The accuracy of road damage detection models based on UAV remote sensing images is generally low, mainly due to the challenges posed by the complex background of road damage, diverse forms, and necessary computational requirements. To tackle the issue, this paper presents CSGEH-YOLO, a lightweight model tailored for UAV-based road damage detection in intricate environments. (1) The star operation from StarNet is integrated into the C2f backbone network, enhancing its capacity to capture intricate details in complex scenes. Moreover, the CAA attention mechanism is employed to strengthen the model’s global feature extraction abilities; (2) a cross-scale feature fusion strategy known as GFPN is developed to tackle the problem of diverse target scales in road damage detection; (3) to reduce computational resource consumption, a lightweight detection head called EP-Detect has been specifically designed to decrease the model’s computational complexity and the number of parameters; and (4) the model’s localization capability for road damage targets is enhanced by integrating an optimized regression loss function called WiseIoUv3. Experimental findings indicate that the CSGEH-YOLO algorithm surpasses the baseline YOLOv8s, achieving a 3.1% improvement in mAP. It also reduces model parameters by 4% and computational complexity to 78%. In contrast to alternative methods, the model proposed in this paper significantly reduces computational complexity while improving accuracy. It offers robust support for deploying UAV-based road damage detection models.

The equity of medical services is crucial for the quality of life of a population and the sustainable development of cities. Current research on optimizing the maximal equity of medical facilities has the following limitations: (1) In the accessibility calculation models for multiple transportation modes, the impact of factors such as public transport transfers and travel distance on the choice of transportation mode is often overlooked. (2) Existing spatial equity indicators are mostly derived from the overall study area, failing to fully consider the differences in population distribution and development gaps within different development zones inside the region. This study proposes a novel Incorporating Multiple Transportation Two-Step Floating Catchment Area (IMT-2SFCA) and a Hierarchical Theil-based Maximal Accessibility Equality model (HT-MAE) to optimize the equity of access to tuberculosis medical institutions in Wuhan. The findings reveal that, compared to single-mode transportation accessibility models, the multimodal accessibility model integrates the characteristics of four transportation modes, providing a more realistic reflection of residents’ access to medical services. The optimization results show a significant improvement in the equity of access to medical services across Wuhan and among different economic development zones, although the equity indicators in non-central urban areas have declined. The results of this study provide a theoretical basis and practical insights for alleviating the inequality of access to medical services across different urban layers.

The rectangular cartogram is a geospatial visualization method that blends the characteristics of maps and charts. By simplifying geographic regions into rectangles and using the area of each rectangle to represent statistical data, it enables efficient geovisualization. This paper summarizes and analyzes the advantages and limitations of two main approaches used in current rectangular cartogram construction algorithms. To address the issues of high computational cost and inadequate preservation of adjacency and relative positional relationships in existing algorithms, we propose and implement a new rectangular cartogram construction algorithm. This algorithm simplifies the layout computation process while ensuring that the adjacency and relative positional relationships between regions during the layout generation process have only minor errors. In adjusting rectangle areas to match attribute values, the algorithm adopts a “region-by-region placement” strategy, ensuring that errors in area accuracy remain within a small range, while also keeping errors in adjacency and relative positional relationships minimal. Finally, by comparing the results of our algorithm with those of existing algorithms using real-world data with varying distribution characteristics, we demonstrate its effectiveness. The results show that the proposed algorithm not only improves computational efficiency but also effectively displays the adjacency and relative positional relationships between regions.