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Hongchao Fan
Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway

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Journal article
Published: 07 July 2021 in International Journal of Geographical Information Science
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Extracting precise location information from microblogs is a crucial task in many applications, particularly in disaster response, revealing where damages are, where people need assistance, and where help can be found. A crucial prerequisite to location extraction is place name extraction. In this paper, we present GazPNE: a hybrid approach to place name extraction which fuses rules, gazetteers, and deep learning techniques without requiring any manually annotated data. The core of the approach is to learn the intrinsic characteristics of multi-word place names with deep learning from gazetteers. Specifically, GazPNE consists of a rule-based system to select n-grams from the microblogs that potentially contain place names, and a C-LSTM model that decides if the selected n-gram is a place name or not. The C-LSTM is trained on 388.1 million examples containing 6.8 million positive examples with US and Indian place names extracted from OpenStreetMap and 381.3 million negative examples synthesized by rules. We evaluate GazPNE against the SoTA on a manually annotated 4,500 tweet dataset which contains 9,026 place names from three foods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). GazPNE achieves SotA performance on the test data with an F1 of 0.84.

ACS Style

Xuke Hu; Hussein S. Al-Olimat; Jens Kersten; Matti Wiegmann; Friederike Klan; Yeran Sun; Hongchao Fan. GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules. International Journal of Geographical Information Science 2021, 1 -28.

AMA Style

Xuke Hu, Hussein S. Al-Olimat, Jens Kersten, Matti Wiegmann, Friederike Klan, Yeran Sun, Hongchao Fan. GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules. International Journal of Geographical Information Science. 2021; ():1-28.

Chicago/Turabian Style

Xuke Hu; Hussein S. Al-Olimat; Jens Kersten; Matti Wiegmann; Friederike Klan; Yeran Sun; Hongchao Fan. 2021. "GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules." International Journal of Geographical Information Science , no. : 1-28.

Research article
Published: 14 May 2021 in Geo-spatial Information Science
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Building façades can feature different patterns depending on the architectural style, functionality, and size of the buildings; therefore, reconstructing these façades can be complicated. In particular, when semantic façades are reconstructed from point cloud data, uneven point density and noise make it difficult to accurately determine the façade structure. When investigating façade layouts, Gestalt principles can be applied to cluster visually similar floors and façade elements, allowing for a more intuitive interpretation of façade structures. We propose a novel model for describing façade structures, namely the layout graph model, which involves a compound graph with two structure levels. In the proposed model, similar façade elements such as windows are first grouped into clusters. A down-layout graph is then formed using this cluster as a node and by combining intra- and inter-cluster spacings as the edges. Second, a top-layout graph is formed by clustering similar floors. By extracting relevant parameters from this model, we transform semantic façade reconstruction to an optimization strategy using simulated annealing coupled with Gibbs sampling. Multiple façade point cloud data with different features were selected from three datasets to verify the effectiveness of this method. The experimental results show that the proposed method achieves an average accuracy of 86.35%. Owing to its flexibility, the proposed layout graph model can deal with different types of façades and qualities of point cloud data, enabling a more robust and accurate reconstruction of façade models.

ACS Style

Hongchao Fan; Yuefeng Wang; Jianya Gong. Layout graph model for semantic façade reconstruction using laser point clouds. Geo-spatial Information Science 2021, 1 -19.

AMA Style

Hongchao Fan, Yuefeng Wang, Jianya Gong. Layout graph model for semantic façade reconstruction using laser point clouds. Geo-spatial Information Science. 2021; ():1-19.

Chicago/Turabian Style

Hongchao Fan; Yuefeng Wang; Jianya Gong. 2021. "Layout graph model for semantic façade reconstruction using laser point clouds." Geo-spatial Information Science , no. : 1-19.

