Accurate and efficient classification maps of urban functional zones (UFZs) are crucial to urban planning, management, and decision making. Due to the complex socioeconomic UFZ properties, it is increasingly challenging to identify urban functional zones by using remote-sensing images (RSIs) alone. Point-of-interest (POI) data and remote-sensing image data play important roles in UFZ extraction. However, many existing methods only use a single type of data or simply combine the two, failing to take full advantage of the complementary advantages between them. Therefore, we designed a deep-learning framework that integrates the above two types of data to identify urban functional areas. In the first part of the complementary feature-learning and fusion module, we use a convolutional neural network (CNN) to extract visual features and social features. Specifically, we extract visual features from RSI data, while POI data are converted into a distance heatmap tensor that is input into the CNN with gated attention mechanisms to extract social features. Then, we use a feature fusion module (FFM) with adaptive weights to fuse the two types of features. The second part is the spatial-relationship-modeling module. We designed a new spatial-relationship-learning network based on a vision transformer model with long- and short-distance attention, which can simultaneously learn the global and local spatial relationships of the urban functional zones. Finally, a feature aggregation module (FGM) utilizes the two spatial relationships efficiently. The experimental results show that the proposed model can fully extract visual features, social features, and spatial relationship features from RSIs and POIs for more accurate UFZ recognition.

Rural building automatic extraction technology is of great significance for rural planning and disaster assessment; however, existing methods face the dilemma of scarce sample data and large regional differences in rural buildings. To solve this problem, this study constructed an image dataset of typical Chinese rural buildings, including nine typical geographical regions, such as the Northeast and North China Plains. Additionally, an improved remote sensing image rural building extraction network called AGSC-Net was designed. Based on an encoder–decoder structure, the model integrates multiple attention gate (AG) modules and a context collaboration network (CC-Net). The AG modules realize focused expression of building-related features through feature selection. The CC-Net module models the global dependency between different building instances, providing complementary localization and scale information to the decoder. By embedding AG and CC-Net modules between the encoder and decoder, the model can capture multiscale semantic information on building features. Experiments show that, compared with other models, AGSC-Net achieved the best quantitative metrics on two rural building datasets, verifying the accuracy of the extraction results. This study provides an effective example for automatic extraction in complex rural scenes and lays the foundation for related monitoring and planning applications.

Deep learning technology, such as fully convolutional networks (FCNs), have shown competitive performance in the automatic extraction of buildings from high-resolution aerial images (HRAIs). However, there are problems of over-segmentation and internal cavity in traditional FCNs used for building extraction. To address these issues, this paper proposes a new building graph convolutional network (BGC-Net), which optimizes the segmentation results by introducing the graph convolutional network (GCN). The core of BGC-Net includes two major modules. One is an atrous attention pyramid (AAP) module, obtained by fusing the attention mechanism and atrous convolution, which improves the performance of the model in extracting multi-scale buildings through multi-scale feature fusion; the other is a dual graph convolutional (DGN) module, the build of which is based on GCN, which improves the segmentation accuracy of object edges by adding long-range contextual information. The performance of BGC-Net is tested on two high spatial resolution datasets (Wuhan University building dataset and a Chinese typical city building dataset) and compared with several state-of-the-art networks. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches (FCN8s, DANet, SegNet, U-Net, ARC-Net, BAR-Net) in both visual interpretation and quantitative evaluations. The BGC-Net proposed in this paper has better results when extracting the completeness of buildings, including boundary segmentation accuracy, and shows great potential in high-precision remote sensing mapping applications.

Strengthening the construction of ecological civilization is an inevitable requirement for promoting high-quality economic and social development. It is of great significance to study the evolutionary trend and relationship between urban spatial structure and ecological efficiency to promote high-quality social development. Taking Shandong Province as an example, this paper obtains data on urban factors such as points of interest, night light, number of employed people at the end of the year and water supply; uses Anselin Local Moran’s I index to identify urban centers; analyzes the distribution form characteristics and agglomeration degree of urban space; and studies the spatial distribution characteristics and causes of differences in ecological efficiency based on the Super-SBM DEA model with undesirable output. The results show that all cities in Shandong Province show an inverse S-shaped circle decreasing trend, Laiwu city has the highest compactness (compactness index is 2.96), and Tai ‘an city has the lowest compactness index of 0.04. The level of eco-efficiency in Shandong Province is “low in the west and high in the east”, and the difference in eco-efficiency between regions is increasing year by year. Urban compactness has a “first increasing and then decreasing” effect on eco-efficiency. Technological innovation and industrial structure narrow the spatial difference in eco-efficiency, and the level of economic development expands it to a certain extent. This study aims to fill the gaps in existing research. By analyzing data on the evolution of urban spatial structure and resource consumption, it will reveal the trends of changes in the urban spatial structure of Shandong Province and study the impact of these changes on ecological benefits.

Semantic segmentation of high-resolution remote sensing urban images is widely used in many fields, such as environmental protection, urban management, and sustainable development. For many years, convolutional neural networks (CNNs) have been a prevalent method in the field, but the convolution operations are deficient in modeling global information due to their local nature. In recent years, the Transformer-based methods have demonstrated their advantages in many domains due to the powerful ability to model global information, such as semantic segmentation, instance segmentation, and object detection. Despite the above advantages, Transformer-based architectures tend to incur significant computational costs, limiting the model’s real-time application potential. To address this problem, we propose a U-shaped network with Transformer as the decoder and CNN as the encoder to segment remote sensing urban scene images. For efficient segmentation, we design a window-based, multi-head, focused linear self-attention (WMFSA) mechanism and further propose the global–local information modeling module (GLIM), which can capture both global and local contexts through a dual-branch structure. Experimenting on four challenging datasets, we demonstrate that our model not only achieves a higher segmentation accuracy compared with other methods but also can obtain competitive speeds to enhance the model’s real-time application potential. Specifically, the mIoU of our method is 68.2% and 52.8% on the UAVid and LoveDA datasets, respectively, while the speed is 114 FPS, with a 1024 × 1024 input on a single 3090 GPU.