Geospatial big data handling theory and methods: A review and research challenges
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ML is key to preprocessing large amounts of data in large sensor networks, allowing only relevant data to be transmitted instead of all raw data.
“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]
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Land Use and Land Cover Mapping in the Era of Big Data
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Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods
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Hashedcubes: Simple, Low Memory, Real-Time Visual Exploration of Big Data
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Mapping and the Citizen Sensor
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An integrated GIS platform architecture for spatiotemporal big data
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Improved Methods for Fire Risk Assessment in Low-Income and Informal Settlements
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- 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 ›
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