Building contour extraction from high-resolution remote sensing images is a basic task for the reasonable planning of regional construction. Recently, building segmentation methods based on the U-Net network have become popular as they largely improve the segmentation accuracy by applying ‘skip connection’ to combine high-level and low-level feature information more effectively. Meanwhile, researchers have demonstrated that introducing an attention mechanism into U-Net can enhance local feature expression and improve the performance of building extraction in remote sensing images. In this paper, we intend to explore the effectiveness of the primeval attention gate module and propose the novel Attention Gate Module (AG) based on adjusting the position of ‘Resampler’ in an attention gate to Sigmoid function for a building extraction task, and a novel Attention Gates U network (AGs-Unet) is further proposed based on AG, which can automatically learn different forms of building structures in high-resolution remote sensing images and realize efficient extraction of building contour. AGs-Unet integrates attention gates with a single U-Net network, in which a series of attention gate modules are added into the ‘skip connection’ for suppressing the irrelevant and noisy feature responses in the input image to highlight the dominant features of the buildings in the image. AGs-Unet improves the feature selection of the attention map to enhance the ability of feature learning, as well as paying attention to the feature information of small-scale buildings. We conducted the experiments on the WHU building dataset and the INRIA Aerial Image Labeling dataset, in which the proposed AGs-Unet model is compared with several classic models (such as FCN8s, SegNet, U-Net, and DANet) and two state-of-the-art models (such as PISANet, and ARC-Net). The extraction accuracy of each model is evaluated by using three evaluation indexes, namely, overall accuracy, precision, and intersection over union. Experimental results show that the proposed AGs-Unet model can improve the quality of building extraction from high-resolution remote sensing images effectively in terms of prediction performance and result accuracy.

The acceleration of urbanization has resulted in a heightened awareness of the impacts of urban environments on residents’ emotional states. This present study focuses on the Lixia District of Jinan City. By using urban street view big data and deep learning methods, we undertook a detailed analysis of the impacts of urban color features on residents’ emotional perceptions. In particular, a substantial corpus of street scene image data was extracted and processed. This was performed using a deep convolutional neural network (DCNN) and semantic segmentation technology (PSPNet), which enabled the simulation and prediction of the subjective perception of the urban environment by humans. Furthermore, the color complexity and coordination in the street scene were quantified and combined with residents’ emotional feedback to carry out a multi-dimensional analysis. The findings revealed that color complexity and coordination were significant elements influencing residents’ emotional perceptions. A high color complexity is visually appealing, but can lead to fatigue, discomfort, and boredom; a moderate complexity stimulates vitality and pleasure; high levels of regional harmony and aesthetics can increase perceptions of beauty and security; and low levels of coordination can increase feelings of depression. The environmental characteristics of different areas and differences in the daily activities of residents resulted in regional differences regarding the impacts of color features on emotional perception. This study corroborates the assertion that environmental color coordination has the capacity to enhance residents’ emotions, thereby providing an important reference point for urban planning. Planning should be based on the functional characteristics of the region, and color complexity and coordination should be reasonably regulated to optimize the emotional experiences of residents. Differentiated color management enhances urban aesthetics, livability, and residents’ happiness and promotes sustainable development. In the future, the influences of color and environmental factors on emotions can be explored in depth, with a view to assist in the formulation of fine urban design.

Street visual quality improvement plays an important role in urban development. An important direction for street quality research lies in accurately perceiving the spatial quality of urban streets and exploring the connection with street constituents. This study applies deep learning to extract visual elements from street view images and uses a human-machine adversarial model to rate them across six dimensions (beautiful, wealthy, safety, lively, depressing, and boring). Through spatial visualization of street quality, overlay analysis with network accessibility, and multiple linear regression, it examines the correlations between street space quality and its constituent elements. The results indicate that the streets within the study area scored highly on the dimensions of beautiful and lively, this is attributed to the reasonable greening construction which characterized by the good layout and density of greenery. Such greening not only enhances aesthetics but also provides environmental benefits. Additionally, the orderly street layout reflects well-organized spatial arrangements of street elements, such as pathways and building facades. Positive visual perception such as beautiful, wealthy, safety, lively is positively correlated with plants and pedestrians, and negatively correlated with walls. It is important to address the distinct types of streets in urban planning, including high-quality/accessibility streets found in urban centers, high-quality/low-accessibility streets at district junctions with sparse networks, low-quality/high-accessibility streets in the southwestern center and Low-quality/accessibility peripheral areas characterized by outdated buildings. Strategies should prioritize improvements in street function, greening, building interfaces, and pedestrian connectivity. These measures will help enhance the overall spatial quality and vitality of the area. In summary, the findings provide data support for more precise urban street improvements and offer a reference for human-centered urban planning research.

The impact of PM2.5 pollution on the ecological environment and human health and safety has attracted worldwide attention. Previous analyses of large-scale PM2.5 spatiotemporal distribution have mainly been based on satellite observations. Due to the limitations of inversion methods, the real temporal and spatial variation of PM2.5 concentrations cannot be obtained from satellite data. Using PM2.5 data from 2014 to 2017 inversed from the daily PM2.5 data in Shandong Province and employing time series, spatial autocorrelation, and geographical detector methods, the temporal and spatial evolution of PM2.5 concentrations in Shandong Province and their driving factors are revealed. The results show that the annual variation trend in the PM2.5 concentration in Shandong Province is downward, with an average annual concentration of 57.6 μg/m3, while the monthly and winter months show significant high-level u-type changes, with small changes in spring and summer, and the number of days for air compliance in 2017 was 294 . Linyi and Dezhou have the largest annual decline in air quality, while coastal cities have a small decline in air quality. From 2014 to 2017, PM2.5 of Shandong Province showed obvious spatial agglomeration and disparity patterns. The correlation between PM2.5 and other elements differs significantly by month, and soot emission has the most significant effect on PM2.5 concentration. Geographic detection analysis indicates that the main driving factors for the change in PM2.5 concentration in Shandong Province are crop broadcast area and soot emissions.

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.

Automatic building extraction based on high-resolution aerial imagery is an important challenge with a wide range of practical applications. One of the mainstream methods for extracting buildings from high-resolution images is deep learning because of its excellent deep feature extraction capability. However, existing models suffer from the problems of hollow interiors of some buildings and blurred boundaries. Furthermore, the increase in remote sensing image resolution has also led to rough segmentation results. To address these issues, we propose a generative adversarial segmentation network (ASGASN) for pixel-level extraction of buildings. The segmentation network of this framework adopts an asymmetric encoder–decoder structure. It captures and aggregates multiscale contextual information using the ASPP module and improves the classification and localization accuracy of the network using the global convolutional block. The discriminator network is an adversarial network that correctly discriminates the output of the generator and ground truth maps and computes multiscale $L_1$ loss by fusing multiscale feature mappings. The segmentation network and the discriminator network are trained alternately on the WHU building dataset and the China typical cities building dataset. Experimental results show that the proposed ASGASN can accurately identify different types of buildings and achieve pixel-level high accuracy extraction of buildings. Additionally, compared to available deep learning models, ASGASN also achieved the highest accuracy performance (89.4% and 83.6% IoU on these two datasets, respectively).