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.
Highlights
The main goal of this study is to propose the general framework by summarizing the Remote sensing (RS) and geospatial big data (GBD) integration approaches used in urban land use mapping and illustrate the advantages and disadvantages by applying them to map the urban land use situation in Hangzhou city, China
The case study was carried out with the assumption that a parcel divided by urban road networks is homogeneous in terms of urban land use function [44,48]
According to the process of the integration methods, these differences can be summarized into four types: (1) different urban road networks in different regions might lead to different urban parcel unit; (2) The variety of GBD in different regions lead to different mapping results; (3) The number of mixed parcels in different regions varies depending on the tiers of cities; (4) The availability of RS data greatly varies in different cities due to the coverage of clouds and cloud shadows that might be another factor affecting the application of the proposed methods
Summary
China has undergone rapid urbanization since the early 1980s, shown as substantial urban expansion and dynamics in urban land use and structure [1]. The timely and accurate urban land use information is important for guiding urban planning and land use management [2]. Remote sensing (RS) techniques were widely used to update urban land use information over the past few decades, by referring to the differences in aspects of texture, spectrum, and context among urban land use categories [3,4]. Due to the high similarity among urban land use categories in physical attributes, it is hard to identify
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