Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. By leveraging its advantages in image processing, deep learning technology has significantly enhanced the accuracy of plant disease and pest detection and identification. This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. It begins by outlining the limitations of traditional methods in this domain, followed by a systematic discussion of the latest developments in applying various deep learning techniques—including image classification, object detection, semantic segmentation, and change detection—to plant disease and pest identification. Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. Key challenges, such as enhancing model generalization, addressing small lesion detection, and ensuring the availability of high-quality, diverse training datasets, are critically examined. Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. Deep learning technology, with its powerful capabilities in data processing and pattern recognition, has become a pivotal tool for promoting sustainable agricultural practices, enhancing productivity, and advancing precision agriculture.

A geographic information system (GIS) is a technical system which is supported by computer software and hardware systems. It focuses on the geographical information related to the whole or part of the earth’s surface. It is used for collecting, storing, managing, calculating, analyzing, displaying, and describing geographical information. It has inherent advantages in processing geographic data and plays an indispensable role in the sustainable detection of natural resources, natural disaster risk management, urban sustainable development planning, etc. With the continuous development of technology, the integration of GIS with emerging technologies such as big data, cloud services, and artificial intelligence creates new geographic information systems and entirely new development directions. The GIS architecture is of great value for the efficient execution of GIS systems. In this process, as the organizational form of GIS systems, the GIS architecture is also constantly evolving with the intersection and integration of GIS and other technologies. This research reviews a large amount of literature on component technologies, 3D technologies, cloud computing, big data, artificial intelligence, and so on, at home and abroad and analyzes and elaborates on the current development status and trends of GIS software architecture. It discusses in detail the characteristics and future development directions of different GIS software architectures in different periods and makes delicate descriptions of their hierarchical features. This study aims to summarize the advantages and disadvantages of architectures in different stages, the interactivity from the user’s perspective. On this basis, it studies the development trends of GIS integrated with big data and artificial intelligence, summarizes the laws and experience of the evolution of its system architecture, and analyzes the technological drivers of each evolution and their impact on GIS applications. Reviewing the evolution history of GIS frameworks is expected to provide guiding references for more efficient GIS system architecture research in the future.

The Location Routing Problem (LRP) has extensive applications in emergency medical facility selection. It involves a complex spatial optimization problem of selecting the optimal facility locations from a set of demand points and candidate facility locations and planning the most efficient routes. This study introduces a two-stage solution approach based on deep reinforcement learning, which fully leverages the potential of deep reinforcement learning in handling sequential decision problems. Firstly, in the location selection stage, through the interaction between an agent and the environment, the agent needs to choose facility locations from numerous demand points to either maximize overall profit or minimize overall costs. This stage determines central points and allocates demand points to the nearest facility locations. Subsequently, in the route planning stage, the agent decides how to distribute deliveries among the pre-selected facility locations to minimize overall route length or costs. Experimental results demonstrate the effectiveness of this method in addressing location routing problems. It may provide valuable support for responding to emergencies and disaster events, helping decision-makers make more intelligent choices for facility locations and route planning, ultimately enhancing service response speed and efficiency. Furthermore, this method holds broad application potential in other domains, further advancing the development and utilization of deep reinforcement learning in spatial optimization problems.

Bus networks are a crucial support for urban commuting. By studying the evolutionary characteristics of bus networks, we can uncover their development patterns, coverage efficiency, and changes in regional balance, providing a scientific basis for sustainable urban development and the optimization of transportation resources. This study systematically analyzes the spatiotemporal evolution characteristics of the bus network in Beijing from 2006 to 2024 using specific spatial analysis tools to analyze spatiotemporal evolution characteristics. By analyzing spatial coverage rates of transit stations using road network and administrative division data, the study reveals the convenience of bus networks in different regions. By combining the research methodology of the Sustainable Development Goals (SDGs) report, a 500-m service radius for bus stops was assessed. A complex network model was used to extract the nodes and edges of the bus network, and the betweenness centrality (BC) characteristics were analyzed. The findings indicate that Beijing’s bus network has gradually expanded from the central urban areas to peripheral regions, with notable expansion in Tongzhou and Yanqing, resulting in an improved balance in the distribution of stations and routes and the emergence of Tongzhou as a new bus hub. The diffusion characteristics of the bus network are significantly influenced by administrative boundaries and the layout of the ring roads. Bus routes and stops are highly concentrated in the central urban areas and within the Second Ring Road, while as the number of ring roads increases, various network indices gradually decrease. The distribution of bus stops shows notable clustering and an uneven directional development. Beijing’s bus stop distribution exhibits significant clustering characteristics, and the areas with a high Population Conveniently Served by Buses (PCSB) are predominantly concentrated in the central urban areas, with a large gap compared to the outer suburban districts. These conclusions expand on the exploration of isolated and static characteristics of the bus network structure, revealing the dynamic mechanisms and evolution patterns of Beijing’s bus network. They provide guidance and recommendations for improving the bus network and offer more comprehensive support for urban planning and resource allocation.

Reliable knowledge of power transmission tower locations is fundamental for large-scale inspection and asset management in modern power grids. However, in satellite and aerial remote sensing imagery, towers typically appear as small, slender structures embedded in cluttered backgrounds, which leads to frequent missed and false detections. To address this challenge, we propose SCOPE-YOLO, an integrated super-resolution-plus-detection framework tailored for scalable transmission and distribution tower monitoring. In the first stage, low-resolution image patches are enhanced by a Real-ESRGAN ×4 super-resolution frontend, which restores high-frequency lattice details and sharpens tower boundaries. The reconstructed images are then processed by SCOPE-YOLO, a YOLOv11-based detector that incorporates a Cross-Scale Feature Aggregation (CFA) module, a Gather–Distribute (GD) routing mechanism, and a high-resolution P2 detection head, together with SAT and layered inference strategies to strengthen small-object perception under complex backgrounds. Experiments on the public SRSPTD dataset demonstrate that SCOPE-YOLO improves F1 score by 0.051 and raises mAP@0.5 by 10.2 percentage points over the YOLOv11-s baseline, while maintaining a compact model size. Compared with a broad set of state-of-the-art detectors, SCOPE-YOLO achieves the best overall performance, reaching 82.8% mAP@0.5 for power tower detection. Cross-domain evaluation on the GZ-PTD test set further confirms the effectiveness of the super-resolution–detection pipeline: Real-ESRGAN×4@2048 + SCOPE-YOLO increases Recall from 0.8621 to 0.9278 and mAP@0.5 from 0.8365 to 0.9132 relative to the low-resolution baseline, substantially reducing missed detections of small and weak tower targets in real-world scenes.

With urban development and a growing focus on sustainability, shared bicycles have become a popular mode of eco-friendly transportation. However, issues like chaotic parking, excessive deployment, and suboptimal distribution need solutions. This study analyzes shared bicycle usage in Wuhan, focusing on time and location patterns to inform better docking point selection. It uses GPS data from Mobike shared bicycles in October 2018, point of interest (POI) data, and population distribution data for Wuhan. The research employs mathematical and statistical analysis, spatial analysis, geographical detectors, and optimization methods. The study uncovers usage patterns, highlights travel trends, and identifies the relationship between factors like POIs, public transportation facilities, population density, and shared bicycle usage. It establishes a model called the Maximum Covering Location Problem (MCLP) for selecting docking points, considering demand intensity. In summary, this study deepens our understanding of shared bicycle usage in Wuhan, provides a model for selecting docking points, and offers valuable insights for urban transportation planning and sustainability.