Geospatial big data handling theory and methods: A review and research challenges

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

Anomaly detection: A survey

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

Untitled work

(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

Big data: A survey

Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913

How Nature Works

Bak, P. (1996) · Springer eBooks · doi:10.1007/978-1-4757-5426-1 · Cited by 2,830

Geospatial Big Data: New Paradigm of Remote Sensing Applications

Deng, X., Liu, P., Liu, X., Wang, R., Zhang, Y., He, J., & Yao, Y. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2944952 · Cited by 61

Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization

Marai, G. E. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2744459 · Cited by 60

Flood hazard assessment and the role of citizen science

Sy, B., Frischknecht, C., Dao, H., Consuegra, D., & Giuliani, G. (2018) · Journal of Flood Risk Management · doi:10.1111/jfr3.12519 · Cited by 60

Digital mapping of heavy metals in urban soils: A review and research challenges

Shi, T., Li, H., Wang, R., Li, Z., Hu, Z., & Wu, G. (2023) · CATENA · doi:10.1016/j.catena.2023.107183 · Cited by 58

A review of the emergent ecosystem of collaborative geospatial tools for addressing environmental challenges

Palomino, J., Muellerklein, O., & Kelly, M. (2017) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2017.05.003 · Cited by 57

Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning

Herfort, B., Li, H., Fendrich, S., Lautenbach, S., & Zipf, A. (2019) · Remote Sensing · doi:10.3390/rs11151799 · Cited by 57

Drivers of Participation in Digital Citizen Science: Case Studies on Järviwiki and Safecast

Palacin, V., Gilbert, S., Orchard, S., Eaton, A., Ferrario, M. A., & Happonen, A. (2020) · Citizen Science Theory and Practice · doi:10.5334/cstp.290 · Cited by 67

When Machine Learning Meets Geospatial Data: A Comprehensive GeoAI Review

Boutayeb, A., Lahsen-Cherif, I., & Khadimi, A. E. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3568715 · Cited by 63

Sensing and detecting traffic events using geosocial media data: A review

Xu, S., Li, S., & Wen, R. (2018) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2018.06.006 · Cited by 56

Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier

Sitthi, A., Nagai, M., Dailey, M. N., & Ninsawat, S. (2016) · Sustainability · doi:10.3390/su8090921 · Cited by 55

Commercial Visual Analytics Systems–Advances in the Big Data Analytics Field

Behrisch, M., Streeb, D., Stoffel, F., Seebacher, D., Matejek, B., Weber, S. H., Mittelstädt, S., Pfister, H., & Keim, D. A. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2859973 · Cited by 58

Scalable space-time trajectory cube for path-finding: A study using big taxi trajectory data

Yang, L., Kwan, M., Pan, X., Wan, B., & Zhou, S. (2017) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2017.03.010 · Cited by 54

Geospatial Information Visualization and Extended Reality Displays

Çöltekin, A., Griffin, A. L., Slingsby, A., Robinson, A. C., Christophe, S., Rautenbach, V., Chen, M., Pettit, C. J., & Klippel, A. (2019) · Springer eBooks · doi:10.1007/978-981-32-9915-3_7 · Cited by 54

Advances in urban information extraction from high-resolution remote sensing imagery

Gong, J., Liu, C., & Huang, X. (2019) · Science China Earth Sciences · doi:10.1007/s11430-019-9547-x · Cited by 51

Towards an integrated urban development considering novel intelligent transportation systems

Richter, A., Löwner, M.-O., Ebendt, R., & Scholz, M. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.119970 · Cited by 51

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Mahabir, R., Schuchard, R., Crooks, A. T., Croitoru, A., & Stefanidis, A. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9060341 · Cited by 51

Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

Cui, N., Malleson, N., Houlden, V., & Comber, A. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10070425 · Cited by 51

InstantCITY: Synthesising morphologically accurate geospatial data for urban form analysis, transfer, and quality control

Wu, A. N., & Biljecki, F. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.11.005 · Cited by 51

Participatory Land Administration on Customary Lands: A Practical VGI Experiment in Nanton, Ghana

Asiama, K. O., Bennett, R., & Zevenbergen, J. A. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6070186 · Cited by 51

Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space

Foody, G. M., Ling, F., Boyd, D. S., Li, X., & Wardlaw, J. (2019) · Remote Sensing · doi:10.3390/rs11030266 · Cited by 51

