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 proliferation of shared electric scooters (E-scooters) has brought convenience to urban transportation but has also introduced challenges such as disorderly parking and an imbalance between supply and demand. Given the current inconsistent quantity and spatial distribution of shared E-scooters, coupled with inadequate research on deployment stations selection, we propose a novel maximal covering location problem (MCLP) based on distance tolerance. The model aims to maximize the coverage of user demand while minimizing the sum of distances from users to deployment stations. A deep reinforcement learning (DRL) was devised to address this optimization model. An experiment was conducted focusing on areas with high concentrations of shared E-scooter trips in Chicago. The solutions of location selection were obtained by DRL, the Gurobi solver, and the genetic algorithm (GA). The experimental results demonstrated the effectiveness of the proposed model in optimizing the layout of shared E-scooter deployment stations. This study provides valuable insights into facility location selection for urban shared transportation tools, and showcases the efficiency of DRL in addressing facility location problems (FLPs).

This study introduces a novel deep learning-based model, UniU-Net, designed to achieve the high-precision segmentation of areca palms in remote sensing imagery. UniU-Net incorporates an auxiliary encoder and a unified attention fusion module (UAFM), enhancing the model’s anti-overfitting capabilities to improve its overall segmentation performance. Specifically, the primary and auxiliary encoders, through isomorphic parallel processing, leverage the principles of structural reparameterization to enhance the model’s effective learning of areca palm features while reducing the risk of overfitting. The UAFM utilizes a spatial attention mechanism to facilitate the effective fusion of multi-scale features. This architecture enables the model to capture intricate morphological details and accurately delineate the boundaries of areca palms, even under complex and heterogeneous environmental conditions such as mixed vegetation and varying illumination. To validate the effectiveness of UniU-Net, comprehensive experiments were conducted on a specialized areca palm dataset, demonstrating superior performance compared to several state-of-the-art semantic segmentation models. The proposed method achieves significant improvements in key evaluation metrics, such as the F1-score and intersection over union (IoU), highlighting its robustness and precision in automated areca palm extraction tasks. The integration of advanced attention mechanisms not only enhances the model’s ability to focus on relevant regions but also improves the segmentation accuracy in challenging scenarios. Beyond the specific application of areca palm segmentation, the methodologies introduced in this study hold substantial practical significance for broader agricultural applications, such as precision farming and crop monitoring.

In the automated modeling generated by oblique photography, various terrains cannot be physically distinguished individually within the triangulated irregular network (TIN). To utilize the data representing individual features, such as a single building, a process of building monomer construction is required to identify and extract these distinct parts. This approach aids subsequent analyses by focusing on specific entities, mitigating interference from complex scenes. A deep convolutional neural network is constructed, combining U-Net and ResNeXt architectures. The network takes as input both digital orthophoto map (DOM) and oblique photography data, effectively extracting the polygonal footprints of buildings. Extraction accuracy among different algorithms is compared, with results indicating that the ResNeXt-based network achieves the highest intersection over union (IOU) for building segmentation, reaching 0.8255. The proposed “dynamic virtual monomer” technique binds the extracted vector footprints dynamically to the original oblique photography surface through rendering. This enables the selective representation and querying of individual buildings. Empirical evidence demonstrates the effectiveness of this technique in interactive queries and spatial analysis. The high level of automation and excellent accuracy of this method can further advance the application of oblique photography data in 3D urban modeling and geographic information system (GIS) analysis.