The DOI matches its Crossref record; 175 of the 176 references it cites were found in a registry or an index.
Geospatial big data handling theory and methods: A review and research challenges
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“ML is key to preprocessing large amounts of data in large sensor networks, allowing only what is relevant to be transmitted, instead of all the raw data that is being collected [480,672,804].”
From Tackling climate change with machine learning (2023) · cites this paper as [480]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Fractal Geometry of Nature
Mandelbrot, B. B., & WHEELER, J. A. (1983) · American Journal of Physics · doi:10.1119/1.13295 · Cited by 21,964
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011) · Perspectives on Psychological Science · doi:10.1177/1745691610393980 · Cited by 9,996
(2011) · Cited by 5,203
Random forests for classification in ecology
Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007) · Ecology · doi:10.1890/07-0539.1 · Cited by 4,950
Citizens as sensors: the world of volunteered geography
Goodchild, M. F. (2007) · GeoJournal · doi:10.1007/s10708-007-9111-y · Cited by 4,611
Big data: a revolution that will transform how we live, work, and think
(2013) · Choice Reviews Online · doi:10.5860/choice.50-6804 · Cited by 3,657
Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913
Bak, P. (1996) · Springer eBooks · doi:10.1007/978-1-4757-5426-1 · Cited by 2,830
Research works printed but not linked: 10
- Ackermann, F., 1994. Digital Elevation Models – Techniques and Applications, Quality Standards, Development. IAPRS, 30/4(Commission IV), pp. 421–432.
- Dasgupta, 2013, Big data: the future is in analytics, Geospatial World
- Dittrich, A., Lucas, C., 2013. A step towards real-time detection and localization of disaster events based on tweets. In: Proceedings of the 10th International Conference on Information Systems for Crisis Response and Management, pp. 868–872.
- Kandwal, 2010, Geospatial analysis of HIV-related social stigma: a study of tested females across Indian mandals, Int. J. Health Geogr., 9
- Mechveliani, S.D., 2001. Computer algebra with Haskell: applying functional-categorial-‘lazy’ programming. In: Proceedings of International Workshop CAAP-2001 (Dubna, Russia), pp. 203–211; <http://ca-d.jinr.ru/confs/CAAP/Final/proceedings/proceed.ps>.
- Milewski, 2009, Lock options, DR DOBBS J., 34, 28
- Pettit, C., Stimson, R., Nino-Ruiz, M., Moradini, L., Widjaja, I., Delaney, P., et al., 2014. Supporting Urban Informatics through a Big Data Analytics Online Workbench. NSF Workshop on Big Data and Urban Informatics.
- Rajasekar, 2006, Image mining for modeling of forest fires from Meteosat images, IEEE Trans. Geosci. Remote Sensing, 45, 246
- Ujang, 2014, 3D hilbert space filling curves in 3D city modeling for faster spatial queries, Int. J. 3-D Inform. Model. (IJ3DIM), 3
- Wang, 2013, On spatial data mining under big data, J. China Acad. Electron. Inform. Technol., 8, 8
Cited by
Sorted by citations
Remote sensing for agricultural applications: A meta-review
Weiss, M., Jacob, F., & Duveiller, G. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111402 · Cited by 1,948
Google Earth Engine for geo-big data applications: A meta-analysis and systematic review
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L. J., Adeli, S., & Brisco, B. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.04.001 · Cited by 1,437
Key issues in rigorous accuracy assessment of land cover products
Stehman, S. V., & Foody, G. M. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.05.018 · Cited by 727
Social media data for conservation science: A methodological overview
Toivonen, T., Heikinheimo, V., Fink, C., Hausmann, A., Hiippala, T., Järv, O., Tenkanen, H., & Minin, E. D. (2019) · Biological Conservation · doi:10.1016/j.biocon.2019.01.023 · Cited by 504
