The extraction of buildings from remote sensing images is of crucial significance in urban management and planning, but it remains difficult to automatically extract buildings with precise boundaries from remote sensing images. In this paper, we propose the FEPA-Net network model, which integrates the feature extraction and position attention module for the extraction of buildings in remote sensing images. The suggested model is implemented by employing U-Net as a base model. Firstly, the number of convolutional operations in the model was increased to extract more abstract features of the objects on the ground; secondly, within the network, the ordinary convolution is substituted with the dilated convolution. This substitution aims to broaden the receptive field, with the primary intention of enabling the output of each convolution layer to incorporate a broader spectrum of feature information. Additionally, a feature extraction module is added to mitigate the loss of detailed features. Finally, the position attention module is introduced to obtain more context information. The model undergoes validation and analysis using the Massachusetts dataset and the WHU dataset. The experimental results demonstrate that the FEPA-Net model outperforms other comparative methods in quantitative evaluation. Specifically, compared to the U-Net model, the average cross-merge ratio on the two datasets improves by 1.41% and 1.43%, respectively. The comparison of the results shows that the FEPA-Net model effectively improves the accuracy of building extraction, reduces the phenomenon of wrong detection and omission, and can more clearly identify the building outline.

Cities along the lower reaches of the Yangtze River (CLRYR) have highlighted human-land conflicts with their rapid urban expansion. Fully assessing the landscape ecological risk (LER) of the region and its response in the context of urbanization is of great significance for regional sustainable development. To address this issue, this study explores the spatiotemporal evolution of LER in CLRYR from the perspective of “production-living-ecological” space (PLES), and evaluates the decoupling status of urbanization processes and LER in different cities, aiming to provide scientific reference for policymakers. The results indicate that the mean LER value increased from 0.2508 in 2000 to 0.2573 in 2020, with an increase in LER fluctuations. From a spatial distribution perspective, the proportion of medium risk is the highest, consistently above 30%; The lowest risk proportion is less than 3%. From 2000 to 2020, the Moran’s I values for LER in the CLRYR were 0.4773, 0.4014, 0.3326, 0.2462, and 0.4779, respectively, indicating a significant positive correlation. Through decoupling model analysis, it was found that only Wuxi, Suzhou, and Changzhou achieved strong decoupling between economic growth and LER between 2010 and 2020. The findings of this study provide an important scientific basis for a deeper understanding of the complex relationship between urbanization and ecological risks in CLRYR and also lay a theoretical foundation for promoting the implementation of green development strategies in the region.

Currently, an interactive environmental planning analysis system platform based on " Three Lines One Permit " (TLOP) is being developed to support environmental planning, construction project approval, and the application of TLOP outcome data in Guangzhou. The main objective is to provide governments, businesses and the public with environmental planning analysis tools to determine the site of construction projects. The platform is using the system architecture of the browser and server. Its core functions are interactive environmental planning analysis tool for construction project and the results display tool supporting map viewing. It provides users with a large number of detailed geospatial data and TLOP results data access and environmental planning analysis functions. This article describes the system architecture and implementation of the system platform and has a case study illustrating the system functionality. At present, the platform has been deployed and trial-operated. The content of the analysis framework is constantly expanding. This promotes the matching of environmental planning and analysis with local conditions. This will implement the application of TLOP and improve the efficiency of project construction and the level of ecological environment planning and management.