Acta Geodaetica et Cartographica Sinica ›› 2022, Vol. 51 ›› Issue (7): 1653-1668.doi: 10.11947/j.AGCS.2022.20220192
• Cartography and Geoinformation • Previous Articles
FAN Hongchao1, KONG Gefei1, YANG Anran2
Received:
2022-03-14
Revised:
2022-04-24
Published:
2022-08-13
Supported by:
CLC Number:
FAN Hongchao, KONG Gefei, YANG Anran. Current status and prospects of research for volunteered geographic information[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1653-1668.
[1] GOODCHILD M F. Citizens as sensors:the world of volunteered geography[J]. GeoJournal, 2007, 69(4):211-221. [2] 单杰,秦昆,黄长青,等.众源地理数据处理与分析方法探讨[J].武汉大学学报(信息科学版), 2014, 39(4):390-396. SHAN Jie, QIN Kun, HUANG Changqing, et al. Methods of crowd sourcing geographic data processing and analysis[J]. Geomatics and Information Science of Wuhan University, 2014, 39(4):390-396. [3] GOODCHILD M F, LI Linna. Assuring the quality of volunteered geographic information[J]. Spatial Statistics, 2012, 1:110-120. [4] 周晓光,赵肄江,李光强,等.顾及信誉的众源时空数据模型[J].武汉大学学报(信息科学版), 2018, 43(1):10-16. ZHOU Xiaoguang, ZHAO Yijiang, LI Guangqiang, et al. Crowdsourcing spatio-temporal data model considering reputation[J]. Geomatics and Information Science of Wuhan University, 2018, 43(1):10-16. [5] WALTER C. Future trends in geospatial information management:the five to ten year vision[R]. 3rd ed. New York:UN-GGIM, 2020:82. [6] CROSSLAND M D, WYNNE B E, PERKINS W C. Spatial decision support systems:an overview of technology and a test of efficacy[J]. Decision Support Systems, 1995, 14(3):219-235. [7] 严滢伟,马大伟,范红超.自发地理信息在灾后恢复监测中的应用研究框架[J].热带地理, 2020, 40(2):184-193. YAN Yingwei, MA Dawei, FAN Hongchao. A research framework for the application of volunteered geographic information in post-disaster recovery monitoring[J]. Tropical Geography, 2020, 40(2):184-193. [8] YAN Yingwei, FENG C C, HUANG Wei, et al. Volunteered geographic information research in the first decade:a narrative review of selected journal articles in GIScience[J]. International Journal of Geographical Information Science, 2020, 34(9):1765-1791. [9] SCHMIDT F, DRÖGE-ROTHAAR A, RIENOW A. Development of a Web GIS for small-scale detection and analysis of COVID-19(SARS-CoV-2) cases based on volunteered geographic information for the city of Cologne, Germany, in July/August 2020[J]. International Journal of Health Geographics, 2021, 20(1):40. [10] WANG Zhiyong, NOVACK T, YAN Yingwei, et al. Quiet route planning for pedestrians in traffic noise polluted environments[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(12):7573-7584. [11] BALLATORE A, JOKAR ARSANJANI J. Placing Wikimapia:an exploratory analysis[J]. International Journal of Geographical Information Science, 2019, 33(8):1633-1650. [12] 尹健,李光强,职露,等.自发地理信息研究综述[J].计算机应用研究, 2016, 33(5):1281-1284. YIN Jian, LI Guangqiang, ZHI Lu, et al. Review of volunteered geographic information[J]. Application Research of Computers, 2016, 33(5):1281-1284. [13] WU Hao, LIN Anai, CLARKE K C, et al. A comprehensive quality assessment framework for linear features from volunteered geographic information[J]. International Journal of Geographical Information Science, 2021, 35(9):1826-1847. [14] FONTE C C, ANTONIOU V, BASTIN L, et al. Assessing VGI data quality[M]//FOODY G, SEE L, FRITZ S, et al. Mapping and the Citizen Sensor. London:Ubiquity Press, 2017:137-163. [15] HONARPARVAR S, MALEK M R, SAEEDI S, et al. Towards development of a real-time point feature quality assessment method for volunteered geographic information using