Identification of inefficient spaces in resource-depleted cities: A case study of Hegang City

Abstract

[Objective] In the new phase of industrialization and urbanization in China, resource-depleted cities are facing various development challenges. Taking the characteristics of resource-dependent cities as a starting point, this study, using Hegang City as an example, proposed a method for identifying inefficient spaces to address typical spatial issues in resource-depleted cities. [Methods] Through a literature review, this study systematically identified a series of spatial issues faced by resource-depleted cities. Based on the actual situation in Hegang City, problems related to mining subsidence areas, urban vacant land, spatial disorder areas, and abandoned buildings were recognized. Building upon existing data, this study introduced innovative deep learning models for automatic detection that identify urban vacant land, spatial disorder areas, and abandoned buildings. [Results] This study employed the DeepLab V3 and SegNet models to generate a dataset of inefficient spaces in Hegang City. The identification results were refined through field surveys. The research visualized the distribution of mining subsidence areas, urban vacant land, spatial disorder areas, and abandoned buildings within the city. [Conclusion] The practical application in Hegang City demonstrated that the research methods are capable of efficiently, quickly, and accurately identifying inefficient spaces at the city scale. This provides an effective technical support for the identification of inefficient spaces in resource-depleted cities. However, there is still room for improvement in the definition of the objects being identified and in the technical details of the proposed research methods, necessitating further research for enhancement.

Keywords

• resource-depleted cities|deep learning|urban vacant land|disordered space|inefficient space|hegang city