Journal article
Published: 28 April 2021 in ISPRS International Journal of Geo-Information
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In spatial analysis applications, measuring the shape similarity of polygons is crucial for polygonal object retrieval and shape clustering. As a complex cognition process, measuring shape similarity should involve finding the difference between polygons, as objects in observation, in terms of visual perception and the differences of the regions, boundaries, and structures formed by the polygons from a mathematical point of view. In existing approaches, the shape similarity of polygons is calculated by only comparing their mathematical characteristics while not taking human perception into consideration. Aiming to solve this problem, we use the features of context and texture of polygons, since they are basic visual perception elements, to fit the cognition purpose. In this paper, we propose a contour diffusion method for the similarity measurement of polygons. By converting a polygon into a grid representation, the contour feature is represented as a multiscale statistic feature, and the region feature is transformed into condensed grid of context features. Instead of treating shape similarity as a distance between two representations of polygons, the proposed method observes similarity as a correlation between textures extracted by shape features. The experiments show that the accuracy of the proposed method is superior to that of the turning function and Fourier descriptor.

ACS Style

Hongchao Fan; Zhiyao Zhao; Wenwen Li. Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors. ISPRS International Journal of Geo-Information 2021, 10, 279 .

AMA Style

Hongchao Fan, Zhiyao Zhao, Wenwen Li. Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors. ISPRS International Journal of Geo-Information. 2021; 10 (5):279.

Chicago/Turabian Style

Hongchao Fan; Zhiyao Zhao; Wenwen Li. 2021. "Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors." ISPRS International Journal of Geo-Information 10, no. 5: 279.

Journal article
Published: 17 April 2021 in Computers, Environment and Urban Systems
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Nowadays, biking is flourishing in many Western cities. While many roads are used for both cars and bicycles, buffered bike lanes are marked for the safety of cyclists. In many cities, segregated paths are built up to have physical separation from motor vehicles. These types of biking ways are regarded as attributes in geographic information system (GIS) data. This information is required and important in the service of route planning, as cyclists may prefer certain types of bikeways. This paper presents a framework for generating networks of bikeways with attribute information from the data collected on the collaborative street view data platform Mapillary. The framework consists of two layers: The first layer focuses on constructing a bikeway road network using Global Positioning System (GPS) information of Mapillary images. Mapillary sequences are classified into walking, cycling, driving (ordinary road), and driving (motorway) trajectories based on the transportation mode with a trained XGBoost classifier. The bikeway road network is then extracted from cycling and driving (ordinary road) trajectories using a raster-based method. The second layer focuses on extracting attribute information from Mapillary images. Cycling-specific information (i.e., bicycle signs/markings) is extracted using a two-stage detection and classification model. A series of quantitative evaluations based on a case study demonstrated the ability and potential of the framework for extracting bikeway road information to enrich the existing OSM cycling road data.

ACS Style

Xuan Ding; Hongchao Fan; Jianya Gong. Towards generating network of bikeways from Mapillary data. Computers, Environment and Urban Systems 2021, 88, 101632 .

AMA Style

Xuan Ding, Hongchao Fan, Jianya Gong. Towards generating network of bikeways from Mapillary data. Computers, Environment and Urban Systems. 2021; 88 ():101632.

Chicago/Turabian Style

Xuan Ding; Hongchao Fan; Jianya Gong. 2021. "Towards generating network of bikeways from Mapillary data." Computers, Environment and Urban Systems 88, no. : 101632.

Journal article
Published: 02 April 2021 in ISPRS International Journal of Geo-Information
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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.

ACS Style

Mengqi Sun; Hongchao Fan. Detecting and Analyzing Urban Centers Based on the Localized Contour Tree Method Using Taxi Trajectory Data: A Case Study of Shanghai. ISPRS International Journal of Geo-Information 2021, 10, 220 .

AMA Style

Mengqi Sun, Hongchao Fan. Detecting and Analyzing Urban Centers Based on the Localized Contour Tree Method Using Taxi Trajectory Data: A Case Study of Shanghai. ISPRS International Journal of Geo-Information. 2021; 10 (4):220.

Chicago/Turabian Style

Mengqi Sun; Hongchao Fan. 2021. "Detecting and Analyzing Urban Centers Based on the Localized Contour Tree Method Using Taxi Trajectory Data: A Case Study of Shanghai." ISPRS International Journal of Geo-Information 10, no. 4: 220.