Wang, J., Bretz, M., Dewan, M. A. A., & Delavar, M. A. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.153559 · Cited by 447
Earth observation in service of the 2030 Agenda for Sustainable Development
Anderson, K., Ryan, B. J., Sonntag, W. E., Kavvada, A., & Friedl, L. (2017) · Geo-spatial Information Science · doi:10.1080/10095020.2017.1333230 · Cited by 411
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
Location based services: ongoing evolution and research agenda
Huang, H., Gärtner, G., Krisp, J. M., Raubal, M., & de Weghe, N. V. (2018) · Journal of Location Based Services · doi:10.1080/17489725.2018.1508763 · Cited by 320
Nighttime light remote sensing for urban applications: Progress, challenges, and prospects
Zheng, Q., Seto, K. C., Zhou, Y., You, S., & Weng, Q. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.05.028 · Cited by 320
Houborg, R., & McCabe, M. F. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.10.004 · Cited by 287
Houborg, R., & McCabe, M. F. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.02.067 · Cited by 277
Heikinheimo, V., Minin, E. D., Tenkanen, H., Hausmann, A., Erkkonen, J., & Toivonen, T. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6030085 · Cited by 275
Potential and Limitations of Open Satellite Data for Flood Mapping
Notti, D., Giordan, D., Calò, F., Pepe, A., Zucca, F., & Galvé, J. P. (2018) · Remote Sensing · doi:10.3390/rs10111673 · Cited by 260
VoPham, T., Hart, J. E., Laden, F., & Chiang, Y. (2018) · Environmental Health · doi:10.1186/s12940-018-0386-x · Cited by 276
Data-Driven Decision Making in Precision Agriculture: The Rise of Big Data in Agricultural Systems
Tantalaki, N., Souravlas, S. I., & Roumeliotis, Μ. (2019) · Journal of Agricultural & Food Information · doi:10.1080/10496505.2019.1638264 · Cited by 270
Understanding the use of urban green spaces from user-generated geographic information
Heikinheimo, V., Tenkanen, H., Bergroth, C., Järv, O., Hiippala, T., & Toivonen, T. (2020) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2020.103845 · Cited by 256
Praticò, S., Solano, F., Fazio, S. D., & Modica, G. (2021) · Remote Sensing · doi:10.3390/rs13040586 · Cited by 260
Smart agriculture – Urgent need of the day in developing countries
Goel, R. K., Yadav, C. S., Vishnoi, S., & Rastogi, R. (2021) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2021.100512 · Cited by 248
Muhammad, R., Zhang, W., Abbas, Z., Guo, F. Y., & Gwiazdzinski, L. (2022) · Land · doi:10.3390/land11030419 · Cited by 230
Spatial Decision Support Systems: Three decades on
Keenan, P., & Jankowski, P. L. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.10.010 · Cited by 239
Integrating remote sensing and geospatial big data for urban land use mapping: A review
Yin, J., Dong, J., Hamm, N., Li, Z., Wang, J., Xing, H., & Fu, P. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102514 · Cited by 221
Advancement of Remote Sensing for Soil Measurements and Applications: A Comprehensive Review
Abdulraheem, M. I., Zhang, W., Li, S., Moshayedi, A. J., Farooque, A. A., & Hu, J. (2023) · Sustainability · doi:10.3390/su152115444 · Cited by 213
Self-citationLi, Y., Shi, T., Zhang, Y., Chen, W., Wang, Z., & Li, H. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.02.009 · Cited by 210
Improving big citizen science data: Moving beyond haphazard sampling
Callaghan, C. T., Rowley, J. J. L., Cornwell, W. K., Poore, A. G. B., & Major, R. E. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000357 · Cited by 215
Fagerholm, N., Raymond, C. M., Olafsson, A. S., Brown, G., Rinne, T. E., Hasanzadeh, K., Broberg, A., & Kyttä, M. (2021) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2020.1869747 · Cited by 186
