Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping
Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI
2021
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/65174/ |
| _version_ | 1848800195640295424 |
|---|---|
| author | Yin, Jiadi Fu, Ping Hamm, Nicholas A.S. Li, Zhichao You, Nanshan He, Yingli Cheshmehzangi, Ali Dong, Jinwei |
| author_facet | Yin, Jiadi Fu, Ping Hamm, Nicholas A.S. Li, Zhichao You, Nanshan He, Yingli Cheshmehzangi, Ali Dong, Jinwei |
| author_sort | Yin, Jiadi |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy. |
| first_indexed | 2025-11-14T20:47:42Z |
| format | Article |
| id | nottingham-65174 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:47:42Z |
| publishDate | 2021 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-651742021-05-07T08:16:35Z https://eprints.nottingham.ac.uk/65174/ Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping Yin, Jiadi Fu, Ping Hamm, Nicholas A.S. Li, Zhichao You, Nanshan He, Yingli Cheshmehzangi, Ali Dong, Jinwei Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy. MDPI 2021-04-19 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/65174/1/YinEtAl21a.pdf Yin, Jiadi, Fu, Ping, Hamm, Nicholas A.S., Li, Zhichao, You, Nanshan, He, Yingli, Cheshmehzangi, Ali and Dong, Jinwei (2021) Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping. Remote Sensing, 13 (8). ISSN 2072-4292 urban land use; remote sensing; geospatial big data; decision-level integration; feature-level integration; Hangzhou https://doi.org/10.3390/rs13081579 doi:10.3390/rs13081579 doi:10.3390/rs13081579 |
| spellingShingle | urban land use; remote sensing; geospatial big data; decision-level integration; feature-level integration; Hangzhou Yin, Jiadi Fu, Ping Hamm, Nicholas A.S. Li, Zhichao You, Nanshan He, Yingli Cheshmehzangi, Ali Dong, Jinwei Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping |
| title | Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping |
| title_full | Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping |
| title_fullStr | Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping |
| title_full_unstemmed | Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping |
| title_short | Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping |
| title_sort | decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping |
| topic | urban land use; remote sensing; geospatial big data; decision-level integration; feature-level integration; Hangzhou |
| url | https://eprints.nottingham.ac.uk/65174/ https://eprints.nottingham.ac.uk/65174/ https://eprints.nottingham.ac.uk/65174/ |