Journal article
Published: 30 March 2021 in IEEE Transactions on Intelligent Transportation Systems
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High-Accuracy and high-efficiency 3-D sensing and associated data processing techniques are urgently needed for today’s roadway inventory, infrastructure health monitoring, autonomous driving, connected vehicles, urban modeling, and smart cities. 3D geospatial data acquired by digital photogrammetry or laser scanning or LiDAR systems have become one of the most critical data sources to support the above-mentioned applications. While progress has been made to applying 3D sensory data to those applications related to intelligent transportation systems (ITS), such as road network extraction, platform localization, obstacle avoidance, high-definition map generation, and transportation infrastructure inventory, many essential questions remain regarding the processing and understanding such massive 3D datasets in ITS-related applications. The authors have selected four articles for review in this Special issue. A summary of these articles is outlined below.

ACS Style

Chenglu Wen; Ayman F. Habib; Jonathan Li; Charles K. Toth; Cheng Wang; Hongchao Fan. Special Issue on 3D Sensing in Intelligent Transportation. IEEE Transactions on Intelligent Transportation Systems 2021, 22, 1947 -1949.

AMA Style

Chenglu Wen, Ayman F. Habib, Jonathan Li, Charles K. Toth, Cheng Wang, Hongchao Fan. Special Issue on 3D Sensing in Intelligent Transportation. IEEE Transactions on Intelligent Transportation Systems. 2021; 22 (4):1947-1949.

Chicago/Turabian Style

Chenglu Wen; Ayman F. Habib; Jonathan Li; Charles K. Toth; Cheng Wang; Hongchao Fan. 2021. "Special Issue on 3D Sensing in Intelligent Transportation." IEEE Transactions on Intelligent Transportation Systems 22, no. 4: 1947-1949.

Journal article
Published: 16 February 2021 in ISPRS International Journal of Geo-Information
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In China, the traditional taxi industry is conforming to the trend of the times, with taxi drivers working with e-hailing applications. This reform is of great significance, not only for the taxi industry, but also for the transportation industry, cities, and society as a whole. Our goal was to analyze the changes in driving behavior since taxi drivers joined e-hailing platforms. Therefore, this paper mined taxi trajectory data from Shanghai and compared the data of May 2015 with those of May 2017 to represent the before-app stage and the full-use stage, respectively. By extracting two-trip events (i.e., vacant trip and occupied trip) and two-spot events (i.e., pick-up spot and drop-off spot), taxi driving behavior changes were analyzed temporally, spatially, and efficiently. The results reveal that e-hailing applications mine more long-distance rides and new pick-up locations for drivers. Moreover, driver initiative have increased at night since using e-hailing applications. Furthermore, mobile payment facilities save time that would otherwise be taken sorting out change. Although e-hailing apps can help citizens get taxis faster, from the driver's perspective, the apps do not reduce their cruising time. In general, e-hailing software reduces the unoccupied ratio of taxis and improves the operating ratio. Ultimately, new driving behaviors can increase the driver’s revenue. This work is meaningful for the formulation of reasonable traffic laws and for urban traffic decision-making.

ACS Style

Yitong Gan; Hongchao Fan; Wei Jiao; Mengqi Sun. Exploring the Influence of E-Hailing Applications on the Taxi Industry—from the Perspective of the Drivers. ISPRS International Journal of Geo-Information 2021, 10, 77 .

AMA Style

Yitong Gan, Hongchao Fan, Wei Jiao, Mengqi Sun. Exploring the Influence of E-Hailing Applications on the Taxi Industry—from the Perspective of the Drivers. ISPRS International Journal of Geo-Information. 2021; 10 (2):77.

Chicago/Turabian Style

Yitong Gan; Hongchao Fan; Wei Jiao; Mengqi Sun. 2021. "Exploring the Influence of E-Hailing Applications on the Taxi Industry—from the Perspective of the Drivers." ISPRS International Journal of Geo-Information 10, no. 2: 77.

