Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping

Yin, Jiadi and Fu, Ping and Hamm, Nicholas A.S. and Li, Zhichao and You, Nanshan and He, Yingli and 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

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Abstract

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.

Item Type: Article
Keywords: urban land use; remote sensing; geospatial big data; decision-level integration; feature-level integration; Hangzhou
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Geographical Sciences
University of Nottingham Ningbo China > Faculty of Science and Engineering > Department of Architecture and Built Environment
Identification Number: https://doi.org/10.3390/rs13081579
Depositing User: Hamm, Nicholas
Date Deposited: 07 May 2021 08:16
Last Modified: 07 May 2021 08:16
URI: http://eprints.nottingham.ac.uk/id/eprint/65174

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