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N. Guo
Department of Computer Science, University of Minnesota, Minneapolis 55455, US

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Journal article
Published: 16 November 2019 in ISPRS International Journal of Geo-Information
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Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model which takes both interpolation error and positioning error into account by making use of motion features of trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called UTSM based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers but also more tolerant of different sample frequencies and asynchronous sampling of trajectories.

ACS Style

Ning Guo; Shashi Shekhar; Wei Xiong; Luo Chen; Ning Jing. UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model. ISPRS International Journal of Geo-Information 2019, 8, 518 .

AMA Style

Ning Guo, Shashi Shekhar, Wei Xiong, Luo Chen, Ning Jing. UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model. ISPRS International Journal of Geo-Information. 2019; 8 (11):518.

Chicago/Turabian Style

Ning Guo; Shashi Shekhar; Wei Xiong; Luo Chen; Ning Jing. 2019. "UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model." ISPRS International Journal of Geo-Information 8, no. 11: 518.

Journal article
Published: 24 October 2019 in ISPRS International Journal of Geo-Information
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Road networks play a significant role in modern city management. It is necessary to continually extract current road structure, as it changes rapidly with the development of the city. Due to the success of semantic segmentation based on deep learning in the application of computer vision, extracting road networks from VHR (Very High Resolution) imagery becomes a method of updating geographic databases. The major shortcoming of deep learning methods for road networks extraction is that they need a massive amount of high quality pixel-wise training datasets, which is hard to obtain. Meanwhile, a large amount of different types of VGI (volunteer geographic information) data including road centerline has been accumulated in the past few decades. However, most road centerlines in VGI data lack precise width information and, therefore, cannot be directly applied to conventional supervised deep learning models. In this paper, we propose a novel weakly supervised method to extract road networks from VHR images using only the OSM (OpenStreetMap) road centerline as training data instead of high quality pixel-wise road width label. Large amounts of paired Google Earth images and OSM data are used to validate the approach. The results show that the proposed method can extract road networks from the VHR images both accurately and effectively without using pixel-wise road training data.

ACS Style

Songbing Wu; Chun Du; Hao Chen; Yingxiao Xu; Ning Guo; Ning Jing. Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline. ISPRS International Journal of Geo-Information 2019, 8, 478 .

AMA Style

Songbing Wu, Chun Du, Hao Chen, Yingxiao Xu, Ning Guo, Ning Jing. Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline. ISPRS International Journal of Geo-Information. 2019; 8 (11):478.

Chicago/Turabian Style

Songbing Wu; Chun Du; Hao Chen; Yingxiao Xu; Ning Guo; Ning Jing. 2019. "Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline." ISPRS International Journal of Geo-Information 8, no. 11: 478.

Journal article
Published: 17 July 2019 in IEEE Access
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Online visualization and query of massive geo-spatial data are facing increasing challenges with the explosive growth of location-based spatial datasets. In the practical scenario, online visualization is carried out in a progressive way, namely, a sketchy view map is first presented, and more detailed view maps are produced gradually as the viewport scale goes deeper. One approach is to use the multi-scale spatial index technique. However, it loses the original data attribute and cannot provide spatial statistics information. The paper is to provide an improved index structure, the Geo-Gap tree, which aims to enhance online interactive access to large spatial datasets, as well as enable one to compute statistical attributes like aggregation at the coarse level. Therefore, the first focus of Geo-Gap tree is improving the efficiency of tree building. For this purpose, an adaptive geohash coding is introduced to reduce the computing of neighboring objects. And, this phase can be improved in parallel once objects are partitioned. Compare to Gap tree, the cost of building the Geo-Gap tree can be greatly reduced. The second contribution is to choose data at different level based on sampling so that a sample for each level can be served as a progressive query result. The third contribution is an estimation of progressive query results, which ensure that progressive query accuracy can be controlled within the range of theoretical analysis. With the query continuing to execute, the query results become more and more accurate. The method is now integrated successfully into a high-performance geographic information system called HiGIS.

ACS Style

Wei Xiong; Ruiqing Li; Jin Peng; Ye Wu; Ning Guo; Ning Jing. Geo-Gap Tree: A Progressive Query and Visualization Method for Massive Spatial Data. IEEE Access 2019, 7, 99428 -99440.

AMA Style

Wei Xiong, Ruiqing Li, Jin Peng, Ye Wu, Ning Guo, Ning Jing. Geo-Gap Tree: A Progressive Query and Visualization Method for Massive Spatial Data. IEEE Access. 2019; 7 ():99428-99440.

