Urbanization challenges city sustainability by aggravating uneven population migration and land exploitation. Understanding the characteristics and dynamics of this imbalance is crucial for promoting sustainable development. With a focus on population-related land change, this study analyzes the urban–rural imbalance characterized by settlement expansion from 1985 to 2019, using nighttime light (NTL) remote sensing imagery and global settlement distribution data, with Guangdong province, China, as a case study. The key findings reveal significant spatiotemporal differences in settlement expansion between the urban and rural regions. The urban settlements experienced faster expansion from 1985 to 2005, which slowed post-2005, while the rural settlements maintained a stable growth rate throughout the study period. The economic and environmental conditions were identified as major drivers of expansion diversity, with economic factors playing a dominant role in the urban regions and both factors influencing the rural regions. A linear regression analysis highlighted the diverse quantity relationships between the urban and rural settlements across different spatial extents; the urban settlements dominated in quantity at the provincial level, primarily due to the contributions of the core Pearl River Delta (PRD) region. In contrast, the rural settlements outnumbered the urban ones in most of the other prefectures, a trend that continued to deepen across Guangdong province. The findings of this study provide deeper insights into the characteristics and evolvement of the urban–rural imbalance, policy implications and actionable strategies are offered for equitable and sustainable city development.

Offshore mariculture is critical for global food security and economic development. Advances in deep learning and data-driven approaches, enable the rapid and effective monitoring of offshore mariculture distribution and changes. However, detector performance depends heavily on training data quality. The lack of standardized classifications and public datasets for offshore mariculture facilities currently hampers effective monitoring. Here, we propose to categorize offshore mariculture facilities into six types: TCC, DWCC, FRC, LC, RC, and BC. Based on these categories, we introduce a benchmark dataset called OMAD-6. This dataset includes over 130,000 instances and more than 16,000 high-resolution remote sensing images. The images with a spatial resolution of 0.6 m were sourced from key regions in China, Chile, Norway, and Egypt, from the Google Earth platform. All instances in OMAD-6 were meticulously annotated manually with horizontal bounding boxes and polygons. Compared to existing remote sensing datasets, OMAD-6 has three notable characteristics: (1) it is comparable to large, published datasets in instances per category, image quantity, and sample coverage; (2) it exhibits high inter-class similarity; (3) it shows significant intra-class diversity in facility sizes and arrangements. Based on the OMAD-6 dataset, we evaluated eight state-of-the-art methods to establish baselines for future research. The experimental results demonstrate that the OMAD-6 dataset effectively represents various real-world scenarios, which have posed considerable challenges for current instance segmentation algorithms. Our evaluation confirms that the OMAD-6 dataset has the potential to improve offshore mariculture identification. Notably, the QueryInst and PointRend algorithms have distinguished themselves as top performers on the OMAD-6 dataset, robustly identifying offshore mariculture facilities even with complex environmental backgrounds. Its ongoing development and application will play a pivotal role in future offshore mariculture identification and management.

Shade houses are critical for modern agriculture, providing optimal growing conditions for shade-sensitive crops. However, their rapid expansion poses ecological challenges, making the accurate extraction of their spatial distribution crucial for sustainable development. The unique dark appearance of shade houses leads to low accuracy and high misclassification rates in traditional spectral index-based extraction methods, while deep learning approaches face challenges such as insufficient datasets, limited receptive fields, and poor generalization capabilities. To address these challenges, we propose ShadeNet, a novel method for shade house detection using high-resolution remote sensing imagery and semantic segmentation. ShadeNet integrates the Swin Transformer and Mask2Former frameworks, enhanced by a Global-Channel and Local-Spatial Attention (GCLSA) module. This architecture significantly improves multi-scale feature extraction and global feature capture, thereby enhancing extraction accuracy. Tested on a self-labeled dataset, ShadeNet achieved a mean Intersection over Union (mIOU) improvement of 2.75% to 7.37% compared to existing methods, significantly reducing misclassification. The integration of the GCLSA module within the Swin Transformer framework enhances the model’s ability to capture both global and local features, addressing the limitations of traditional CNNs. This innovation provides a robust solution for shade houses detection, supporting sustainable agricultural development and environmental protection.