning and Land Resource Research Center | Wang P.,Nanjing University of Information Science and Technology
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2016

As high spatial resolution remotely sensed image be acquired more easily, there is a great potential for obtaining forest inventory automatically and cost-efficiently. A method was proposed to detect the lichee's treetop and delineate tree-crown. The method can be divided into three steps. In the first step, a 3×3 mean filter was utilized to smooth image, and then the image was inverted through subtracting image from the maximum of the filtered image. The second step was individual tree detection, namely treetop detection. The inverted image can be viewed as a topographic surface, the flow direction grid was built and then the depressions grid was extracted. The depressions distributed on roads and constructions were deleted according to the predefined threshold. Watersheds were delineated to obtain the contributing area of depressions viewing depressions as the pour point. For solving that the multiple depressions were erroneously identified within the same crown, the depressions were deleted if the distance to the nearest depression was less than threshold and the mean value of depression in the filtered image was not the maximum in multiple depressions, the watersheds of multiple depressions were merged. The remaining depressions were viewed as the detected treetop. The third step was to delineate the tree-crown by using region growing method. The remaining depressions were used for seed points, crown regions were expanded from depression to surrounding pixels until the difference between the pixel and mean value of depression exceeded the predefined threshold or to the boundary of depression watershed. A 324 pixel×483 pixel Pléiades image with 0.5 m resolution was employed to test the method. A promising agreement between the detected results and manual delineation results was achieved in counting the number of trees and the area of delineating tree crowns. For individual tree detection, the overall accuracy was 87.75%, user's accuracy was 80.69%, producer's accuracy was 96.06%; for individual tree-crow delineation, the overall accuracy was 78.69%, user's accuracy was 71.32%, producer's accuracy was 87.76%. © 2016, Chinese Society of Agricultural Machinery. All right reserved.

Wu X.,Wuhan University | Wu X.,Key Laboratory of Urban Land Resources Monitoring and Simulation | Chen X.,Key Laboratory of Urban Land Resources Monitoring and Simulation | Zhan F.B.,Texas State University | Hong S.,Wuhan University
Landslides | Year: 2015

A bibliometric analysis was conducted to evaluate landslide research from different perspectives during the period 1991–2014 based on the Science Citation Index-Expanded and Social Sciences Citation Index databases. Based on a sample of 10,567 articles that were related to landslides, the bibliometric analysis revealed the scientific outputs, science categories, source titles, global geographical distribution of the authors, productive authors, international collaborations, institutions, and temporal evolution of keyword frequencies. Landslide-related research has undergone notable growth during the past two decades. Multidisciplinary Geosciences, Geological Engineering, and Water Resources were the three major science categories, and Geomorphology was the most active journal during the surveyed period. The major author clusters and research regions are located in North America, Western Europe, and East Asia. The USA was a leading contributor to global landslide research, with the most independent and collaborative articles, and its dominance was also confirmed in the national/regional collaboration network. The Chinese Academy of Sciences, US Geological Survey, and Italian National Research Council were the three major contributing institutions. Guzzetti F from the Italian National Research Council was the most productive author, with the most high-quality articles. A keyword analysis found that landslide susceptibility assessment, rainfall- and earthquake-induced landslide stability, and effective research technologies and methods were consistent topics that attracted the most attention during the study period. Several keywords, such as “landslide susceptibility”, “earthquake”, “GIS”, “remote sensing”, and “logistic regression”, received dramatically increased attention during the study period, possibly signalling future research trends. © 2015 Springer-Verlag Berlin Heidelberg

Wu X.,Wuhan University | Wu X.,Key Laboratory of Urban Land Resources Monitoring and Simulation | Benjamin Zhan F.,Texas State University | Zhang K.,Wuhan University | Deng Q.,Wuhan University
Environmental Earth Sciences | Year: 2016

Several extensive landslides have occurred in the vicinity of the Three Gorges Reservoir since its initial impoundment in June 2003. A reduction of the landslide risk is essential for the safety and security of lives and property in the region. This study analyses the deformation states of two typical colluvial landslides (the Baijiabao and Laoshewo landslides) using 6 years of monitoring data, a two-step cluster analysis, and the Apriori algorithm. The landslide displacement versus time curves exhibit step-like patterns, and the landslide deformation is highly correlated with fluctuations in the reservoir level and seasonal precipitation. To determine different types of landslide deformation, the monthly displacement curves of the colluvial landslides are classified into three types using a two-step cluster analysis: initial deformation, constant deformation, and rapid deformation. Five driving factors were selected as the antecedents for the Apriori algorithm to obtain rules that describe the relationships between the landslide deformation and the influential parameters. These factors include the cumulative precipitation over the previous month, the maximum daily precipitation during the current month, changes in the reservoir level over the previous month, cumulative increases in the reservoir level and the average reservoir level during the current month. The analytical results were validated by comparing them with observed landslide deformation characteristics using three measurement standards: support, confidence and lift. The results show that the combined method of a two-step cluster analysis with the Apriori algorithm can effectively model the landslide deformation states that are associated with the Baijiabao and Laoshewo landslides. Moreover, this method may serve as a potential reference for deformation analyses of colluvial landslides in the Three Gorges. © 2015, Springer-Verlag Berlin Heidelberg.

Wu X.,Wuhan University | Wu X.,Key Laboratory of Urban Land Resources Monitoring and Simulation | Shen S.,Key Laboratory of Urban Land Resources Monitoring and Simulation | Niu R.,Wuhan University
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2016

Landslide susceptibility prediction is the key technology in landslide monitoring, early warning, and assessment. The core problem in quantitative prediction of landslide hazards is the effective selection of conditioning factors and prediction models. In this paper, the Three Gorges Reservoir area was selected as a case study to predict landslide susceptibility. First,, key landslide-related factors were selected as input variables using topographic, geological, and remote sensing data. Secondly, according to the nonlinear and uncertainty characteristics of landslides, a PSO-SVM model was trained and used to assess landslide susceptibility. Finally, the prediction results of grid- and object-based prediction models were validated by comparing them with known landslides using the classification accuracy. The results show that object-based PSO-SVM possesses high prediction accuracy with the area under curve of 0.8415 and a Kappa coefficient of 0.8490. These experimental results are consistent with field investigations and can provide a reference for landslide prevention and reduction in the Three Gorges, China. © 2016, Wuhan University All right reserved.

Yu W.,Wuhan University | Ai T.,Key Laboratory of Urban Land Resources Monitoring and Simulation | Ai T.,Wuhan University | Shao S.,Wuhan University
Journal of Transport Geography | Year: 2015

Central Business District (CBD) is the core area of urban planning and decision management. The cartographic definition and representation of CBD is of great significance in studying the urban development and its functions. In order to facilitate these processes, the Kernel Density Estimation (KDE) is a very efficient tool as it considers the decay impact of services and allows the enrichment of the information from a very simple input scatter plot to a smooth output density surface. However, most existing methods of density analysis consider geographic events in a homogeneous and isotropic space under Euclidean space representation. Considering the case that the physical movement in the urban environment is usually constrained by a street network, we propose a different method for the delimitation of CBD with network configurations. First, starting from the locations of central activities, a concentration index is presented to visualize the functional urban environment by means of a density surface, which is refined with network distances rather than Euclidean ones. Then considering the specialties of network distance computation problem, an efficient way supported by flow extension simulation is proposed. Taking Shenzhen and Guangzhou, two quite developed cities in China as two case studies, we demonstrate the easy implementation and practicability of our method in delineating CBD. © 2015 Elsevier Ltd.

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