Chicago/Turabian Style

Wei Xiong; Ruiqing Li; Jin Peng; Ye Wu; Ning Guo; Ning Jing. 2019. "Geo-Gap Tree: A Progressive Query and Visualization Method for Massive Spatial Data." IEEE Access 7, no. : 99428-99440.

Journal article
Published: 22 March 2019 in IEEE Access
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Geographic meshing system is an essential technology in digital earth framework and has essential applications in the integration and organization of heterogeneous spatial data, along with corresponding coding method. But current meshing and coding methods show unsatisfying locality and performance. To make an improvement, an adaptive Hilbert-Geohash meshing and coding method called AHG is proposed, which could represent both the location and the approximate size of the coded object directly by the meshing hierarchy and the corresponding coding length. This unique feature helps to accelerate the spatial range query and neighbor query. By simple string operations, many candidate objects that do not meet the query criteria can be quickly filtered out without precise spatial calculation. In addition, AHG code can also support spatial size query by finding objects whose size is within a certain interval quickly, without calculating the precise size of each object, which brings great convenience to spatial size statistics of massive spatial dataset. Demonstrated by experiments over different types of spatial dataset in a common PC environment, AHG shows favorable stability and scalability besides its capability in accelerating spatial query. The method is now applied successfully in several spatial query tools in a high-performance geographic information system called HiGIS.

ACS Style

Ning Guo; Wei Xiong; Ye Wu; Luo Chen; Ning Jing. A Geographic Meshing and Coding Method Based on Adaptive Hilbert-Geohash. IEEE Access 2019, 7, 39815 -39825.

AMA Style

Ning Guo, Wei Xiong, Ye Wu, Luo Chen, Ning Jing. A Geographic Meshing and Coding Method Based on Adaptive Hilbert-Geohash. IEEE Access. 2019; 7 (99):39815-39825.

Chicago/Turabian Style

Ning Guo; Wei Xiong; Ye Wu; Luo Chen; Ning Jing. 2019. "A Geographic Meshing and Coding Method Based on Adaptive Hilbert-Geohash." IEEE Access 7, no. 99: 39815-39825.

Conference paper
Published: 06 November 2018 in Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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ACS Style

Mengyu Ma; Wei Xiong; Luo Chen; Ning Guo; Jun Li; Ning Jing. HiAccess. Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data 2018, 28 -31.

AMA Style

Mengyu Ma, Wei Xiong, Luo Chen, Ning Guo, Jun Li, Ning Jing. HiAccess. Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data. 2018; ():28-31.

Chicago/Turabian Style

Mengyu Ma; Wei Xiong; Luo Chen; Ning Guo; Jun Li; Ning Jing. 2018. "HiAccess." Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data , no. : 28-31.

Journal article
Published: 17 September 2018 in IEEE Access
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Accessibility is an important issue in transport geography, land planning, and many other related fields. Accessibility problems become computationally demanding when involving high-resolution requirements. Using conventional methods, providing high-resolution accessibility analysis for real-time decision support remains a challenge. In this paper, we present a parallel processing model, named HiAccess, to solve the high-resolution accessibility analysis problems in real time. One feature of HiAccess is a fast road network construction method, in which the road network topology is determined by traversing the original road nodes only once. The parallel strategies of HiAccess are fully optimized with few repeated computations. Moreover, a simple, efficient, and highly effective map generalization method is proposed to reduce computation load without an accuracy loss. The flexibility of HiAccess enables it to work well when applied to different accessibility analysis models. To further demonstrate the applicability of HiAccess, a case study of settlement sites selection for poverty alleviation in Xiangxi, Central China, is carried out. The accessibility of jobs, health care, educational resources, and other public facilities are comprehensively analyzed for settlement sites selection. HiAccess demonstrates the striking performance of measuring high-resolution (using $100~\text {m} \times 100~\text {m}$ grids) accessibility of a city (in total over 250k grids, roads with 232k segments, and 40 facilities) in 1 sec without preprocessing, while ArcGIS takes nearly 1 h to achieve a less satisfactory result. In additional experiments, HiAccess is tested on much larger data sets with excellent performance.

ACS Style

Mengyu Ma; Ye Wu; Ning Guo; Luo Chen; Qi Gong; Jun Li. A Parallel Processing Model for Accelerating High-Resolution Geo-Spatial Accessibility Analysis. IEEE Access 2018, 6, 52936 -52952.