Citizen Scientists’ Preferences for Communication of Scientific Output: A Literature Review

de Vries, M., Land‐Zandstra, A. M., & Smeets, I. (2019) · Citizen Science Theory and Practice · doi:10.5334/cstp.136 · Cited by 62

Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products

Mayr, S., Kuenzer, C., Geßner, U., Klein, I., & Rutzinger, M. (2019) · Remote Sensing · doi:10.3390/rs11222616 · Cited by 50

The ‘cottage effect’ in citizen science? Spatial bias in aquatic monitoring programs

Millar, E., Hazell, E. C., & Melles, S. (2018) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2018.1423686 · Cited by 57

Efficient Method for POI/ROI Discovery Using Flickr Geotagged Photos

Kuo, C., Chan, T., Fan, I.-C., & Zipf, A. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7030121 · Cited by 49

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza, S. S., & Chiang, J. (2023) · Environments · doi:10.3390/environments10100170 · Cited by 50

Mapping urban functional zones with remote sensing and geospatial big data: a systematic review

Du, S., Zhang, X., Lei, Y., Huang, X., Tu, W., Liu, B., Meng, Q., & Du, S. (2024) · GIScience & Remote Sensing · doi:10.1080/15481603.2024.2404900 · Cited by 48

Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China

Wei, Y., Shi, Z., Biswas, A., Yang, S., Ding, J., & Wang, F. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.135387 · Cited by 47

Using volunteered geographic information (VGI) in design-based statistical inference for area estimation and accuracy assessment of land cover

Stehman, S. V., Fonte, C. C., Foody, G. M., & See, L. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.04.014 · Cited by 47

Review on Integrating Geospatial Big Datasets and Open Research Issues

Al-Yadumi, S., Xion, T. E., Wei, S. G. W., & Boursier, P. (2021) · IEEE Access · doi:10.1109/access.2021.3051084 · Cited by 46

The spatial-temporal coupling pattern of grain yield and fertilization in the North China plain

Wen-jia, Y., Yue, Y., & Wang, F. (2021) · Agricultural Systems · doi:10.1016/j.agsy.2021.103330 · Cited by 46

Unsupervised detection of contextual anomaly in remotely sensed data

Liu, Q., Klucik, R., Chen, C., Grant, G. E., Gallaher, D. W., Lv, Q., & Shang, L. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.01.034 · Cited by 46

Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques

Mobasheri, A., Huang, H., Degrossi, L. C., & Zipf, A. (2018) · Sensors · doi:10.3390/s18020509 · Cited by 46

A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks

Wan, L., Hong, Y., Huang, Z., Peng, X., & Li, R. (2018) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2018.1458988 · Cited by 47

A CitSci app for landslide data collection

Kocaman, S., & Gökçeoğlu, C. (2018) · Landslides · doi:10.1007/s10346-018-1101-2 · Cited by 45

Subsurface Topographic Modeling Using Geospatial and Data Driven Algorithm

Shahri, A. A., Kheiri, A., & Hamzeh, A. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10050341 · Cited by 45

Incorporating multimodal context information into traffic speed forecasting through graph deep learning

Zhang, Y., Zhao, T., Gao, S., & Raubal, M. (2023) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2023.2234959 · Cited by 44

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

Molinier, M., López-Sánchez, C. A., Toivanen, T., Korpela, I., Corral‐Rivas, J. J., Tergujeff, R., & Tuomas Häme, D. (2016) · Remote Sensing · doi:10.3390/rs8100869 · Cited by 44

Characterizing Data Ecosystems to Support Official Statistics with Open Mapping Data for Reporting on Sustainable Development Goals

den Homberg, M. J. C. V., & Susha, I. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7120456 · Cited by 44

Los Angeles as a digital place: The geographies of user‐generated content

Ballatore, A., & Sabbata, S. D. (2020) · Transactions in GIS · doi:10.1111/tgis.12600 · Cited by 44

Towards user-adaptive remote sensing: Knowledge-driven automatic classification of Sentinel-2 time series

Arvor, D., Betbeder, J., Daher, F. R. G., Blossier, T., Roux, R. L., Corgne, S., Corpetti, T., Silgueiro, V. D. F., & da Silva, C. A. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112615 · Cited by 45

Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments

Dang, X., Zheng, R. Y., & Liang, X. (2021) · Sensors · doi:10.3390/s21010230 · Cited by 43

Data Consistency Theory and Case Study for Scientific Big Data

Shi, P., Cui, Y., Xu, K., Zhang, M., & Ding, L. (2019) · Information · doi:10.3390/info10040137 · Cited by 42

Can citizen science data guide the surveillance of invasive plants? A model-based test with Acacia trees in Portugal

de Sá, N. C., Marchante, H., Marchante, E., Cabral, J. A., Honrado, J. P., & Vicente, J. R. (2019) · Biological Invasions · doi:10.1007/s10530-019-01962-6 · Cited by 42

Comparing Human Activity Density and Green Space Supply Using the Baidu Heat Map in Zhengzhou, China

Zhang, S., Zhang, W., Wang, Y., Zhao, X., Song, P., Tian, G., & Mayer, A. L. (2020) · Sustainability · doi:10.3390/su12177075 · Cited by 42

Geodiversity Assessment with Crowdsourced Data and Spatial Multicriteria Analysis

Jankowski, P. L., Najwer, A., Zwoliński, Z., & Niesterowicz, J. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9120716 · Cited by 41

Towards a Protocol for the Collection of VGI Vector Data

Mooney, P., Minghini, M., Laakso, M., Antoniou, V., Olteanu‐Raimond, A., & Skopeliti, A. (2016) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi5110217 · Cited by 40

Robustness challenges in Reinforcement Learning based time-critical cloud resource scheduling: A Meta-Learning based solution

Liu, H., Chen, P., Ouyang, X., Gao, H., Yan, B. C., Grosso, P., & Zhao, Z. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.03.029 · Cited by 39

Recent Developments in Satellite Remote Sensing for Air Pollution Surveillance in Support of Sustainable Development Goals

Stratoulias, D., Nuthammachot, N., Dejchanchaiwong, R., Tekasakul, P., & Carmichael, G. R. (2024) · Remote Sensing · doi:10.3390/rs16162932 · Cited by 41

Crowdsourcing: It Matters Who the Crowd Are. The Impacts of between Group Variations in Recording Land Cover

Comber, A., Mooney, P., Purves, R. S., Rocchini, D., & Walz, A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0158329 · Cited by 39

Crowd-sourced pictures geo-localization method based on street view images and 3D reconstruction

Cheng, L., Yuan, Y., Xia, N., Chen, S., Chen, Y., Yang, K., Ma, L., & Li, M. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.006 · Cited by 39

Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap

Sehra, S. S., Singh, J., & Rai, H. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6070195 · Cited by 39

Hands-On Experience of Crowdsourcing for Flood Risks. An Android Mobile Application Tested in Frederikssund, Denmark

Frigerio, S., Schenato, L., Bossi, G., Mantovani, M., Marcato, G., & Pasuto, A. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15091926 · Cited by 38

Tagging in Volunteered Geographic Information: An Analysis of Tagging Practices for Cities and Urban Regions in OpenStreetMap

Davidović, N., Mooney, P., Stoimenov, L. V., & Minghini, M. (2016) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi5120232 · Cited by 37

Effect of Label Noise on the Machine-Learned Classification of Earthquake Damage

Frank, J., Rebbapragada, U. D., Bialas, J., Oommen, T., & Havens, T. C. (2017) · Remote Sensing · doi:10.3390/rs9080803 · Cited by 36

Comparing spatial patterns

Long, J., & Robertson, C. (2017) · Geography Compass · doi:10.1111/gec3.12356 · Cited by 36

Application of big data technology in agricultural Internet of Things

Zhang, C., & Liu, Z.-F. (2019) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147719881610 · Cited by 36

Human values and digital citizen science interactions

Palacin, V., Ferrario, M. A., Hsieh, G., Knutas, A., Wolff, A., & Porras, J. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102605 · Cited by 40

IBM PAIRS curated big data service for accelerated geospatial data analytics and discovery

Lu, S., Shao, X., Freitag, M., Klein, L. J., Renwick, J. D., Marianno, F. J., Albrecht, C. M., & Hamann, H. F. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840910 · Cited by 35

The Value of OpenStreetMap Historical Contributions as a Source of Sampling Data for Multi-Temporal Land Use/Cover Maps

