While great progress in the development of a methodological approach to measure the accessibility of healthcare services has been made, the exclusion of the complex multi-mode travel behavior of urban residents and a rough calculation of travel costs from the origin to the destination limit its potential for making a detailed assessment, especially in urban areas. In this paper, we aim to describe and implement an enhanced method that enables the integration of multiple transportation modes into a two-step floating catchment area (2SFCA) method to estimate accessibility. We used a travel-mode choice survey, based on distance sections, to determine the complex multi-mode travel behavior of urban residents. Taking Nanjing as a study area, we proposed complete door-to-door approaches to determine every aspect of basic transportation modes. Additionally, we processed open data to implement an accurate computing of the origin-destination (OD) time cost. We applied the enhanced method to estimate the accessibility of residents to hospitals and compared it with three single-mode 2SFCA methods. The results showed that the proposed method effectively identified more accessibility details and provided more realistic accessibility values.

Coal mining can create a variety of environmental, ecological, and land-use problems. Subsidence areas resulting from coal mining are a common and particularly difficult problem to manage. Despite much discussion in the academic literature as well as among local and international stakeholders, there is neither a uniform standard nor a universally accepted approach for selecting an appropriate governance model for a subsidence area. In particular, the lack of quantitative evaluation methods and excessive subjectivity represent key obstacles to the effective selection of governance models for subsidence areas. This paper proposes a selection framework for a coal mining subsidence governance model that integrates the analytic hierarchy process (AHP) and entropy weight method (EWM). The model comprehensively considers the settlement characteristics of the subsidence area, its geographic location, the water index, as well as the vegetation index. These variables are used as indicators to develop an evaluation framework upon which different subsidence zones can be quantitatively analyzed. The selection framework is demonstrated using examples from three subsidence areas in the Huainan and Huaibei mining areas in China, for which relevant data were collected and processed with the help of field surveys, remote sensing images, and subsidence prediction software. Applying the novel selection framework, the most suitable governance model for each subsidence area was obtained and determined to be consistent with the recommendations of an academic panel composed of multiple experts. The novel selection framework has high efficacy and potential to overcome the problem of subjectivity in the selection of governance models for coal mining subsidence areas. It is also envisaged that future incorporation of the selection framework into a user-friendly software package will significantly improve the efficiency with which suitable governance models for coal mining subsidence areas are selected.

Because of their low cost and ease of deployment, side-scan sonars is one of the most widely used underwater survey instruments. However, the complexity of the marine environment and the difficulty in target acquisition limit the detection accuracy of side-scan sonars. To address these issues, this study proposes a small-sample target detection method of side-scan sonar images using the Cycle-Consistent Generative Adversarial Network (CycleGAN) model and the improved YOLOv8 model. First, considering the difficulty in obtaining side-scan sonar target images, the proposed method uses the CycleGAN model to generate pseudo-side-scan sonar images from optical images for data augmentation. Second, the original YOLOv8 model is revised by adding attention mechanisms, employing deformable convolution networks, and updating the loss function to improve the target detection accuracy of side-scan sonar images. Experimental results demonstrated the effectiveness of the CycleGAN model in generating pseudo-side-scan sonar images and better performance of the improved YOLOv8 model in the target detection of side-scan images. Moreover, the combination of data augmentation and the improved YOLOv8 model can remarkably increase the target detection accuracy of side-scan images. The proposed methods can effectively improve the target detection efficiency of underwater sonars in marine surveying.

The centroid is most often used to describe the average position of an object’s mass and has very important applications in computational geometry, applied physics, and spatial information fields, amongst others. Based on the suspension theory of physics, this paper proposes a new method to determine the centroid of a non-homogeneous polygon by the intersection of the two balance lines. By considering the inside point value and distance to the balance line, the proposed method overcomes the traditional method’s limitation of only considering the geometric coordinates of the boundary points of the polygon. The results show that the consideration of grid distance and grid value is logical and consistent with the calculation of the centroid of a non-homogeneous polygon. While using this method, a suitable value for relative parameters needs to be established according to specific application instances. The proposed method can be applied to aid in solving specific problems such as location assessment, allocation of resources, spatial optimization, and other relative uses.

Evolutionary changes in shallow lake shorelines can significantly impact wetland biodiversity transformation. This paper aims to further elucidate the wetland evolution process by investigating the temporal and spatial characteristics and rules governing lake shoreline evolution. Departing from traditional analyses of wetland area and shoreline length changes, this paper presents a comprehensive approach to quantifying typical lake shoreline evolution patterns using the concept of object increments. These evolutionary patterns are classified into four types: “expansion”, “shrinkage”, “appearance”, and “disappearance”. Using Shengjin Lake as a case study, Landsat images from 2001 to 2020 were used to extract the lake shoreline. The temporal series characteristics of different evolution patterns, the laws at the patch scale, and the characteristics of evolution direction were analyzed. The key findings are as follows. (1) The evolution of Shengjin Lake’s shoreline from 2001 to 2020 was primarily characterized by “expansion” or “shrinkage” patterns with a clear negative correlation between them. The “appearance” and “disappearance” of lake shorelines were rare. (2) The evolutionary patterns of “expansion” or “shrinkage” mainly occurred in smaller patches with a large number, while the “appearance” and “disappearance” of lake shorelines occurred mostly in larger patches with a small number, and there were no occurrences in certain years. (3) The “expansion” evolutionary pattern was more dominant in the northeast and east-by-northeast regions, while the “shrinkage” evolutionary pattern varied across the southwest and west-by-southwest regions. In conclusion, the analysis of shoreline evolution’s temporal and spatial characteristics, based on spatiotemporal object increments, can quantitatively elucidate the lake wetland evolution process and offers a novel perspective for future research on lake wetlands.