Recognition of Urban Functional Regions at Street Scale Based on Lj1-01 Night-Time Light Remote Sensing and Mobile Big Data

Zhou, Qiming; Zhang, Y.; Sun, Baozhen

doi:10.5194/isprs-annals-iv-4-w9-119-2019

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…On the one hand, the setting of POIs weights does not consider the impact of their quantity and spatial distribution, so it is necessary to improve data retrieval and data mining algorithms to improve mapping accuracy [30]. On the other hand, POIs data, NTL data and 3D urban information can be applied in UFA classification, but they are rarely applied together [3], [31]- [34]. In addition, some studies show that Hyperspectral data imaging has great potential [35]- [39].…”

Section: Introductionmentioning

confidence: 99%

Mapping Urban Functional Areas Using Multisource Remote Sensing Images and Open Big Data

Chen,

He,

Guo

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The urban functional areas (UFA) are the basic spatial units of a city, identifying their category information and spatial distribution is of great significance for studying urban spatial structure and formulating scientific and reasonable urban planning policies. However, traditional classification systems are still limited due to the data processing costs and time consuming. With the development of high-resolution satellite, the two-dimensional image has been able to identify more ground object information on a fine scale, but it lacks the application of three-dimensional characteristics and social attributes. At the same time, machine learning and deep learning have shown great utility in extracting features, and they should be applied more to the classification of urban functional areas to improve efficiency. To solve these problems, we propose an efficient and accurate framework for mapping UFA using multi-source geospatial data. It can grasp the dynamic changes in the city more accurately, and provide reference for the study of urban functional areas. An improved frequency density (IFD) model was proposed to improve the overall classification accuracy by 4.4%. Besides, the nighttime light (NTL) data from SDGSAT-1 satellite glimmer imagers are also used to classify UFA. By combining NTL and urban building height data, a sky view enhanced nightlight index (SVENI) was proposed, which can improve the overall classification accuracy of UFA by 5.8%. This study systematically clarifies the role of data sources, methods, and automatically integration models in the classification framework for UFA, which is of great significance for urban planning and sustainable development.

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Section: Introductionmentioning

confidence: 99%

Mapping Urban Functional Areas Using Multisource Remote Sensing Images and Open Big Data

Chen,

He,

Guo

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Nighttime light imagery or mobile phone footprints: Which better reflects urban socio-economics at the grid level? A case study in the Pearl River Delta, China

Cao,

et al. 2025

Computers, Environment and Urban Systems

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Potential of Using Night-Time Light to Proxy Social Indicators for Sustainable Development

et al. 2023

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Satellite-observed night-time light (NTL) data provide a measure of the lighting brightness seen from space at different times and spatial and temporal resolutions, thus offering opportunities to explore them in many applications at different spatial locations (global, regional, local). However, most applications to date have been at relatively large spatial scales, despite the need to measure indicators at a local level. This paper sets out an analysis of the potential of NTL data for populating indicators at more local (neighbourhood, street) scales. We first reviewed the overall potential of NTL data for social indicators at different spatial scales by using a systematic search of the literature and applying the Maturity Matrix Framework (MMF). We also explored a case study (Durango State, Mexico) using Visible Infrared Imaging Radiometer Suite (VIIRS) imageries, other geospatial data, and the social gap index (SGI) to identify social gaps at the local scale. The literature review showed that NTL can play a role in supporting 49 out of 192 sustainable development goal (SDG) indicators having a focus on social issues, but most of these have been explored at the global or country scales. In the case study, we found that low radiance is indeed associated with higher SGI levels (i.e., more social deprivation) and vice versa. However, more research is needed from other contexts to support a link between NTL radiance levels and social indicators at local scales.

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Recognition of Urban Functional Regions at Street Scale Based on Lj1-01 Night-Time Light Remote Sensing and Mobile Big Data

Cited by 3 publications

References 12 publications

Mapping Urban Functional Areas Using Multisource Remote Sensing Images and Open Big Data

Mapping Urban Functional Areas Using Multisource Remote Sensing Images and Open Big Data

Nighttime light imagery or mobile phone footprints: Which better reflects urban socio-economics at the grid level? A case study in the Pearl River Delta, China

Potential of Using Night-Time Light to Proxy Social Indicators for Sustainable Development

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