A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data

Author:

Tao Yuan123,Liu Wanzeng23,Chen Jun23,Gao Jingxiang1,Li Ran23,Ren Jiaxin234,Zhu Xiuli23

Affiliation:

1. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

2. National Geomatics Center of China, Beijing 100830, China

3. Key Laboratory of Spatio-Temporal Information and Intelligent Services, Ministry of Natural Resources of China, Beijing 100830, China

4. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

Abstract

Physical urban boundaries (PUBs) are basic geographic information data for defining the spatial extent of urban landscapes with non-agricultural land and non-agricultural economic activities. Accurately mapping PUBs provides a spatiotemporal database for urban dynamic monitoring, territorial spatial planning, and ecological environment protection. However, traditional extraction methods often have problems, such as subjective parameter settings and inconsistent cartographic scales, making it difficult to identify PUBs objectively and accurately. To address these problems, we proposed a self-supervised learning approach for PUB extraction. First, we used nighttime light and OpenStreetMap road data to map the initial urban boundary for data preparation. Then, we designed a pretext task of self-supervised learning based on an unsupervised mutation detection algorithm to automatically mine supervised information in unlabeled data, which can avoid subjective human interference. Finally, a downstream task was designed as a supervised learning task in Google Earth Engine to classify urban and non-urban areas using impervious surface density and nighttime light data, which can solve the scale inconsistency problem. Based on the proposed method, we produced a 30 m resolution China PUB dataset containing six years (i.e., 1995, 2000, 2005, 2010, 2015, and 2020). Our PUBs show good agreement with existing products and accurately describe the spatial extent of urban areas, effectively distinguishing urban and non-urban areas. Moreover, we found that the gap between the national per capita GDP and the urban per capita GDP is gradually decreasing, but regional coordinated development and intensive development still need to be strengthened.

Funder

National Key Research and Development Program of China

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3