the internet of things[J]. ISPRS International Journal of Geo-Information, 2021, 10(3):151. [16] GIRRES J F, TOUYA G. Quality assessment of the French OpenStreetMap dataset[J]. Transactions in GIS, 2010, 14(4):435-459. [17] HAKLAY M. How good is volunteered geographical information?A comparative study of openstreetmap and ordnance survey datasets[J]. Environment and Planning B:Planning and Design, 2010, 37(4):682-703. [18] ZIELSTRA D, ZIPF A. A comparative study of proprietary geodata and volunteered geographic information for Germany[C]//Proceedings of the 13th AGILE International Conference on Geographic Information Science. Guimarães, Protugal:[s.n.], 2010:1-15. [19] FORGHANI M, DELAVAR M R. A quality study of the OpenStreetMap dataset for Tehran[J]. ISPRS International Journal of Geo-Information, 2014, 3(2):750-763. [20] ZHANG Liming, PFOSER D. Using OpenStreetMap point-of-interest data to model urban change-A feasibility study[J]. PLoS One, 2019, 14(2):e0212606. [21] BALDUCCI F. Is OpenStreetMap a good source of information for cultural statistics?The case of Italian museums[J]. Environment and Planning B:Urban Analytics and City Science, 2021, 48(3):503-520. [22] HELBICH M, AMELUNXEN C, NEIS P, et al. Comparative spatial analysis of positional accuracy of OpenStreetMap and proprietary Geodata[C]//Proceedings of 2012 GI_Forum 2012:Geovizualisation, Society and Learning. Salzburg, Austria:Wichmann/VDE Verlag, 2012:24-33. [23] CHEHREGHAN A, ABBASPOUR R A. An evaluation of data completeness of VGI through geometric similarity assessment[J]. International Journal of Image and Data Fusion, 2018, 9(4):319-337. [24] ZHANG Hongyu, MALCZEWSKI J. Accuracy evaluation of the Canadian OpenStreetMap road networks[J]. International Journal of Geospatial and Environmental Research, 2018, 5(2):1. [25] CIEPȽUCH B, JACOB R, MOONEY P, et al. Comparison of the accuracy of OpenStreetMap for Ireland with Google maps and Bing maps[C]//Proceedings of the 9th International Symposium on Spatial Accuracy Assessment in Natural Resuorces and Enviromental Sciences. Leicester, UK:University of Leicester, 2010:337. [26] 罗路长,刘波,刘雪朝. OpenStreetMap路网数据质量评价及应用分析[J].江西科学, 2017, 35(1):151-157. LUO Luchang, LIU Bo, LIU Xuechao. Data quality assessment and application analysis for OpenStreetMap road network[J]. Jiangxi Science, 2017, 35(1):151-157. [27] JUHÁSZ L, HOCHMAIR H H. User contribution patterns and completeness evaluation of Mapillary, a crowdsourced street level photo service[J]. Transactions in GIS, 2016, 20(6):925-947. [28] HECHT R, KUNZE C, HAHMANN S. Measuring completeness of building footprints in OpenStreetMap over space and time[J]. ISPRS International Journal of Geo-Information, 2013, 2(4):1066-1091. [29] FAN Hongchao, ZIPF A, FU Qing, et al. Quality assessment for building footprints data on OpenStreetMap[J]. International Journal of Geographical Information Science, 2014, 28(4):700-719. [30] BALLATORE A, ZIPF A. A conceptual quality framework for volunteered geographic information[C]//Proceedings of the 12th International Conference on Spatial Information Theory. Santafe New mexico, USA:Springer, 2015:89-107. [31] 万义良.空间数据质量检查与评估理论研究[D].武汉:武汉大学, 2015. WAN Yiliang. Research on the theory for spatial data quality inspection and assessment[D]. Wuhan:Wuhan University, 2015. [32] MOONEY P, CORCORAN P, WINSTANLEY A C. Towards quality metrics for OpenStreetMap[C]//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. San Jose, CA, USA:ACM, 2010:514-517. [33] 王明,李清泉,胡庆武,等.