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

Jiadi Yin, Ping Fu, Nicholas Hamm, Zhichao Li, Nanshan You, Yingli He, Ali Cheshmehzangi, Jinwei Dong

Research output: Journal PublicationArticlepeer-review

10 Citations (Scopus)


Information about urban land use is important for urban planning and sustainable devel-opment. 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 sum-marize 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.

Original languageEnglish
Article number1579
JournalRemote Sensing
Issue number8
Publication statusPublished - 2 Apr 2021


  • Decision-level integration
  • Feature-level integration
  • Geospatial big data
  • Hangzhou
  • Remote sensing
  • Urban land use

ASJC Scopus subject areas

  • Earth and Planetary Sciences (all)


Dive into the research topics of 'Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping'. Together they form a unique fingerprint.

Cite this