AMA Style

Mengyu Ma, Ye Wu, Ning Guo, Luo Chen, Qi Gong, Jun Li. A Parallel Processing Model for Accelerating High-Resolution Geo-Spatial Accessibility Analysis. IEEE Access. 2018; 6 ():52936-52952.

Chicago/Turabian Style

Mengyu Ma; Ye Wu; Ning Guo; Luo Chen; Qi Gong; Jun Li. 2018. "A Parallel Processing Model for Accelerating High-Resolution Geo-Spatial Accessibility Analysis." IEEE Access 6, no. : 52936-52952.

Conference paper
Published: 01 June 2018 in 2018 26th International Conference on Geoinformatics
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Accessibility is an important issue in transport geography, land planning and many other related fields. Accessibility problems become computationally demanding when involving high-resolution requirements. Using conventional methods, providing high-resolution accessibility analysis for real time decision support remains a challenge. In this paper, we present a parallel method, named HiAccess, to solve the high-resolution accessibility analysis problems in real time. Pointing at accelerating accessibility analysis, we proposed an extended road network structure. Correspondingly, a fast road network construction method is proposed, in which the road network topology is determined by traversing the original road nodes only once. The parallel strategies of HiAccess are fully optimized through theoretical analysis and experimental comparisons. HiAccess demonstrates the striking performance of measuring high-resolution (using 100 m×100m grids) accessibility of a city (in total over 250k grids, roads with 232k segments, 40 facilities) in 1 second without preprocessing, while ArcGIS takes nearly 1 hour to achieve a less satisfactory result.

ACS Style

Mengyu Ma; Ye Wu; Ning Guo; Luo Chen; Qi Gong; Jun Li. HiAccess: A Parallel Method for Measuring High-Resolution Spatial Accessibility in Real Time. 2018 26th International Conference on Geoinformatics 2018, 1 -6.

AMA Style

Mengyu Ma, Ye Wu, Ning Guo, Luo Chen, Qi Gong, Jun Li. HiAccess: A Parallel Method for Measuring High-Resolution Spatial Accessibility in Real Time. 2018 26th International Conference on Geoinformatics. 2018; ():1-6.

Chicago/Turabian Style

Mengyu Ma; Ye Wu; Ning Guo; Luo Chen; Qi Gong; Jun Li. 2018. "HiAccess: A Parallel Method for Measuring High-Resolution Spatial Accessibility in Real Time." 2018 26th International Conference on Geoinformatics , no. : 1-6.

Conference paper
Published: 01 June 2018 in 2018 26th International Conference on Geoinformatics
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We interpret the geographic world as a combination of geographic entities (such as city, road, river). Using these geographic entities as the basic granularity for description, storage and manipulation, therefore, can provide a bridge between conceptual understanding and practical application. However, the issue arises of how to design a data model that is generic enough for the geographic entities when the data can change spatially and temporally. This difficulty is compounded when there is a need for geographic inference. In this paper, we offer a feasible solution by presenting a Spatio-Temporal Data Model of Geographic Entities (STDMGE), which describes each geographic entity as geographic object part and geographic relation part, both of which are well designed to organize the multi-source data (traditional geospatial data, sensor data, relationship data among geographic entities, etc.). Spatial-temporal changes can be accurately expressed and efficiently implemented using this model. Moreover, the formal description of geographic rules and events are incorporated in the data model for the possible future requirements of geographic reasoning. We further illustrate the data model by applying it to the case study of the river data.

ACS Style

Qi Gong; Ning Guo; Wei Xiong; Luo Chen; Ning Jing. A Spatio-Temporal Data Model of Geographic Entities. 2018 26th International Conference on Geoinformatics 2018, 1 -6.

AMA Style

Qi Gong, Ning Guo, Wei Xiong, Luo Chen, Ning Jing. A Spatio-Temporal Data Model of Geographic Entities. 2018 26th International Conference on Geoinformatics. 2018; ():1-6.

Chicago/Turabian Style

Qi Gong; Ning Guo; Wei Xiong; Luo Chen; Ning Jing. 2018. "A Spatio-Temporal Data Model of Geographic Entities." 2018 26th International Conference on Geoinformatics , no. : 1-6.

Journal article
Published: 30 October 2017 in ISPRS International Journal of Geo-Information
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The processing and analysis of trajectories are the core of many location-based applications and services, while trajectory similarity is an essential concept regularly used. To address the time-consuming problem of similarity query, an efficient algorithm based on Fréchet distance called Ordered Coverage Judge (OCJ) is proposed, which could realize the filtering query with a given Fréchet distance threshold on large-scale trajectory datasets. The OCJ algorithm can obtain the result set quickly by a two-step operation containing morphological characteristic filtering and ordered coverage judgment. The algorithm is expedient to be implemented in parallel for further increases of speed. Demonstrated by experiments over real trajectory data in a multi-core hardware environment, the new algorithm shows favorable stability and scalability besides its higher efficiency in comparison with traditional serial algorithms and other Fréchet distance algorithms.