面向众源开放街道地图空间数据的质量评价方法[J].武汉大学学报(信息科学版), 2013, 38(12):1490-1494. WANG Ming, LI Qingquan, HU Qingwu, et al. Quality analysis on crowd sourcing Geographic data with open street map data[J]. Geomatics and Information Science of Wuhan University, 2013, 38(12):1490-1494. [34] SEHRA S S, SINGH J, RAI H S. Assessing OpenStreetMap data using intrinsic quality indicators:an extension to the QGIS processing toolbox[J]. Future Internet, 2017, 9(2):15. [35] ZHOU Qi. Exploring the relationship between density and completeness of urban building data in OpenStreetMap for quality estimation[J]. International Journal of Geographical Information Science, 2018, 32(2):257-281. [36] KASHIAN A, RAJABIFARD A, RICHTER K F, et al. Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns[J]. International Journal of Geographical Information Science, 2019, 33(7):1420-1443. [37] SEVERINSEN J, DE ROISTE M, REITSMA F, et al. VGTrust:measuring trust for volunteered geographic information[J]. International Journal of Geographical Information Science, 2019, 33(8):1683-1701. [38] LAWRENCE H, ROBERTSON C, FEICK R, et al. The Spatial-comprehensiveness (S-COM) index:identifying optimal spatial extents in volunteered geographic information point datasets[J]. ISPRS International Journal of Geo-Information, 2020, 9(9):497. [39] MOHAMMADI N, SEDAGHAT A. A framework for classification of volunteered geographic data based on user's need[J]. Geocarto International, 2021, 36(11):1276-1291. [40] YEBOAH G, PORTO DE ALBUQUERQUE J, TROILO R, et al. Analysis of OpenStreetMap data quality at different stages of a participatory mapping process:evidence from slums in Africa and Asia[J]. ISPRS International Journal of Geo-Information, 2021, 10(4):265. [41] ZACHAROPOULOU D, SKOPELITI A, NAKOS B. Assessment and visualization of OSM consistency for European cities[J]. ISPRS International Journal of Geo-Information, 2021, 10(6):361. [42] KEßLER C, DE GROOT R T A. Trust as a proxy measure for the quality of volunteered geographic information in the case of OpenStreetMap[M]//VANDENBROUCKE D, BUCHER B, CROMPVOETS J. Geographic Information Science at the Heart of Europe. Cham:Springer, 2013:21-37. [43] 赵肄江,周晓光.地理信息志愿者信誉度评估的版本相似度模型——以面目标为例[J].测绘学报, 2015, 44(5):578-584, 589. DOI:10.11947/j.AGCS.2015.20140065. ZHAO Yijiang, ZHOU Xiaoguang. Version similarity-based model for volunteers' reputation of volunteered geographic information:a case study of polygon[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(5):578-584, 589. DOI:10.11947/j.AGCS.2015.20140065. [44] ZHOU Xiaoguang, ZHAO Yijiang. A version-similarity based trust degree computation model for crowdsourcing geographic data[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B2:327-333. [45] 马超,孙群,徐青,等.自发地理信息可信度及其评价[J].地球信息科学学报, 2016, 18(10):1305-1311. MA Chao, SUN Qun, XU Qing, et al. The credibility and evaluation of volunteered geographic information[J]. Journal of Geo-Information Science, 2016, 18(10):1305-1311. [46] MUTTAQIEN B I, OSTERMANN F O, LEMMENS R L G. Modeling aggregated expertise of user contributions to assess the credibility of OpenStreetMap features[J]. Transactions in GIS, 2018, 22(3):823-841. [47] MINGHINI M, FRASSINELLI F. OpenStreetMap history for intrinsic quality assessment:is OSM up-to-date?