Achieving smart sustainable cities with GeoICT support: The Saudi evolving smart cities
Aina, Y. A. (2017) · Cities · doi:10.1016/j.cities.2017.07.007 · Cited by 184
Sun, Z., Di, L., & Fang, H. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1516313 · Cited by 187
Citizen observations contributing to flood modelling: opportunities and challenges
Assumpção, T. H., Popescu, I., Jonoski, A., & SOLOMATINE, D. P. (2018) · Hydrology and earth system sciences · doi:10.5194/hess-22-1473-2018 · Cited by 179
Big Data for transportation and mobility: recent advances, trends and challenges
Torre-Bastida, A. I., Ser, J. D., Laña, I., Ilardia, M., Bilbao, M. N., & Campos‐Cordobés, S. (2018) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2018.5188 · Cited by 175
Guo, H., Goodchild, M. F., & Annoni, A. (2019) · Springer eBooks · doi:10.1007/978-981-32-9915-3 · Cited by 172
BIM Big Data Storage in WebVRGIS
Lv, Z., Li, X., Lv, H., & Xiu, W. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2916689 · Cited by 167
Robinson, A. C., Demšar, U., Moore, A., Buckley, A. R., Jiang, B., Field, K. S., Kraak, M. J., Camboim, S. P., & Sluter, C. R. (2017) · International Journal of Cartography · doi:10.1080/23729333.2016.1278151 · Cited by 164
Self-citationSharma, R., Kamble, S., & Gunasekaran, A. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.10.001 · Cited by 163
Uncovering inconspicuous places using social media check-ins and street view images
Zhang, F., Zu, J., Hu, M., Zhu, D., Kang, Y., Gao, S., Zhang, Y., & Huang, Z. (2020) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2020.101478 · Cited by 150
Ang, K. L.-M., Seng, J. K. P., Ngharamike, E., & Ijemaru, G. K. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11020085 · Cited by 150
Qiang, Y., Huang, Q., & Xu, J. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102115 · Cited by 149
Zheng, F., Tao, R., Maier, H. R., See, L., Savic, D. A., Zhang, T., Chen, Q., Assumpção, T. H., Yang, P., Haratmeh, B. H., et al. (2018) · Reviews of Geophysics · doi:10.1029/2018rg000616 · Cited by 154
Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data
Houborg, R., & McCabe, M. F. (2018) · Remote Sensing · doi:10.3390/rs10060890 · Cited by 146
A Review of Citizen Science and Crowdsourcing in Applications of Pluvial Flooding
See, L. (2019) · Frontiers in Earth Science · doi:10.3389/feart.2019.00044 · Cited by 143
Remote sensing heritage in a petabyte-scale: satellite data and heritage Earth Engine© applications
Αγαπίου, Ά. (2016) · International Journal of Digital Earth · doi:10.1080/17538947.2016.1250829 · Cited by 134
Defining and spatially modelling cultural ecosystem services using crowdsourced data
Havinga, I., Bogaart, P. W., Hein, L., & Tuia, D. (2020) · Ecosystem Services · doi:10.1016/j.ecoser.2020.101091 · Cited by 134
Crowdsourcing geo-information on landscape perceptions and preferences: A review
Bubalo, M., Zanten, B. T. V., & Verburg, P. H. (2019) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2019.01.001 · Cited by 137
Niu, T., Wang, J., Zhang, K., & Du, P. (2017) · Renewable Energy · doi:10.1016/j.renene.2017.10.075 · Cited by 129
Yan, Y., Feng, C., Huang, W., Fan, H., Wang, Y., & Zipf, A. (2020) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2020.1730848 · Cited by 122
Geospatial Big Data and archaeology: Prospects and problems too great to ignore
McCoy, M. D. (2017) · Journal of Archaeological Science · doi:10.1016/j.jas.2017.06.003 · Cited by 99
Chen, H., Li, Z., Wu, J., Xiong, W. H., & Du, C. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.03.012 · Cited by 115
Mapping trees along urban street networks with deep learning and street-level imagery
Lumnitz, S., Devisscher, T., Mayaud, J. R., Radić, V., Coops, N. C., & Griess, V. C. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.01.016 · Cited by 123
