The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Mapping Urban Land Use by Using Landsat Images and Open Social Data
Open accessAbstract
High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large areas using satellite images and open social data. We first derived parcels from road networks contained in Open Street Map (OSM) and used the parcels as the basic mapping unit. We then used 10 features derived from Points of Interest (POI) data and two indices obtained from Landsat 8 Operational Land Imager (OLI) images to classify parcels into eight Level I classes and sixteen Level II classes of land use. Similarity measures and threshold methods were used to identify land use types in the classification process. This protocol was tested in Beijing, China. The results showed that the generated land use map had an overall accuracy of 81.04% and 69.89% for Level I and Level II classes, respectively. The map revealed significantly more details of the spatial pattern of land uses in Beijing than the land use map released by the government.
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Cites
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Self-citationUrbanisation and health in China
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Self-citationDiscovering regions of different functions in a city using human mobility and POIs
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Bringing Ecosystem Services into Economic Decision-Making: Land Use in the United Kingdom
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Social Sensing: A New Approach to Understanding Our Socioeconomic Environments
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Cited by
Schug, F., Okujeni, A., Hauer, J., Hostert, P., Nielsen, J. Ø., & van der Linden, S. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.03.022 · Cited by 96
Metadata
- Authors
- Hu, T., Yang, J., Li, X., & Gong, P.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2016-02-17
- Volume
- 8
- Issue
- 2
- Pages
- 151
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2276327097
- Cited by
- 389
- References
- 38
- FWCI
- 23.75
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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