Original research article
Published: 02 January 2021 in Big Earth Data
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The applications of 3D building models are limited as producing them requires massive labor and time costs as well as expensive devices. In this paper, we aim to propose a novel and web-based interactive platform, VGI3D, to overcome these challenges. The platform is designed to reconstruct 3D building models by using free images from internet users or volunteered geographic information (VGI) platform, even though not all these images are of high quality. Our interactive platform can effectively obtain each 3D building model from images in 30 seconds, with the help of user interaction module and convolutional neural network (CNN). The user interaction module provides the boundary of building facades for 3D building modeling. And this CNN can detect facade elements even though multiple architectural styles and complex scenes are within the images. Moreover, user interaction module is designed as simple as possible to make it easier to use for both of expert and non-expert users. Meanwhile, we conducted a usability testing and collected feedback from participants to better optimize platform and user experience. In general, the usage of VGI data reduces labor and device costs, and CNN simplifies the process of elements extraction in 3D building modeling. Hence, our proposed platform offers a promising solution to the 3D modeling community.

ACS Style

Hongchao Fan; Gefei Kong; Chaoquan Zhang. An Interactive platform for low-cost 3D building modeling from VGI data using convolutional neural network. Big Earth Data 2021, 5, 49 -65.

AMA Style

Hongchao Fan, Gefei Kong, Chaoquan Zhang. An Interactive platform for low-cost 3D building modeling from VGI data using convolutional neural network. Big Earth Data. 2021; 5 (1):49-65.

Chicago/Turabian Style

Hongchao Fan; Gefei Kong; Chaoquan Zhang. 2021. "An Interactive platform for low-cost 3D building modeling from VGI data using convolutional neural network." Big Earth Data 5, no. 1: 49-65.

Editorial
Published: 02 January 2021 in Big Earth Data
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ACS Style

Songnian Li; Monica Wachowicz; Hongchao Fan. Analytics of big geosocial media and crowdsourced data. Big Earth Data 2021, 5, 1 -4.

AMA Style

Songnian Li, Monica Wachowicz, Hongchao Fan. Analytics of big geosocial media and crowdsourced data. Big Earth Data. 2021; 5 (1):1-4.

Chicago/Turabian Style

Songnian Li; Monica Wachowicz; Hongchao Fan. 2021. "Analytics of big geosocial media and crowdsourced data." Big Earth Data 5, no. 1: 1-4.

Journal article
Published: 23 November 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Façade parsing is an essential process before the 3-D modeling of digital or virtual 3-D city models. The existing grammar-based approaches for façade parsing rely on strong prior knowledge but can obtain façade parts with better structure. Pixelwise-segmentation-based approaches achieve façade parsing with much less knowledge but the resulting structure of façade parts is normally incomplete. Both these approaches are restricted by their high reliance on the data set. Therefore, they cannot be applied for façade parsing with complex scenes. To address this issue, we built a large street-level data set by taking Mapillary images as the training data for more general scenes. At the same time, we propose a new pipeline based on convolutional neural network (CNN) that combines pixelwise segmentation and global object detection to achieve better results for facade parsing. Our pipeline can be applied to façade images after rectification and street-level façade images with complex scenes. The result of the ablation study demonstrates that the design of our pipeline is effective. We test our pipeline on the classic ECP2011 data set and our new large street-level data set. Our pipeline achieves state-of-the-art results for both the data sets: an accuracy of 98.2% and the mean average precision (mAP) of 98.8% on the ECP2011 data set as well as the mAP of 81.1% for façade parts parsing on our street-level data set.

ACS Style

Gefei Kong; Hongchao Fan. Enhanced Facade Parsing for Street-Level Images Using Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -13.

AMA Style

Gefei Kong, Hongchao Fan. Enhanced Facade Parsing for Street-Level Images Using Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-13.

Chicago/Turabian Style

Gefei Kong; Hongchao Fan. 2020. "Enhanced Facade Parsing for Street-Level Images Using Convolutional Neural Networks." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-13.