Viana, C. M., Encalada-Abarca, L., & Rocha, J. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8030116 · Cited by 35

Current Efforts for Prediction and Assessment of Natural Disasters: Earthquakes, Tsunamis, Volcanic eruptions, Hurricanes, Tornados, and Floods

Amézquita-Sánchez, J. P., Valtierra‐Rodriguez, M., & Adeli, H. (2017) · Scientia Iranica · doi:10.24200/sci.2017.4589 · Cited by 34

Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts

Robertson, C., & Feick, R. (2018) · Transactions in GIS · doi:10.1111/tgis.12321 · Cited by 34

Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach

Vahidi, H., Klinkenberg, B., Johnson, B. A., Moskal, L. M., & Yan, W. (2018) · Remote Sensing · doi:10.3390/rs10071134 · Cited by 34

A hybrid is born: Integrating collective sensing, citizen science and professional monitoring of the environment

Becken, S., Connolly, R. M., Chen, J., & Stantić, B. (2019) · Ecological Informatics · doi:10.1016/j.ecoinf.2019.05.001 · Cited by 34

An Interaction Methodology to Collect and Assess User-Driven Requirements to Define Potential Opportunities of Future Hyperspectral Imaging Sentinel Mission

Taramelli, A., Tornato, A., Magliozzi, M. L., Mariani, S., Valentini, E., Zavagli, M., Costantini, M., Nieke, J., Adams, J., & Rast, M. (2020) · Remote Sensing · doi:10.3390/rs12081286 · Cited by 34

Geographic Information System and Big Spatial Data

Usmani, R. S. A., Hashem, M., Pillai, T. R., Saeed, A., & Abdullahi, A. M. (2020) · International Journal of Enterprise Information Systems · doi:10.4018/ijeis.2020100106 · Cited by 34

Digital Farming and Field Robotics: Internet of Things, Cloud Computing, and Big Data

Paraforos, D. S., & Griepentrog, H. W. (2021) · Agriculture automation and control · doi:10.1007/978-3-030-70400-1_14 · Cited by 34

Public participation GIS for improving wood burning emissions from residential heating and urban environmental management

López-Aparicio, S., Vogt, M., Schneider, P., Kahila-Tani, M., & Broberg, A. (2017) · Journal of Environmental Management · doi:10.1016/j.jenvman.2017.01.018 · Cited by 33

Contemporary American cartographic research: a review and prospective

Clarke, K., Johnson, J. M., & Trainor, T. (2019) · Cartography and Geographic Information Science · doi:10.1080/15230406.2019.1571441 · Cited by 36

Implementing crowdsourcing initiatives in land consolidation procedures in Poland

Krupowicz, W., Czarnecka, A., & Grus, M. (2020) · Land Use Policy · doi:10.1016/j.landusepol.2020.105015 · Cited by 33

Coastal Vulnerability Mapping by Integrating Geospatial Techniques and Analytical Hierarchy Process (AHP) along the Vishakhapatnam Coastal Tract, Andhra Pradesh, India

Baig, M. R. I., Shahfahad, Ahmad, I. A., Tayyab, M., Asgher, M. S., & Rahman, A. (2020) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-020-01204-6 · Cited by 33

Sustainable Smart Cities and Energy Management: The Labor Market Perspective

Pilipczuk, O. (2020) · Energies · doi:10.3390/en13226084 · Cited by 33

GeoAI Technologies and Their Application Areas in Urban Planning and Development: Concepts, Opportunities and Challenges in Smart City (Kuwait, Study Case)

Alastal, A. I., & Shaqfa, A. H. (2022) · Journal of Data Analysis and Information Processing · doi:10.4236/jdaip.2022.102007 · Cited by 33

Fundamental practices for drone remote sensing research across disciplines

Mathews, A. J., Singh, K. K., Cummings, A. R., & Rogers, S. R. (2023) · Drone Systems and Applications · doi:10.1139/dsa-2023-0021 · Cited by 34

Dynamic 3D Simulation of Flood Risk Based on the Integration of Spatio-Temporal GIS and Hydrodynamic Models

Wu, Y., Peng, F., Peng, Y., Kong, X., Liang, H., & Li, Q. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8110520 · Cited by 32

Cartographic Vandalism in the Era of Location-Based Games—The Case of OpenStreetMap and Pokémon GO

Juhász, L., Novack, T., Hochmair, H. H., & Qiao, S. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9040197 · Cited by 32

Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning

Badreldin, N., Prieto, B., & Fisher, R. J. (2021) · Remote Sensing · doi:10.3390/rs13244972 · Cited by 32

Which NoSQL database for IoT Applications?

