Visual attention plays a crucial role in the map-reading process and is closely related to the map cognitive process. Eye-tracking data contains a wealth of visual information that can be used to identify cognitive behavior during map reading. Nevertheless, few researchers have applied these data to quantifying visual attention. This study proposes a method for quantitatively calculating visual attention based on eye-tracking data for 3D scene maps. First, eye-tracking technology was used to obtain the differences in the participants’ gaze behavior when browsing a street view map in the desktop environment, and to establish a quantitative relationship between eye movement indexes and visual saliency. Then, experiments were carried out to determine the quantitative relationship between visual saliency and visual factors, using vector 3D scene maps as stimulus material. Finally, a visual attention model was obtained by fitting the data. It was shown that a combination of three visual factors can represent the visual attention value of a 3D scene map: color, shape, and size, with a goodness of fit (R²) greater than 0.699. The current research helps to determine and quantify the visual attention allocation during map reading, laying the foundation for automated machine mapping.

Accurate translation of aerial imagery to maps is a direction of great value and challenge in mapping, a method of generating maps that does not require using vector data as traditional mapping methods do. The tremendous progress made in recent years in image translation based on generative adversarial networks has led to rapid progress in aerial image-to-map translation. Still, the generated results could be better regarding quality, accuracy, and visual impact. This paper proposes a supervised model (SAM-GAN) based on generative adversarial networks (GAN) to improve the performance of aerial image-to-map translation. In the model, we introduce a new generator and multi-scale discriminator. The generator is a conditional GAN model that extracts the content and style space from aerial images and maps and learns to generalize the patterns of aerial image-to-map style transformation. We introduce image style loss and topological consistency loss to improve the model’s pixel-level accuracy and topological performance. Furthermore, using the Maps dataset, a comprehensive qualitative and quantitative comparison is made between the SAM-GAN model and previous methods used for aerial image-to-map translation in combination with excellent evaluation metrics. Experiments showed that SAM-GAN outperformed existing methods in both quantitative and qualitative results.

The community is the basic spatial unit for urban residents to live and rest. It is a crucial direction of city image research to explore people’s cognitive characteristics of community space image. Aiming at the lack of cognitive quantification of community spatial images, a new method that can quantify community spatial data into cognitive results is proposed. By employing spatial analysis tools, eleven spatial indicators from the perspective of community spatial form and spatial services are selected, and an image structure is constructed based on the characteristics of the indicator results. The results of multiple indicators are organized through the improved technique for order preference by similarity to ideal solution (TOPSIS) and overlay analysis method to produce a spatial image map of the community. The study displays that the spatial image characteristics of the community scale can be comprehensively expressed through three types of elements: district, path (edge), and node (landmark). These three types of elements constitute the image structure at the community scale and present apparent elements’ characteristics. This scrutiny is also aimed to demonstrate the construction and use process of the methodology and to provide new ideas for the cognitive research of urban spatial image at the community scale.

Street trees extraction and further acquisition of their property information is one of the current research hotspots, which can be applied in various urban management. For the processing task of automated identification of tree points from large-scale point clouds in urban road scenes, we propose an improved RandLA-Net network that takes into account both extraction accuracy and complexity. The method can eliminate the problem of insufficient feature extraction and more redundancy within the RandLA-Net model, and achieve efficient recognition of the street trees. As an application of street tree recognition results, we studied the accurate calculation method of street tree shading area and designed a density-based iterative α-shape algorithm. The method effectively solves the problems of large density differences among different point sets and overestimation of shading area caused by the existing convex hull algorithms’ unrefined contour reconstruction, and improves the calculation accuracy of the canopy shading area. Experiments on the Paris-Lille-3D dataset show that the improved RandLA-Net network improves the IoU of three classes by 3.19%~6.46% and reduces the model complexity by 1.4% compared to the original model. The density-based iterative α-shape algorithm achieves more refined contour reconstruction and solves the overestimation of shading area by 26% and 9% due to the other two convex hull algorithms. Its ability to effectively calculate accurate shading area was confirmed.

A series of urban law enforcement events involving city inspectors dispatched by the city management department can reflect some problems in smart city management, such as illegal advertising and unlicensed street operation. In this paper, we propose a model for the allocation of city inspectors and the optimization of patrol paths. The objective is to minimize the average response time and the number of inspectors. We also develop a priority-patrol-and-multiobjective genetic algorithm (DP-MOGA) to classify patrol segments according to the frequency of events and develop an improved genetic algorithm to achieve the aforementioned objective. We conduct numerical experiments using patrol data obtained from city inspectors in Zhengzhou, China, to clearly show that the proposed algorithm generates reasonable routes that reduce the average response time of events and the number of patrol inspectors. Furthermore, we test the algorithm for three different time scenarios (roads with different average numbers of events) and demonstrate the efficiency of the algorithm. The experimental results show that our proposed algorithm is more stable and efficient than other existing algorithms.

Forest fire regimes are changing as a function of increasing global weather extremes, socioeconomic development, and land use change. It is appropriate to use long-term time series satellite observations to better understand forest fire regimes. However, many studies that have analyzed the spatiotemporal characteristics of forest fires based on fire frequency have been inadequate. In this study, a set of metrics was derived from the VIIRS active fire data in China, from 2012 to 2021, through spatial extraction, spatiotemporal clustering, and spread reconstruction to obtain the frequency of forest fire spots (FFS), the frequency of forest fire events (FFE), the frequency of large forest fire events (LFFE), duration, burned area, and spread rate; these metrics were compared to explore the characteristics of forest fires at different spatiotemporal scales. The experimental results include 72.41 × 10⁴ forest fire spots, 7728 forest fire events, 1118 large forest fire events, and a burned area of 58.4 × 10⁴ ha. Forest fires present a significant spatiotemporal aggregation, with the most FFS and FFE in the Southern Region and the most severe LFFE and burned area in the Southwest Region. The FFS, FFE, and LFFE show a general decreasing trend on an annual scale, with occasional minor rebounds. However, the burned area had substantial rebounds in 2020. The high incidence of forest fires was concentrated from March to May. Additionally, 74.7% of the forest fire events had a duration of less than 5 days, while 25.3% of the forest fire events lasted more than 5 days. This helps us to understand the characteristics of more serious or higher risk forest fires. This study can provide more perspectives for exploring the characteristics of forest fires, and more data underpinning for forest fire prevention and management. This will contribute towards reasonable forest protection policies and a sustainable environment.