[J]. Open Geospatial Data, Software and Standards, 2019, 4(1):9. [48] JACOBS K T, MITCHELL S W. OpenStreetMap quality assessment using unsupervised machine learning methods[J]. Transactions in GIS, 2020, 24(5):1280-1298. [49] ALGHANIM A, JILANI M, BERTOLOTTO M, et al. Leveraging road characteristics and contributor behavior for assessing road type quality in OSM[J]. ISPRS International Journal of Geo-Information, 2021, 10(7):436. [50] ZHANG Die, GE Yong, STEIN A, et al. Ranking of VGI contributor reputation using an evaluation-based weighted pagerank[J]. Transactions in GIS, 2021, 25(3):1439-1459. [51] YEOW L W, LOW R, TAN Yuxiang, et al. Point-of-Interest (POI) data validation methods:an urban case study[J]. ISPRS International Journal of Geo-Information, 2021, 10(11):735. [52] MOCNIK F B. Benford's law and geographical information-the example of OpenStreetMap[J]. International Journal of Geographical Information Science, 2021, 35(9):1746-1772. [53] HOCHMAIR H H, ZIELSTRA D. Positional accuracy of Flickr and Panoramio images in Europe[C]//Proceedings of 2012 GI_Forum:Geovisualization, Society and Learning. Berlin, Germany:Wichmann, 2012:14-23. [54] SENARATNE H, BRÖRING A, SCHRECK T. Using reverse viewshed analysis to assess the location correctness of visually generated VGI[J]. Transactions in GIS, 2013, 17(3):369-386. [55] ZIELSTRA D, HOCHMAIR H H. Positional accuracy analysis of Flickr and Panoramio images for selected world regions[J]. Journal of Spatial Science, 2013, 58(2):251-273. [56] ANTONIOU V, SKOPELITI A, FONTE C, et al. Using OSM, geo-tagged Flickr photos and authoritative data:a quality perspective[C]//Proceedings of the 6th International Conference on Cartography and GIS. Sofia, Bulgaria:International Cartographic Association, 2016:482-492. [57] TOUYA G, ANTONIOU V, OLTEANU-RAIMOND A M, et al. Assessing crowdsourced POI quality:combining methods based on reference data, history, and spatial relations[J]. ISPRS International Journal of Geo-Information, 2017, 6(3):80. [58] BARRINGTON-LEIGH C, MILLARD-BALL A. The world's user-generated road map is more than 80% complete[J]. PLoS One, 2017, 12(8):e0180698. [59] XIE Xuejing, ZHOU Yi, XU Yongyang, et al. OpenStreetMap data quality assessment via deep learning and remote sensing imagery[J]. IEEE Access, 2019, 7:176884-176895. [60] GRÖCHENIG S, BRUNAUER R, REHRL K. Digging into the history of VGI data-sets:results from a worldwide study on OpenStreetMap mapping activity[J]. Journal of Location Based Services, 2014, 8(3):198-210. [61] NEIS P, ZIPF A. Analyzing the contributor activity of a volunteered geographic information project-the case of OpenStreetMap[J]. ISPRS International Journal of Geo-Information, 2012, 1(2):146-165. [62] BÉGIN D, DEVILLERS R, ROCHE S. Assessing Volunteered Geographic Information (VGI) quality based on contributors' mapping behaviors[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, XL-2/W1:149-154. [63] MADUBEDUBE A, COETZEE S, RAUTENBACH V. A contributor-focused intrinsic quality assessment of OpenStreetMap in Mozambique using unsupervised machine learning[J]. ISPRS International Journal of Geo-Information, 2021, 10(3):156. [64] MOONEY P, CORCORAN P. How social is OpenStreetMap?[C]//Proceedings of the 15th Association of Geographic Information Laboratories for Europe International Conference on Geographic Information Science. Avignon, France:[s.n.], 2012:24-27. [65] YANG Anran, FAN Hongchao, JING Ning. Amateur or professional:assessing the expertise of major contributors in OpenStreetMap based on contributing behaviors[J]. ISPRS International Journal of Geo-Information, 2016, 5(2):21. [66] MA Dawei, FAN Hongchao, LI Wenwen, et al. The state of Mapillary:an exploratory analysis[J]. ISPRS International Journal of Geo-Information, 2020, 9(1):10. [67] LI Linna, GOODCHILD M F, XU Bo. Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr[J]. Cartography and Geographic Information Science, 2013, 40(2):61-77. [68] ALIVAND M, HOCHMAIR H H. Spatiotemporal analysis of photo contribution patterns to Panoramio and Flickr[J]. Cartography and Geographic Information Science, 2017, 44(2):170-184. [69] 李德仁,钱新林.浅论自发地理信息的数据管理[J].武汉大学学报(信息科学版), 2010, 35(4):379-383. LI Deren, QIAN Xinlin. A brief introduction of data management for volunteered geographic information[J]. Geomatics and Information Science of Wuhan University, 2010, 35(4):379-383. [70] 黄梦妮,周晓光,赵肄江.顾及可信度的OpenStreetMap数据清理[J].测绘与空间地理信息, 2017, 40(1):177-181. HUANG Mengni, ZHOU Xiaoguang, ZHAO Yijiang. Cleaning model of OSM data which considering the trustworthiness[J]. Geomatics&Spatial Information Technology, 2017, 40(1):177-181. [71] 马超,孙群,徐青,等.基于影像匹配的自发地理信息道路精度评价与改善[J].测绘通报, 2017,(3):22-25. DOI:10.13474/j.cnki.11-2246.2017.0076. MA Chao, SUN Qun, XU Qing, et al. Accuracy evaluation and improvement of volunteer geographic information based on image matching[J]. Bulletin of Surveying and Mapping, 2017,(3):22-25. DOI:10.13474/j.cnki.11-2246.2017.0076. [72] ZHANG Xiang, WANG Tianfu, JIAO Delin, et al. Detecting inconsistent information in crowd-sourced street networks based on parallel carriageways identification and the rule of symmetry[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 175:386-402. [73] 张云菲.多源道路网与兴趣点的一致性整合方法[D].武汉:武汉大学, 2015. ZHANG Yunfei. Methods for congruent conflation of multisource road networks and POIS[D]. Wuhan:Wuhan University, 2015. [74] JOHNSON B A, IIZUKA K. Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping:case study of the Laguna de Bay Area of the Philippines[J]. Applied Geography, 2016, 67:140-149. [75] 马超.自发地理信息道路数据融合处理关键技术研究[D].郑州:信息工程大学, 2017. MA Chao. Research on key technology of data fusion of volunteered information geographic road data[D]. Zhengzhou:Information Engineering University, 2017. [76] DING Linfang, XIAO Guohui, CALVANESE D, et al. Consistency assessment for open geodata integration:an ontology-based approach[J]. Geoinformatica, 2021, 25(4):733-758. [77] JEANSOULIN R. A century of French railways:the value of remote sensing and VGI in the fusion of historical data[J]. ISPRS International Journal of Geo-Information, 2021, 10(3):154. [78] LIU Lanfa, OLTEANU-RAIMOND A M, JOLIVET L, et al. A data fusion-based framework to integrate multi-source VGI in an authoritative land use database[J]. International Journal of Digital Earth, 2021, 14(4):480-509. [79] ELWOOD S, LESZCZYNSKI A. Privacy, reconsidered:new representations, data practices, and the Geoweb[J]. Geoforum, 2011, 42(1):6-15. [80] SCASSA T. Legal issues with volunteered geographic information[J]. The Canadian Geographer/Le Géographe Canadien, 2013, 57(1):1-10. [81] BLATT A J. The benefits and risks of volunteered geographic information[J]. Journal of Map&Geography Libraries, 2015, 11(1):99-104. [82] DUNKEL A, LÖCHNER M, BURGHARDT D. Privacy-aware visualization of volunteered geographic information (VGI) to analyze spatial activity:a benchmark implementa-tion[J]. ISPRS International Journal of Geo-Information, 2020, 9(10):607. [83] NEIS P, GOETZ M, ZIPF A. Towards automatic vandalism detection in OpenStreetMap[J]. ISPRS International Journal of Geo-Information, 2012, 1(3):315-332. [84] TRUONG Q T, TOUYA G, DE RUNZ C. OSMWatchman:learning how to detect vandalized contributions in OSM using a random