Article
Published: 27 October 2020 in Journal of Geovisualization and Spatial Analysis
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Volunteered geographic information (VGI) has been widely explored by researchers for decision support in various application domains because the data are cost-effective to collect and their richness in volume and spatiotemporal coverage is unrivaled against traditional data sources. This study visualizes and analyzes a network of the authors of selected journal articles in GIScience about the first decade of VGI research. It uses the number of citations, one local network centrality measures (i.e., degree), and three global network centrality measures (i.e., closeness centrality, betweenness centrality, and eigenvector centrality) for quantifying the author importance. A new rule-based weighting method has also been developed for taking into account author sequences when computing the global centrality measures. Results show that the connectedness of the European researchers is strong, and Europe and North America have the highest numbers of prominent VGI researchers. Closeness among researchers does not seem to contribute heavily to the increase in citations. Rather, the number of direct connections in the network, the authors’ control over the network, and the quality of research connections is more important. European and North American authors as a whole play a leading role in the VGI research, but on average (per author influence) are only outstanding in terms of the citation numbers and have relatively more control over the network. Lastly, this study has revealed the relatively more diverse VGI research topics investigated over a longer time span in North America and Europe compared with other regions of the globe, highlighting the major problems that have been studied across the VGI research network.

ACS Style

Yingwei Yan; Dawei Ma; Wei Huang; Chen-Chieh Feng; Hongchao Fan; Yingbin Deng; Jianhui Xu. Volunteered Geographic Information Research in the First Decade: Visualizing and Analyzing the Author Connectedness of Selected Journal Articles in GIScience. Journal of Geovisualization and Spatial Analysis 2020, 4, 1 -13.

AMA Style

Yingwei Yan, Dawei Ma, Wei Huang, Chen-Chieh Feng, Hongchao Fan, Yingbin Deng, Jianhui Xu. Volunteered Geographic Information Research in the First Decade: Visualizing and Analyzing the Author Connectedness of Selected Journal Articles in GIScience. Journal of Geovisualization and Spatial Analysis. 2020; 4 (2):1-13.

Chicago/Turabian Style

Yingwei Yan; Dawei Ma; Wei Huang; Chen-Chieh Feng; Hongchao Fan; Yingbin Deng; Jianhui Xu. 2020. "Volunteered Geographic Information Research in the First Decade: Visualizing and Analyzing the Author Connectedness of Selected Journal Articles in GIScience." Journal of Geovisualization and Spatial Analysis 4, no. 2: 1-13.

Research article
Published: 19 August 2020 in Transactions in GIS
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Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but neglect room usage. To mitigate the issue, this work proposes a general room tagging method for public buildings, which can benefit both existing map providers and automatic mapping solutions by inferring the missing room usage based on indoor geometric maps. Two kinds of statistical learning‐based room tagging methods are adopted: traditional machine learning (e.g., random forests) and deep learning, specifically relational graph convolutional networks (R‐GCNs), based on the geometric properties (e.g., area), topological relationships (e.g., adjacency and inclusion), and spatial distribution characteristics of rooms. In the machine learning‐based approach, a bidirectional beam search strategy is proposed to deal with the issue that the tag of a room depends on the tag of its neighbors in an undirected room sequence. In the R‐GCN‐based approach, useful properties of neighboring nodes (rooms) in the graph are automatically gathered to classify the nodes. Research buildings are taken as examples to evaluate the proposed approaches based on 130 floor plans with 3,330 rooms by using fivefold cross‐validation. The experiments conducted show that the random forest‐based approach achieves a higher tagging accuracy (0.85) than R‐GCN (0.79).

ACS Style

Xuke Hu; Hongchao Fan; Alexey Noskov; Zhiyong Wang; Alexander Zipf; Fuqiang Gu; Jianga Shang. Room semantics inference using random forest and relational graph convolutional networks: A case study of research building. Transactions in GIS 2020, 25, 71 -111.

AMA Style

Xuke Hu, Hongchao Fan, Alexey Noskov, Zhiyong Wang, Alexander Zipf, Fuqiang Gu, Jianga Shang. Room semantics inference using random forest and relational graph convolutional networks: A case study of research building. Transactions in GIS. 2020; 25 (1):71-111.

Chicago/Turabian Style

Xuke Hu; Hongchao Fan; Alexey Noskov; Zhiyong Wang; Alexander Zipf; Fuqiang Gu; Jianga Shang. 2020. "Room semantics inference using random forest and relational graph convolutional networks: A case study of research building." Transactions in GIS 25, no. 1: 71-111.

