Progress and prospect of spatio-temporal data pedestal for Three Rural Issues of modernization in China

The spatio-temporal data pedestal (STDP) is a fundamental infrastructure for providing unified and authoritative geospatial data support for socio-economic development. In the context of China’s Three Rural Issues (TRI)—rural areas, agriculture, and farmers-STDP is increasingly seen as critical for modernization. This paper provides a systematic review and framework synthesis of the progress and challenges in constructing a STDP for TRI in China. Based on an analysis of domestic and international policies, research, and pilot projects, we synthesize an integrated framework for the TRI STDP. The framework focuses on four key dimensions: (1) spatial information infrastructure services (including geographic information services, integrated space-air-ground remote sensing, IoT monitoring, navigation services, and new infrastructure like 5G and AI); (2) data standards and data systems, with a particular focus on grass-root social governance elements; (3) a grid-based rural governance model; and (4) typical spatio-temporal big data platform architectures. We further identify critical challenges, including a lack of quantitative evidence on impact, fragmented data standards, and insufficient rural infrastructure. Finally, we discuss future research directions, emphasizing the need for highly integrated rural perception, intelligent governance capabilities, and robust data storage facilities. This review aims to provide a foundational reference for researchers and policymakers seeking to enhance the scientific, intelligent, and refined management of China’s rural development.

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Publication Details

Journal
Discover Applied Sciences
Published
2026-06-30
DOI
https://doi.org/10.1007/s42452-026-09130-x
Primary Topic
Remote Sensing and Land Use
Type
article
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Progress and prospect of spatio-temporal data pedestal for Three Rural Issues of modernization in China

Limin Dun, SHENG CHI, Xinyuan Cao, Xiaosong Ma et al.
Discover Applied Sciences
Remote Sensing and Land Use
article

Progress and prospect of spatio-temporal data pedestal for Three Rural Issues of modernization in China

Limin Dun, SHENG CHI, Xinyuan Cao, Xiaosong Ma, Shuyang Shi, Zhao Liu
article en

Abstract

The spatio-temporal data pedestal (STDP) is a fundamental infrastructure for providing unified and authoritative geospatial data support for socio-economic development. In the context of China’s Three Rural Issues (TRI)—rural areas, agriculture, and farmers-STDP is increasingly seen as critical for modernization. This paper provides a systematic review and framework synthesis of the progress and challenges in constructing a STDP for TRI in China. Based on an analysis of domestic and international policies, research, and pilot projects, we synthesize an integrated framework for the TRI STDP. The framework focuses on four key dimensions: (1) spatial information infrastructure services (including geographic information services, integrated space-air-ground remote sensing, IoT monitoring, navigation services, and new infrastructure like 5G and AI); (2) data standards and data systems, with a particular focus on grass-root social governance elements; (3) a grid-based rural governance model; and (4) typical spatio-temporal big data platform architectures. We further identify critical challenges, including a lack of quantitative evidence on impact, fragmented data standards, and insufficient rural infrastructure. Finally, we discuss future research directions, emphasizing the need for highly integrated rural perception, intelligent governance capabilities, and robust data storage facilities. This review aims to provide a foundational reference for researchers and policymakers seeking to enhance the scientific, intelligent, and refined management of China’s rural development.

Discover Applied Sciences
Shenyang Aerospace University (CN), Liaoning University of Technology (CN), Chengdu Surveying Geotechnical Research Institute (CN)
Sustainable cities and communities
Openalex Percentile: Top 8%
Remote Sensing and Land Use
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