Amghar, S., Cherdal, S., & Mouline, S. (2018) · International Conference on Selected Topics in Mobile and Wireless Networking (MoWNeT) · doi:10.1109/mownet.2018.8428922 · Cited by 32

Automatic Extraction and Filtering of OpenStreetMap Data to Generate Training Datasets for Land Use Land Cover Classification

Fonte, C. C., Patriarca, J., Jesus, I., & Duarte, D. (2020) · Remote Sensing · doi:10.3390/rs12203428 · Cited by 31

Spatial data infrastructure management: A two-sided market approach for strategic reflections

Jabbour, C., Rey‐Valette, H., Maurel, P., & Salles, J.-M. A. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.10.022 · Cited by 31

A method to evaluate task-specific importance of spatio-temporal units based on explainable artificial intelligence

Cheng, X., Wang, J., Li, H., Zhang, Y., Wu, L., & Liu, Y. (2020) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2020.1805116 · Cited by 31

A Citizen Science Approach for Collecting Toponyms

Perdana, A. P., & Ostermann, F. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7060222 · Cited by 30

Mapping and the Citizen Sensor

Foody, G. M., Fritz, S., Fonte, C. C., B., L., A.-M., O.-R., Mooney, P., See, L., Antoniou, V., H.-Y., L., Minghini, M., et al. (2017) · Ubiquity Press eBooks · doi:10.5334/bbf.a · Cited by 30

Detecting spatiotemporal traffic events using geosocial media data

Xu, S., Li, S., Huang, W., & Wen, R. (2022) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2022.101797 · Cited by 29

Point of Interest Matching between Different Geospatial Datasets

Deng, Y., Luo, A., Liu, J., & Wang, Y. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8100435 · Cited by 29

Voronoi classified and clustered data constellation: A new 3D data structure for geomarketing strategies

Azri, S., Ujang, U., & Rahman, A. A. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.01.022 · Cited by 29

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Odipo, V., Nickless, A., Berger, C. R., Baade, J., Urbazaev, M., Walther, C., & Schmullius, C. (2016) · Forests · doi:10.3390/f7120294 · Cited by 29

The Research Trend of Big Data in Education and the Impact of Teacher Psychology on Educational Development During COVID-19: A Systematic Review and Future Perspective

Li, J., & Jiang, Y. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.753388 · Cited by 28

Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data

Foody, G. M., See, L., Fritz, S., Moorthy, I., Perger, C., Schill, C., & Boyd, D. S. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7030080 · Cited by 28

IMU-Assisted 2D SLAM Method for Low-Texture and Dynamic Environments

Wang, Z., Chen, Y., Mei, Y., Yang, K., & Cai, B. (2018) · Applied Sciences · doi:10.3390/app8122534 · Cited by 27

Per-point processing for detailed urban solar estimation with aerial laser scanning and distributed computing

Vo, A.-V., Laefer, D. F., Smolić, A., & Zolanvari, S. M. I. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.06.009 · Cited by 27

Case Studies in Applied Bayesian Data Science

Centre International de Rencontres Mathématiques (Marseille), Ullah, I., & Mengersen, K. L. (2020) · Lecture notes in mathematics · doi:10.1007/978-3-030-42553-1 · Cited by 35

Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects

Li, H., Lu, H. C., Jensen, C. S., Tang, B., & Cheema, M. A. (2022) · ACM Computing Surveys · doi:10.1145/3498338 · Cited by 27

GEODATA IN SCIENCE – A REVIEW OF SELECTED SCIENTIFIC FIELDS

Apollo, M., Jakubiak, M., Nistor, S., Lewińska, P., Krawczyk, A., Borowski, Ł., Specht, M., Krzykowska, K., Marchel, Ł., Pęska‐Siwik, A., et al. (2023) · Acta Scientiarum Polonorum Formatio Circumiectus · doi:10.15576/asp.fc/2023.22.2.02 · Cited by 27

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