What is going on within google earth engine? A systematic review and meta-analysis
Pérez‐Cutillas, P., Pérez-Navarro, A., García, C. C., Zema, D. A., & Álvarez, J. P. A. (2022) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2022.100907 · Cited by 114
Iterative integration of deep learning in hybrid Earth surface system modelling
Chen, M., Qian, Z., Boers, N., Jakeman, A. J., Kettner, A. J., Brandt, M., Kwan, M., Batty, M., Li, W., Zhu, R., et al. (2023) · Nature Reviews Earth & Environment · doi:10.1038/s43017-023-00452-7 · Cited by 116
A review of drought monitoring with big data: Issues, methods, challenges and research directions
Balti, H., Abbes, A. B., Mellouli, N., Farah, I. R., Sang, Y., & Lamolle, M. (2020) · Ecological Informatics · doi:10.1016/j.ecoinf.2020.101136 · Cited by 112
Characterizing urban land changes of 30 global megacities using nighttime light time series stacks
Zheng, Q., Weng, Q., & Wang, K. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.01.002 · Cited by 110
Emerging trends and research frontiers in spatial multicriteria analysis
Malczewski, J., & Jankowski, P. L. (2020) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2020.1712403 · Cited by 116
Nardi, F., Cudennec, C., Abrate, T., Allouch, C., Annis, A., Assumpção, T. H., Aubert, A. H., Bérod, D. D., Braccini, A. M., Buytaert, W., et al. (2020) · Hydrological Sciences Journal · doi:10.1080/02626667.2020.1849707 · Cited by 108
Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX)
Bechtel, B., Demuzere, M., Sismanidis, P., Fenner, D., Brousse, O., Beck, C., Coillie, F. V., Conrad, O., Keramitsoglou, I., Middel, A., et al. (2017) · Urban Science · doi:10.3390/urbansci1020015 · Cited by 107
Taleb, I., Serhani, M. A., & Rachida Dssouli, A. (2018) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2018.00029 · Cited by 107
Review of Web Mapping: Eras, Trends and Directions
Veenendaal, B., Brovelli, M. A., & Li, S. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6100317 · Cited by 106
Self-citationDigital footprints: Incorporating crowdsourced geographic information for protected area management
Walden‐Schreiner, C., Leung, Y., & Tateosian, L. G. (2017) · Applied Geography · doi:10.1016/j.apgeog.2017.11.004 · Cited by 104
Technologies to Support Community Flood Disaster Risk Reduction
McCallum, I., Liu, W., See, L., Mechler, R., Keating, A., Hochrainer‐Stigler, S., Mochizuki, J., Fritz, S., Dugar, S., Arestegui, M., et al. (2016) · International Journal of Disaster Risk Science · doi:10.1007/s13753-016-0086-5 · Cited by 103
Kotsev, A., Minghini, M., Tomas, R., Cetl, V., & Lutz, M. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9030176 · Cited by 102
Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping
Song, Y., Kalácska, M., Gašparović, M., Yao, J., & Najibi, N. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103300 · Cited by 102
Land Use and Land Cover Mapping in the Era of Big Data
Zhang, C., & Li, X. (2022) · Land · doi:10.3390/land11101692 · Cited by 101
Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods
Niu, H., & Silva, E. A. (2020) · Journal of Urban Planning and Development · doi:10.1061/(asce)up.1943-5444.0000566 · Cited by 100
Hashedcubes: Simple, Low Memory, Real-Time Visual Exploration of Big Data
Pahins, C. A. L., Stephens, S. A., Scheidegger, C., & Comba, J. L. D. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598624 · Cited by 98
Mapping and the Citizen Sensor
Antoniou, V., & Geographic Directorate, P. C. (2017) · Ubiquity Press eBooks · doi:10.5334/bbf · Cited by 98
An integrated GIS platform architecture for spatiotemporal big data
Wang, S., Zhong, Y., & Wang, E. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.034 · Cited by 97
Exploring urban tourism crowding in Shanghai via crowdsourcing geospatial data