Journal article
Published: 31 May 2020 in Sensors
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Similarity measurement is one of the key tasks in spatial data analysis. It has a great impact on applications i.e., position prediction, mining and analysis of social behavior pattern. Existing methods mainly focus on the exact matching of polylines which result in the trajectories. However, for the applications like travel/drive behavior analysis, even for objects passing by the same route the trajectories are not the same due to the accuracy of positioning and the fact that objects may move on different lanes of the road. Further, in most cases of spatial data mining, locations and sometimes sequences of locations on trajectories are most important, while how objects move from location to location (the exact geometries of trajectories) is of less interest. For the abovementioned situations, the existing approaches cannot work anymore. In this paper, we propose a grid aware approach to convert trajectories into sequences of codes, so that shape details of trajectories are neglected while emphasizing locations where trajectories pass through. Experiments with Shanghai Float Car Data (FCD) show that the proposed method can calculate trajectories with high similarity if these pass through the same locations. In addition, the proposed methods are very efficient since the data volume is considerably reduced when trajectories are converted into grid-codes.

ACS Style

Wei Jiao; Hongchao Fan; Terje Midtbø. A Grid-Based Approach for Measuring Similarities of Taxi Trajectories. Sensors 2020, 20, 1 .

AMA Style

Wei Jiao, Hongchao Fan, Terje Midtbø. A Grid-Based Approach for Measuring Similarities of Taxi Trajectories. Sensors. 2020; 20 (11):1.

Chicago/Turabian Style

Wei Jiao; Hongchao Fan; Terje Midtbø. 2020. "A Grid-Based Approach for Measuring Similarities of Taxi Trajectories." Sensors 20, no. 11: 1.

Review
Published: 04 April 2020 in ISPRS Journal of Photogrammetry and Remote Sensing
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This study had two main aims: (1) to provide a comprehensive review of terrestrial laser scanner (TLS) point cloud registration methods and a better understanding of their strengths and weaknesses; and (2) to provide a large-scale benchmark data set (Wuhan University TLS: Whu-TLS) to support the development of cutting-edge TLS point cloud registration methods, especially deep learning-based methods. In particular, we first conducted a thorough review of TLS point cloud registration methods in terms of pairwise coarse registration, pairwise fine registration, and multiview registration, as well as analyzing their strengths, weaknesses, and future research trends. We then reviewed the existing benchmark data sets (e.g., ETH Dataset and Robotic 3D Scanning Repository) for TLS point cloud registration and summarized their limitations. Finally, a new benchmark data set was assembled from 11 different environments (i.e., subway station, high-speed railway platform, mountain, forest, park, campus, residence, riverbank, heritage building, underground excavation, and tunnel environments) with variations in the point density, clutter, and occlusion. In addition, we summarized future research trends in this area, including auxiliary data-guided registration, deep learning-based registration, and multi-temporal point cloud registration.

ACS Style

Zhen Dong; Fuxun Liang; Bisheng Yang; Yusheng Xu; Yufu Zang; Jianping Li; Yuan Wang; Wenxia Dai; Hongchao Fan; Juha Hyyppä; Uwe Stilla. Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 163, 327 -342.

AMA Style

Zhen Dong, Fuxun Liang, Bisheng Yang, Yusheng Xu, Yufu Zang, Jianping Li, Yuan Wang, Wenxia Dai, Hongchao Fan, Juha Hyyppä, Uwe Stilla. Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 163 ():327-342.

Chicago/Turabian Style

Zhen Dong; Fuxun Liang; Bisheng Yang; Yusheng Xu; Yufu Zang; Jianping Li; Yuan Wang; Wenxia Dai; Hongchao Fan; Juha Hyyppä; Uwe Stilla. 2020. "Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark." ISPRS Journal of Photogrammetry and Remote Sensing 163, no. : 327-342.

Journal article
Published: 25 March 2020 in ISPRS International Journal of Geo-Information
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With the rapid development of urban traffic, accurate and up-to-date road maps are in crucial demand for daily human life and urban traffic control. Recently, with the emergence of crowdsourced mapping, a surge in academic attention has been paid to generating road networks from spatio-temporal trajectory data. However, most existing methods do not explore changing road patterns contained in multi-temporal trajectory data and it is still difficult to satisfy the precision and efficiency demands of road information extraction. Hence, in this paper, we propose a hybrid method to incrementally extract urban road networks from spatio-temporal trajectory data. First, raw trajectory data were partitioned into K time slices and were used to initialize K-temporal road networks by a mathematical morphology method. Then, the K-temporal road networks were adjusted according to a gravitation force model so as to amend their geometric inconsistencies. Finally, road networks were geometrically delineated using the k-segment fitting algorithm, and the associated road attributes (e.g., road width and driving rule) were inferred. Several case studies were examined to demonstrate that our method can effectively improve the efficiency and precision of road extraction and can make a significant attempt to mine the incremental change patterns in road networks from spatio-temporal trajectory data to help with road map renewal.