ACS Style

Ning Guo; Mengyu Ma; Wei Xiong; Luo Chen; Ning Jing. An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold. ISPRS International Journal of Geo-Information 2017, 6, 326 .

AMA Style

Ning Guo, Mengyu Ma, Wei Xiong, Luo Chen, Ning Jing. An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold. ISPRS International Journal of Geo-Information. 2017; 6 (11):326.

Chicago/Turabian Style

Ning Guo; Mengyu Ma; Wei Xiong; Luo Chen; Ning Jing. 2017. "An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold." ISPRS International Journal of Geo-Information 6, no. 11: 326.

Conference paper
Published: 29 September 2016 in 2016 24th International Conference on Geoinformatics
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As a critical component of Location-Based Service(LBS), reverse geocoding is the process of converting a coordinate obtained by GPS to a readable street address. With the widespread use of location-aware mobile electronic devices and the extensive application of LBS, the capability of reverse geocoding is in high demand. Meanwhile, with the constantly expanding of spatial data size, the computing quantity of reverse geocoding continues to increase. Based on Global Subdivision Model(GSM), this paper presents an efficient reverse geocoding method named Geohash-based reverse geocoding(GBRG). In GBRG, spatial objects are encoded to Geohash codes which can be used to identify the location of the spatial objects. A key point in this step is encoding spatial line objects and region objects with an adaptive Geohash method. Then the spatial objects near the given coordinate are retrieved by comparing the Geohash codes. In order to make sure that the nearest spatial objects are in the scope of the results retrieved, we use the eight neighborhood for retrieval. PostGIS provides a lot of functions for geometric calculation and we use these functions to determine the spatial relations between the given coordinate and the spatial objects. We carry out some experiments using the data of Beijing, China. The results show that GBRG is of high efficiency with some loss in accuracy which can be ignored without obvious influences.

ACS Style

Mengyu Ma; Zhinong Zhong; Ning Guo; Ning Jing; Wei Xiong. An efficient reverse geocoding method based on Global Subdivision Model. 2016 24th International Conference on Geoinformatics 2016, 1 -9.

AMA Style

Mengyu Ma, Zhinong Zhong, Ning Guo, Ning Jing, Wei Xiong. An efficient reverse geocoding method based on Global Subdivision Model. 2016 24th International Conference on Geoinformatics. 2016; ():1-9.

Chicago/Turabian Style

Mengyu Ma; Zhinong Zhong; Ning Guo; Ning Jing; Wei Xiong. 2016. "An efficient reverse geocoding method based on Global Subdivision Model." 2016 24th International Conference on Geoinformatics , no. : 1-9.

Journal article
Published: 01 January 2016 in Advances in Electrical and Computer Engineering
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Building tile-pyramids is an effective way for publishing and accessing the map visualization service of large-scale geospatial data in the web. But it is a time-consuming task in Geographic Information System (GIS) to build tile-pyramids using traditional methods. In this article, an adaptive multilevel tiles generation method is proposed, which first builds grid index for the geospatial raster dataset, and then generates tiles according to different hierarchy level numbers in the tile-pyramid. With the optimized map rendering engine implemented, a parallel tiles pyramid generation method for large-scale geospatial raster dataset is integrated into a high performance GIS platform. Proved by experiments, the new method shows acceptable applicability, stability and scalability besides its high efficiency

ACS Style

N. Guo; W. Xiong; Q. Wu; N. Jing. An Efficient Tile-Pyramids Building Method for Fast Visualization of Massive Geospatial Raster Datasets. Advances in Electrical and Computer Engineering 2016, 16, 3 -8.

AMA Style

N. Guo, W. Xiong, Q. Wu, N. Jing. An Efficient Tile-Pyramids Building Method for Fast Visualization of Massive Geospatial Raster Datasets. Advances in Electrical and Computer Engineering. 2016; 16 (4):3-8.

Chicago/Turabian Style

N. Guo; W. Xiong; Q. Wu; N. Jing. 2016. "An Efficient Tile-Pyramids Building Method for Fast Visualization of Massive Geospatial Raster Datasets." Advances in Electrical and Computer Engineering 16, no. 4: 3-8.