Shi, B., Zhao, J., & Chen, P. (2016) · Current Issues in Tourism · doi:10.1080/13683500.2016.1224820 · Cited by 94
Improved Methods for Fire Risk Assessment in Low-Income and Informal Settlements
Twigg, J., Christie, N., Haworth, J., Osuteye, E., & Skarlatidou, A. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14020139 · Cited by 94
Self-citationIntegrated Participatory and Collaborative Risk Mapping for Enhancing Disaster Resilience
Liu, W., Dugar, S., McCallum, I., Gaurav, T., See, L., Khadka, P., Budhathoki, N. R., Brown, S., Mechler, R., Fritz, S., et al. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7020068 · Cited by 108
Geospatial Dashboards for Monitoring Smart City Performance
Jing, C., Du, M., Li, S., & Liu, S. (2019) · Sustainability · doi:10.3390/su11205648 · Cited by 93
Self-citationSun, Y., Du, Y., Wang, Y., & Zhuang, L. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14060644 · Cited by 90
Using crowd-sourced photos to assess seasonal patterns of visitor use in mountain-protected areas
Walden‐Schreiner, C., Rossi, S. D., Barros, A., Pickering, C. M., & Leung, Y. (2018) · AMBIO · doi:10.1007/s13280-018-1020-4 · Cited by 90
Citizen science for assessing ecosystem services: Status, challenges and opportunities
Schröter, M., Kraemer, R., Mantel, M., Kabisch, N., Hecker, S., Richter, A., Neumeier, V., & Bonn, A. (2017) · Ecosystem Services · doi:10.1016/j.ecoser.2017.09.017 · Cited by 87
Harrison, G., Grant‐Muller, S., & Hodgson, F. C. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102672 · Cited by 87
Digital earth: yesterday, today, and tomorrow
Annoni, A., Nativi, S., Çöltekin, A., Desha, C., Eremchenko, E. N., Gevaert, C. M., Giuliani, G., Chen, M., Perez-Mora, L., Strobl, J., et al. (2023) · International Journal of Digital Earth · doi:10.1080/17538947.2023.2187467 · Cited by 92
Big Earth data analytics: a survey
Yang, C. P., Yu, M., Li, Y., Hu, F., Jiang, Y., Liu, Q., Sha, D., Xu, M., & Gu, J. (2019) · Big Earth Data · doi:10.1080/20964471.2019.1611175 · Cited by 87
Niu, H., & Silva, E. A. (2023) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2022.101934 · Cited by 83
Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30
Fonte, C. C., Minghini, M., Patriarca, J., Antoniou, V., See, L., & Skopeliti, A. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6040125 · Cited by 82
Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review
Pokhariyal, S., Patel, N. R., & Govind, A. (2023) · Agronomy · doi:10.3390/agronomy13092302 · Cited by 81
Yamashita, H., Sonobe, R., Hirono, Y., Morita, A., & Ikka, T. (2020) · Scientific Reports · doi:10.1038/s41598-020-73745-2 · Cited by 80
Depietri, Y., Ghermandi, A., Campisi‐Pinto, S., & Orenstein, D. E. (2021) · Ecosystem Services · doi:10.1016/j.ecoser.2021.101277 · Cited by 80
Using social media to assess recreation across urban green spaces in times of abrupt change
Grzyb, T., Kulczyk, S., Derek, M., & Woźniak, E. (2021) · Ecosystem Services · doi:10.1016/j.ecoser.2021.101297 · Cited by 79
de Albuquerque, J. P., Herfort, B., & Eckle, M. (2016) · Remote Sensing · doi:10.3390/rs8100859 · Cited by 77
Annis, A., & Nardi, F. (2019) · Geo-spatial Information Science · doi:10.1080/10095020.2019.1626135 · Cited by 76
Persistent challenges in geovisualization – a community perspective
Çöltekin, A., Bleisch, S., Andrienko, G., & Dykes, J. A. (2017) · International Journal of Cartography · doi:10.1080/23729333.2017.1302910 · Cited by 76
Muñoz, L., Hausner, V. H., Runge, C. A., Brown, G., & Daigle, R. (2020) · People and Nature · doi:10.1002/pan3.10083 · Cited by 73
Tilon, S., Nex, F., Kerle, N., & George Vosselman, A. (2020) · Remote Sensing · doi:10.3390/rs12244193 · Cited by 73
Can, R., Kocaman, S., & Gökçeoğlu, C. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8070300 · Cited by 74