forest classifier[J]. ISPRS International Journal of Geo-Information, 2020, 9(9):504. [85] MOBASHERI A, HUANG Haosheng, DEGROSSI L C, et al. Enrichment of OpenStreetMap data completeness with sidewalk geometries using data mining techniques[J]. Sensors, 2018, 18(2):509. [86] REHRL K, GRÖCHENIG S. A framework for data-centric analysis of mapping activity in the context of volunteered geographic information[J]. ISPRS International Journal of Geo-Information, 2016, 5(3):37. [87] LOTFIAN M, INGENSAND J, BROVELLI M A. A framework for classifying participant motivation that considers the typology of citizen science projects[J]. ISPRS International Journal of Geo-Information, 2020, 9(12):704. [88] HOLLENSTEIN L, PURVES R S. Exploring place through user-generated content:using Flickr tags to describe city cores[J]. Journal of Spatial Information Science, 2010,(1):21-48. [89] HOFFMANN E J, WERNER M, ZHU Xiaoxiang. Building instance classification using social media images[C]//Proceedings of 2019 Joint Urban Remote Sensing Event (JURSE). Vannes, France:IEEE, 2019:1-4. [90] FERSTER C, FISCHER J, MANAUGH K, et al. Using OpenStreetMap to inventory bicycle infrastructure:a comparison with open data from cities[J]. International Journal of Sustainable Transportation, 2020, 14(1):64-73. [91] FONTE C, MINGHINI M, ANTONIOU V, et al. An automated methodology for converting OSM data into a Land Use/Cover map[C]//Proceedings of the 6th International Conference on Cartography and GIS. Sofia, Bulgaria:International CartographicAssociation, 2016:462-473. [92] FONTE C C, PATRIARCA J A, MINGHINI M, et al. Using OpenStreetMap to create land use and land cover maps:development of an application[M]//Information Resources Management Association. Geospatial Intelligence:Concepts, Methodologies, Tools, and Applications. Pennsylvania:IGI Global, 2019:1100-1123. [93] SCHULTZ M, VOSS J, AUER M, et al. Open land cover from OpenStreetMap and remote sensing[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 63:206-213. [94] LUDWIG C, HECHT R, LAUTENBACH S, et al. Mapping public urban green spaces based on OpenStreetMap and Sentinel-2 imagery using belief functions[J]. ISPRS International Journal of Geo-Information, 2021, 10(4):251. [95] FORGET Y, SHIMONI M, GILBERT M, et al. Mapping 20 years of urban expansion in 45 urban areas of Sub-Saharan Africa[J]. Remote Sensing, 2021, 13(3):525. [96] 宋宏利,张晓楠.基于VGI的土地覆被遥感产品精度验证[J].河北工程大学学报(自然科学版), 2016, 33(4):98-102. SONG Hongli, ZHANG Xiaonan. Analysis of land cover category accuracy based on volunteered geographic information[J]. Journal of Hebei University of Engineering (Natural Science Edition), 2016, 33(4):98-102. [97] ZHU Yi, DENG Xueqing, NEWSAM S. Fine-grained land use classification at the city scale using ground-level images[J]. IEEE Transactions on Multimedia, 2019, 21(7):1825-1838. [98] TERROSO-SAENZ F, MUÑOZ A. Land use discovery based on volunteer geographic information classification[J]. Expert Systems with Applications, 2020, 140:112892. [99] OVER M, SCHILLING A, NEUBAUER S, et al. Generating web-based 3D city models from OpenStreetMap:the current situation in Germany[J]. Computers, Environment and Urban Systems, 2010, 34(6):496-507. [100] GOETZ M, ZIPF A. Extending OpenStreetMap to indoor environments:bringing volunteered geographic information to the next level[J]. Urban and Regional Data Management:UDMS Annual 2011, 9:51-62. [101] GOETZ M. Towards generating highly detailed 3D CityGML models from OpenStreetMap[J]. International