The Chinese government started an important project “Geographical conditions monitoring (GCM)” in 2012, which aims at fully revealing the spatial pattern of natural resources, and economic and social development. Urban development assessment and analysis are an important topic of the comprehensive statistics in GCM project. This study quantitatively evaluates the sustainable development level of 287 cities at prefecture level and above in China and analyzes their spatial distribution. Both an index system of urban sustainable development (USI) constructed by three aspects, namely society, economy and environment, and the TOPSIS-Entropy method are adopted. The results show that the overall level of urban sustainable development in China is not high, and obvious differences in urban sustainable development among cities exist. The coordination of social, economic and environmental sustainable development in China is at a low level. In terms of spatial distribution, cities from eastern coastal areas of China are at relatively higher levels of sustainable development with central and northeastern cities ranking second and western ones lowest. Several spatial clusters of urban sustainable development can be found. The “High–High” clusters, in which cities have high levels of sustainable development, are distributed in urban agglomerations. Finally, some suggestions have been provided to achieve all-round sustainable development in China.

Semantic change detection (SCD) is a challenging task in remote sensing, which aims to locate and identify changes between the bi-temporal images, providing detailed “from-to” change information. This information is valuable for various remote sensing applications. Recent studies have shown that multi-task networks, with dual segmentation branches and single change branch, are effective in SCD tasks. However, these networks primarily focus on extracting contextual information and ignore spatial details, resulting in the missed or false detection of small targets and inaccurate boundaries. To address the limitations of the aforementioned methods, this paper proposed a spatial-temporal semantic perception network (STSP-Net) for SCD. It effectively utilizes spatial detail information through the detail-aware path (DAP) and generates spatial-temporal semantic-perception features through combining deep contextual features. Meanwhile, the network enhances the representation of semantic features in spatial and temporal dimensions by leveraging a spatial attention fusion module (SAFM) and a temporal refinement detection module (TRDM). This augmentation results in improved sensitivity to details and adaptive performance balancing between semantic segmentation (SS) and change detection (CD). In addition, by incorporating the invariant consistency loss function (ICLoss), the proposed method constrains the consistency of land cover (LC) categories in invariant regions, thereby improving the accuracy and robustness of SCD. The comparative experimental results on three SCD datasets demonstrate the superiority of the proposed method in SCD. It outperforms other methods in various evaluation metrics, achieving a significant improvement. The Sek improvements of 2.84%, 1.63%, and 0.78% have been observed, respectively.

As urbanization has progressed over the past 40 years, continuous population growth and the rapid expansion of urban land use have caused some regions to experience various problems, such as insufficient resources and issues related to the environmental carrying capacity. The urbanization process can be understood using nighttime light data to quickly and accurately extract urban boundaries at large scales. A new method is proposed here to quickly and accurately extract urban boundaries using nighttime light imagery. Three types of nighttime light data from the DMSP/OLS (US military’s defense meteorological satellite), NPP-VIIRS (National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite), and Luojia1-01 data sets are selected, and the high-precision urban boundaries obtained from a high-resolution image are selected as the true value. Next, 15 cities are selected as the training samples, and the Jaccard coefficient is introduced. The spatial data comparison method is then used to determine the optimal threshold function for the urban boundary extraction. Alternative high-precision urban boundary truth-values for the 13 cities are then selected, and the accuracy of the urban boundary extraction results obtained using the optimal threshold function and the mutation detection method are evaluated. The following observations are made from the results: (i) The average relative errors for the urban boundary extraction results based on the three nighttime light data sources (DMSP/OLS, NPP-VIIRS, and Luojia1-01) using the optimal threshold functions are 29%, 20%, and 39%, respectively. Compared with the mutation detection method, these relative errors are reduced by 83%, 18%, and 77%, respectively; (ii) The average overall classification accuracies of the extracted urban boundaries are 95%, 96%, and 93%, respectively, which are 5%, 1%, and 7% higher than those for the mutation detection method; (iii) The average Kappa coefficients of the extracted urban boundaries are 61%, 71%, and 61%, respectively, which are 5%, 4%, and 12% higher than for the mutation detection method.

The urbanization level in China has increased rapidly since beginning of the 21st century, and the monitoring and analysis of urban expansion has become a popular topic in geoscience applications. However, problems, such as inconsistent concepts and extraction standards, low precision, and poor comparability, existing in urban monitoring may lead to wrong conclusions. This study selects 337 cities at the prefecture level and above in China as research subjects and uses high-resolution images and geographic information data in a semi-automatic extraction method to identify urban areas in 2000, 2005, 2010, and 2015. City size distribution patterns, urban expansion regional characteristics, and expansion types are analyzed. Results show that Chinese cities maintained a high-speed growth trend from 2000 to 2015, with the total area increasing by 115.79%. The overall scale of a city continues to expand, and the system becomes increasingly complex. The urban system is more balanced than the ideal Zipf distribution, but it also exhibited different characteristics in 2005. Urban areas are mostly concentrated in the eastern and central regions, and the difference between the east and the west is considerable. However, cities in the western region continuously expand. Beijing, Shanghai, Tianjin, and Guangzhou are the four largest cities in China. Approximately 73.30% of the cities are expanding in an extended manner; the urban form tends to be scattered, and land use efficiency is low. The new urban areas mainly come from cultivated land and ecological land.

Semantic change detection (SCD) extends the traditional change detection (CD) task to simultaneously identify the change areas and their corresponding land cover categories in bi-temporal images. This “from-to” change information holds significant value in numerous practical applications and is increasingly garnering attention in the remote sensing domain. However, prevalent challenges such as loss of detail and class imbalance significantly hinder the efficacy of SCD applications. To address this challenge, we propose a novel semantic information collaboration network (SIC-Net). This network incorporates a detail capture path (DCP) and a spatial-temporal semantic coordination module (STSCM) aiming to effectively execute SCD by fusing detailed information with contextual features and harnessing synergies between binary CD and semantic segmentation. Additionally, we introduce a pseudo-label growth algorithm (PLGA) to mitigate the substantial loss of sample category information. The experimental results on two widely used SCD datasets demonstrate that SIC-Net outperforms other methods across various evaluation metrics, achieving SeK of 23.96% and 61.29%, respectively. These findings not only validate the effectiveness of the SIC-Net but also provide new ideas and directions for future research in the field of remote sensing, particularly in SCD. The source code is available at https://github.com/hy-pn/SICNet .