ACS Style

Yunfei Zhang; Zexu Zhang; Jincai Huang; Tingting She; Min Deng; Hongchao Fan; Peng Xu; Xingshen Deng. A Hybrid Method to Incrementally Extract Road Networks Using Spatio-Temporal Trajectory Data. ISPRS International Journal of Geo-Information 2020, 9, 186 .

AMA Style

Yunfei Zhang, Zexu Zhang, Jincai Huang, Tingting She, Min Deng, Hongchao Fan, Peng Xu, Xingshen Deng. A Hybrid Method to Incrementally Extract Road Networks Using Spatio-Temporal Trajectory Data. ISPRS International Journal of Geo-Information. 2020; 9 (4):186.

Chicago/Turabian Style

Yunfei Zhang; Zexu Zhang; Jincai Huang; Tingting She; Min Deng; Hongchao Fan; Peng Xu; Xingshen Deng. 2020. "A Hybrid Method to Incrementally Extract Road Networks Using Spatio-Temporal Trajectory Data." ISPRS International Journal of Geo-Information 9, no. 4: 186.

Research article
Published: 20 February 2020 in Transactions in GIS
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Semantic information in 3D building models is of vital importance for various applications in terms of smart cities. To infer the semantic information and localize the components on building facades, this article proposes a novel approach to model facades with semantics by constructing hierarchical topological graphs. This method utilizes the topological characteristics of building facades. In the first‐layer layout graph, the algorithm takes the nearest cluster as the vertex and the distance between components as the edge. Thus, a topology graph is generated for the facade. The proposed algorithm is divided into three steps. First, the topology graph is obtained by calculating the spacing between the components. It is reasonable to calculate the topological graph by encoding the topological edges. If this calculation is not effective, the topology is justified by adjusting the spacing between components. Finally, the vertices in the graph are used to repair the occluded parts of the facade. In the second‐layer graph, a grid is constructed according to the first‐layer graph. Then, the attributes of the nodes are used to reconstruct the facade. The experimental results show that this method has a high accuracy of 90% and that the average time consumption is 6 s.

ACS Style

Yuefeng Wang; Hongchao Fan; Guoqing Zhou. Reconstructing facade semantic models using hierarchical topological graphs. Transactions in GIS 2020, 24, 1073 -1097.

AMA Style

Yuefeng Wang, Hongchao Fan, Guoqing Zhou. Reconstructing facade semantic models using hierarchical topological graphs. Transactions in GIS. 2020; 24 (4):1073-1097.

Chicago/Turabian Style

Yuefeng Wang; Hongchao Fan; Guoqing Zhou. 2020. "Reconstructing facade semantic models using hierarchical topological graphs." Transactions in GIS 24, no. 4: 1073-1097.

Journal article
Published: 20 December 2019 in ISPRS International Journal of Geo-Information
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As the world’s largest crowdsourcing-based street view platform, Mapillary has received considerable attention in both research and practical applications. By February 2019, more than 20,000 users worldwide contributed approximately 6.3 million kilometers of streetscape sequences. In this study, we attempted to get a deep insight into the Mapillary project through an exploratory analysis from the perspective of contributors, including the development of users, the spatiotemporal analysis of active users, the contribution modes (walking, cycling, and driving), and the devices used to contribute. It shows that inequality exists in the distribution of contributed users, similar to that in other volunteered geographic information (VGI) projects. However, the inequality in Mapillary contribution is less than in OpenStreetMap (OSM). Compared to OSM, the other main difference is that the data collection demonstrated obvious seasonal variation because contributions to OSM can be accomplished on a computer, whereas images have to be captured on the streets for Mapillary, and this is considerably affected by seasonal weather.