Journal of Geographical Information Science, 2013, 27(5):845-865. [102] KLONNER C, BARRON C, NEIS P, et al. Updating digital elevation models via change detection and fusion of human and remote sensor data in urban environments[J]. International Journal of Digital Earth, 2015, 8(2):153-171. [103] BAGHERI H, SCHMITT M, ZHU Xiaoxiang. Fusion of multi-sensor-derived heights and OSM-derived building footprints for urban 3D reconstruction[J]. ISPRS International Journal of Geo-Information, 2019, 8(4):193. [104] FAN Hongchao, KONG Gefei, ZHANG Chaoquan. An interactive platform for low-cost 3D building modeling from VGI data using convolutional neural network[J]. Big Earth Data, 2021, 5(1):49-65. [105] POSER K, DRANSCH D. Volunteered geographic information for disaster management with application to rapid flood damage estimation[J]. Geomatica, 2010, 64(1):89-98. [106] 张仁军,沈林.应用自发地理信息的灾害预警技术[J].重庆理工大学学报(自然科学), 2013, 27(10):80-83, 116. ZHANG Renjun, SHEN Lin. The early warning system of disasters based on volunteered geographic information[J]. Journal of Chongqing Institute of Technology (National Science), 2013, 27(10):80-83, 116. [107] CROOKS A T, WISE S. GIS and agent-based models for humanitarian assistance[J]. Computers, Environment and Urban Systems, 2013, 41:100-111. [108] SCHELHORN S J, HERFORT B, LEINER R, et al. Identifying elements at risk from OpenStreetMap:the case of flooding[C]//Proceedings of the 11th International ISCRAM Conference. Philadelphia, PA, USA:[s.n.], 2014. [109] SCHUMANN Ⅲ R L. Ground truthing spatial disaster recovery metrics with participatory mapping in Post-Katrina Mississippi[J]. Applied Geography, 2018, 99:63-76. [110] LIN Anqi, WU Hao, LIANG Guanghua, et al. A big data-driven dynamic estimation model of relief supplies demand in urban flood disaster[J]. International Journal of Disaster Risk Reduction, 2020, 49:101682. [111] VAHIDNIA M H, HOSSEINALI F, SHAFIEI M. Crowdsource mapping of target buildings in hazard:the utilization of smartphone technologies and geographic services[J]. Applied Geomatics, 2020, 12(1):3-14. [112] 王守成,郭风华,傅学庆,等.基于自发地理信息的旅游地景观关注度研究——以九寨沟为例[J].旅游学刊, 2014, 29(2):84-92. WANG Shoucheng, GUO Fenghua, FU Xueqing, et al. A study of the spatial patterns of tourist sightseeing based on volunteered geographic information:the case of the Jiuzhai Valley[J]. Tourism Tribune, 2014, 29(2):84-92. [113] ALIVAND M, HOCHMAIR H H. Choice set generation for modeling scenic route choice behavior with geographic information systems[J]. Transportation Research Record:Journal of the Transportation Research Board, 2015, 2495(1):101-111. [114] GONZÁLEZ-RAMIRO A, GONÇALVES G, SÁNCHEZ-RÍOS A, et al. Using a VGI and GIS-based multicriteria approach for assessing the potential of rural tourism in Extremadura (Spain)[J]. Sustainability, 2016, 8(11):1144. [115] YAN Yingwei, ECKLE M, KUO C L, et al. Monitoring and assessing post-disaster tourism recovery using Geotagged social media data[J]. ISPRS International Journal of Geo-Information, 2017, 6(5):144. [116] 陈旭.基于众源地理数据的热门旅游路线推荐算法及实证研究[D].上海:上海师范大学, 2018. CHEN Xu. Popular tourist route recommendation algorithm based on crowd-sourced geographical data and empirical research[D]. Shanghai:Shanghai Normal University, 2018. [117] DEVKOTA B, MIYAZAKI H, WITAYANGKURN A, et al. Using volunteered geographic information and nighttime light remote sensing data to identify tourism areas of interest[J]. Sustainability, 2019, 11(17):4718. |
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