Accurate and timely urban land mapping is fundamental to supporting large area environmental and socio-economic research. Most of the available large-area urban land products are limited to a spatial resolution of 30 m. The fusion of optical and synthetic aperture radar (SAR) data for large-area high-resolution urban land mapping has not yet been widely explored. In this study, we propose a fast and effective urban land extraction method using ascending/descending orbits of Sentinel-1A SAR data and Sentinel-2 MSI (MultiSpectral Instrument, Level 1C) optical data acquired from 1 January 2015 to 30 June 2016. Potential urban land (PUL) was identified first through logical operations on yearly mean and standard deviation composites from a time series of ascending/descending orbits of SAR data. A Yearly Normalized Difference Vegetation Index (NDVI) maximum and modified Normalized Difference Water Index (MNDWI) mean composite were generated from Sentinel-2 imagery. The slope image derived from SRTM DEM data was used to mask mountain pixels and reduce the false positives in SAR data over these regions. We applied a region-specific threshold on PUL to extract the target urban land (TUL) and a global threshold on the MNDWI mean, and slope image to extract water bodies and high-slope regions. A majority filter with a three by three window was applied on previously extracted results and the main processing was carried out on the Google Earth Engine (GEE) platform. China was chosen as the testing region to validate the accuracy and robustness of our proposed method through 224,000 validation points randomly selected from high-resolution Google Earth imagery. Additionally, a total of 735 blocks with a size of 900 × 900 m were randomly selected and used to compare our product’s accuracy with the global human settlement layer (GHSL, 2014), GlobeLand30 (2010), and Liu (2015) products. Our method demonstrated the effectiveness of using a fusion of optical and SAR data for large area urban land extraction especially in areas where optical data fail to distinguish urban land from spectrally similar objects. Results show that the average overall, producer’s and user’s accuracies are 88.03%, 94.50% and 82.22%, respectively.

China’s urbanization has attracted a lot of attention due to its unprecedented pace and intensity in terms of land, population, and economic impact. However, due to the lack of consistent and harmonized data, little is known about the patterns and dynamics of the interaction between these different aspects over the past few decades. Along with the implementation of the 2030 Agenda for Sustainable Development, a standardized dataset for assessing the sustainability of urbanization in China is needed. In this paper, we used remote sensing data from multiple sources (time-series of Landsat and Sentinel images) to map the impervious surface area (ISA) at five-year intervals from 1990 to 2015 and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more. This dataset was produced following the well-established rules adopted by the United Nations (UN). Validation of the ISA maps in urban areas based on the visual interpretation of Google Earth images showed that the average overall accuracy (OA), producer’s accuracy (PA) and user’s accuracy (UA) were 91.24%, 92.58% and 89.65%, respectively. Comparisons with other existing urban built-up area datasets derived from the National Bureau of Statistics of China, the World Bank and UN-habitat indicated that our dataset, namely the standardized urban built-up area dataset for China (SUBAD–China), provides an improved description of the spatiotemporal characteristics of the urbanization process and is especially applicable to a combined analysis of the spatial and socio-economic domains in urban areas. Potential applications of this dataset include combining the spatial expansion and demographic information provided by UN to calculate sustainable development indicators such as SDG 11.3.1. The dataset could also be used in other multidimensional syntheses related to the study of urbanization in China. The published dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00004.

Driven by the 2030 Agenda for Sustainable Development, the importance of sustainable urbanization has taken center stage. In this study, we investigate the impact of polycentric structures on CO₂ emissions using data from 279 Chinese cities and employing two-way fixed effects complemented by instrumental variables. Our findings indicate that polycentric structures effectively alleviate CO₂ emissions. We identify two key pathways through which polycentric structures contribute to CO₂ reduction: promoting green technology and curbing energy consumption. Additionally, we discover that these relationships are influenced by market integration levels and resource dependency. This research offers valuable insights into the future development of sustainable urban spatial structures, paving the way for more eco-friendly cities around the globe.

One important application of quantum process algebras is to formally verify quantum communication protocols. With a suitable notion of behavioural equivalence and a decision method, one can determine if an implementation of a protocol is consistent with its specification. Ground bisimulation is a convenient behavioural equivalence for quantum processes because of its associated coinduction proof technique. We exploit this technique to design and implement two on-the-fly algorithms for the strong and weak versions of ground bisimulation to check if two given processes in quantum CCS are equivalent. We then develop a tool that can verify interesting quantum protocols such as the BB84 quantum key distribution scheme.

Accurate mapping of urban impervious surfaces is fundamental for quantifying urbanization dynamics, evaluating land-use efficiency, and assessing ecological impacts. Although Sustainable Development Goal (SDG) 11.3.1 emphasizes monitoring land-use efficiency, there remains no standardized, high-resolution global reference for impervious surfaces estimation. While several 10-m global impervious surface products have been released in recent years, they use diverse input data, classification schemes, and mapping algorithms, leading to significant discrepancies in their accuracy and spatial comparability. To address this gap, this study systematically evaluates eight representative 10-m global urban land-cover products using a unified validation framework to quantify their accuracy, spatial consistency, and systematic biases. Our multi-level evaluation framework integrates pixel-wise confusion analysis, block-scale validation, cross-product spatial agreement mapping, and continental-scale area estimation, supported by a robust, globally distributed reference dataset. The analysis reveals distinct systematic biases: certain products overestimate impervious surfaces in high-density urban cores, while others significantly underestimate infrastructure such as roads and low-density built-up zones. Continental-scale estimates differ by a factor up to 3.7, mainly driven by differences in sensor inputs, feature engineering, and definition thresholds. These findings provide a rigorous basis for interpreting current 10-m global datasets and highlight the necessity for standardized definitions and multi-source fusion strategies to support reliable SDG 11.3.1 monitoring.