Chen, M., Claramunt, C., Çöltekin, A., Liu, X., Peng, P., Robinson, A. C., Wang, D., Strobl, J., Wilson, J. P., Batty, M., et al. (2023) · Earth-Science Reviews · doi:10.1016/j.earscirev.2023.104438 · Cited by 72
Self-citationAn integrated environmental analytics system (IDEAS) based on a DGGS
Robertson, C., Chaudhuri, C., Hojati, M., & Roberts, S. A. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.02.009 · Cited by 68
Point-of-Interest (POI) Data Validation Methods: An Urban Case Study
Yeow, L. W., Low, R., Tan, Y. X., & Cheah, L. W. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10110735 · Cited by 68
A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information
Degrossi, L. C., de Albuquerque, J. P., Rocha, R. D. S., & Zipf, A. (2018) · Transactions in GIS · doi:10.1111/tgis.12329 · Cited by 65
Advent of Big Data in Urban Transportation for Smart Cities
Matcha, B. N., Sivanesan, S., Ng, K. C., Noum, S. Y. E., & Sharma, A. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-5264-6.ch001 · Cited by 64
Using big data to enhance crisis response and disaster resilience for a smart city
Yang, C., Su, G., & Chen, J. (2017) · IEEE International Conference on Big Data Analysis (ICBDA) · doi:10.1109/icbda.2017.8078684 · Cited by 64
Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology
Bayas, J. C. L., See, L., Fritz, S., Sturn, T., Perger, C., Dürauer, M., Karner, M., Moorthy, I., Schepaschenko, D. G., Domian, D., et al. (2016) · Remote Sensing · doi:10.3390/rs8110905 · Cited by 63
Protecting our prairies: Research and policy actions for conserving America’s grasslands
Lark, T. J. (2020) · Land Use Policy · doi:10.1016/j.landusepol.2020.104727 · Cited by 67
Krellenberg, K., Artmann, M., Stanley, C., & Hecht, R. (2021) · Urban forestry & urban greening · doi:10.1016/j.ufug.2021.126986 · Cited by 63
Welvaert, M., & Caley, P. A. (2016) · SpringerPlus · doi:10.1186/s40064-016-3583-5 · Cited by 65
Comber, A., & Wulder, M. A. (2019) · Transactions in GIS · doi:10.1111/tgis.12559 · Cited by 62
Robinson, J. A., Kocman, D., Speyer, O., & Gerasopoulos, E. (2021) · Journal of Environmental Planning and Management · doi:10.1080/09640568.2020.1853507 · Cited by 61
Li, Z., Чэн, Б., Huang, Y., Wang, H., Wang, Y., Yuan, Y., Li, X., Chen, J. M., Xu, B., & Gong, P. (2025) · 中国科学通报:英文版 · doi:10.1016/j.scib.2025.07.006 · Cited by 61
Metadata
- Authors
- Li, S., Dragicevic, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth, J., Stein, A., & Cheng, T.
- Affiliations
- Toronto Metropolitan University (CA)
- Technical University of Denmark (DK)
- Leibniz University Hannover (DE)
- The University of Melbourne (AU)
- UNSW Sydney (AU)
- University of Gävle (SE)
- University College London (GB)
- University of Twente (NL)
- Corresponding author
- Li, Songnian
- ORCID
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 115
- Pages
- 119-133
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2344199778
- Cited by
- 749
- References
- 176
- Citations by year
- 2026: 42 · 2025: 59 · 2024: 49 · 2023: 52 · 2022: 55 · 2021: 87 · 2020: 102 · 2019: 100 · 2018: 89 · 2017: 79 · 2016: 35
- FWCI
- 354.25
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- University of Twente Research Information (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- University of Twente Research Information (submittedVersion)
- arXiv (Cornell University)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- NSERC
- Topics
- Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Engineering › Building and Construction › 3D Modeling in Geospatial Applications
- Keywords
- geospatial big data, spatial data management, geospatial data storage, geoprocessing, geospatial data visualization, data quality checks, research challenges
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review