ACS Style

Dawei Ma; Hongchao Fan; Wenwen Li; Xuan Ding. The State of Mapillary: An Exploratory Analysis. ISPRS International Journal of Geo-Information 2019, 9, 10 .

AMA Style

Dawei Ma, Hongchao Fan, Wenwen Li, Xuan Ding. The State of Mapillary: An Exploratory Analysis. ISPRS International Journal of Geo-Information. 2019; 9 (1):10.

Chicago/Turabian Style

Dawei Ma; Hongchao Fan; Wenwen Li; Xuan Ding. 2019. "The State of Mapillary: An Exploratory Analysis." ISPRS International Journal of Geo-Information 9, no. 1: 10.

Journal article
Published: 17 September 2019 in ISPRS International Journal of Geo-Information
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In recent years, volunteered-geographic-information (VGI) image data have served as a data source for various geographic applications, attracting researchers to assess the quality of these images. However, these applications and quality assessments are generally focused on images associated with geolocation through textual annotations, which is only part of valid images to them. In this paper, we explore the distribution pattern for most relevant VGI images of specific landmarks to extend the current quality analysis, and to provide guidance for improving the data-retrieval process of geographic applications. Distribution is explored in terms of two aspects, namely, semantic distribution and spatial distribution. In this paper, the term semantic distribution is used to describe the matching of building-image tags and content with each other. There are three kinds of images (semantic-relevant and content-relevant, semantic-relevant but content-irrelevant, and semantic-irrelevant but content-relevant). Spatial distribution shows how relevant images are distributed around a landmark. The process of this work can be divided into three parts: data filtering, retrieval of relevant landmark images, and distribution analysis. For semantic distribution, statistical results show that an average of 60% of images tagged with the building’s name actually represents the building, while 69% of images depicting the building are not annotated with the building’s name. There was also an observation that for most landmarks, 97% of relevant building images were located within 300 m around the building in terms of spatial distribution.

ACS Style

Xuan Ding; Hongchao Fan; Ding; Fan. Exploring the Distribution Patterns of Flickr Photos. ISPRS International Journal of Geo-Information 2019, 8, 418 .

AMA Style

Xuan Ding, Hongchao Fan, Ding, Fan. Exploring the Distribution Patterns of Flickr Photos. ISPRS International Journal of Geo-Information. 2019; 8 (9):418.

Chicago/Turabian Style

Xuan Ding; Hongchao Fan; Ding; Fan. 2019. "Exploring the Distribution Patterns of Flickr Photos." ISPRS International Journal of Geo-Information 8, no. 9: 418.

Journal article
Published: 28 June 2019 in Remote Sensing
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Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. This work investigates the feasibility of inferring the room type by using grammars based on geometric maps. Specifically, we take the research buildings at universities as examples and create a constrained attribute grammar to represent the spatial distribution characteristics of different room types as well as the topological relations among them. Based on the grammar, we propose a bottom-up approach to construct a parse forest and to infer the room type. During this process, Bayesian inference method is used to calculate the initial probability of belonging an enclosed room to a certain type given its geometric properties (e.g., area, length, and width) that are extracted from the geometric map. The approach was tested on 15 maps with 408 rooms. In 84% of cases, room types were defined correctly. It, to a certain degree, proves that grammars can benefit semantic enrichment (in particular, room type tagging).

ACS Style

Xuke Hu; Hongchao Fan; Alexey Noskov; Alexander Zipf; Zhiyong Wang; Jianga Shang. Feasibility of Using Grammars to Infer Room Semantics. Remote Sensing 2019, 11, 1535 .

AMA Style

Xuke Hu, Hongchao Fan, Alexey Noskov, Alexander Zipf, Zhiyong Wang, Jianga Shang. Feasibility of Using Grammars to Infer Room Semantics. Remote Sensing. 2019; 11 (13):1535.

Chicago/Turabian Style

Xuke Hu; Hongchao Fan; Alexey Noskov; Alexander Zipf; Zhiyong Wang; Jianga Shang. 2019. "Feasibility of Using Grammars to Infer Room Semantics." Remote Sensing 11, no. 13: 1535.