Exploring Relationships between Spatial Pattern Change in Steel Plants and Land Cover Change in Tangshan City

Abstract

It is of great significance for the sustainable development of steel cities to explore the relationship between the spatial pattern change in steel plants and land cover change during the transformation of steel cities. To address the issue of unsatisfactory results for segmenting steel plants based on high-resolution remote sensing images, due to insufficient sample datasets and task complexity, we proposed a steel plant segmentation strategy that combines high-resolution remote sensing images, POI data, and OSM data. Additionally, we discussed the effect of POI data and OSM data on steel plant segmentation, analyzing the spatial pattern change in steel plants in Tangshan City during 2017–2022 and its relationship with land cover change. The results demonstrate that: (1) The proposed strategy can significantly improve the accuracy of steel plant segmentation. The introduction of POI data can significantly improve the precision of steel plant segmentation, however, it will to some extent reduce the recall of steel plant segmentation, and this phenomenon weakens as the distance threshold increases. The introduction of OSM data can effectively improve the effectiveness of steel plant segmentation, however, it has significant limitations. (2) During 2017–2022, the spatial distribution center of steel plants in Tangshan City moved obviously to the southeast, and the positive change in steel plants was mainly concentrated in the coastal regions of southern Tangshan City, while the negative change in steel plants was mainly concentrated in central Tangshan City. (3) There is a relatively strong spatial correlation between the positive change in steel plants and the transition from vegetation to built area, as well as the transition from cropland to built area.

A Study on Industrial Heritage Renewal Strategy Based on Hybrid Bayesian Network

“…For this study, the essential POI data consist of fundamental information stored in the geographic information repository. Building upon insights gained from previous research that utilized POI data by other experts [85][86][87], we have processed and integrated After the experts' evaluation of the question items, several choices were modified. The outcomes of this question selection hold paramount importance within the present study, despite certain limitations.…”

“…For this study, the essential POI data consist of fundamental information stored in the geographic information repository. Building upon insights gained from previous research that utilized POI data by other experts [85][86][87], we have processed and integrated the POI data from Baidu (https://lbsyun.baidu.com/ (accessed on 30 April 2023)) and Gaode (https://lbs.amap.com/ (accessed on 30 April 2023)). These two major companies, Baidu and Gaode, sufficiently cover the most significant databases of geographic information in China.…”