Dark ships, vessels deliberately disabling their AIS signals, constitute a grave maritime safety hazard, with detection efforts hindered by issues like over-reliance on AIS, inadequate surveillance coverage, and significant mismatch rates. This paper proposes an improved multi-feature association method that integrates satellite remote sensing and AIS data, with a focus on oriented bounding box course estimation, to improve the detection of dark ships and enhance maritime surveillance. Firstly, the oriented bounding box object detection model (YOLOv11n-OBB) is trained to break through the limitations of horizontal bounding box orientation representation. Secondly, by integrating position, dimensions (length and width), and course characteristics, we devise a joint cost function to evaluate the combined significance of multiple features. Subsequently, an advanced JVC global optimization algorithm is employed to ensure high-precision association in dense scenes. Finally, by integrating data from Gaofen-6 (optical) and Gaofen-3B (SAR) satellites, a day-and-night collaborative monitoring framework is constructed to address the blind spots of single-sensor monitoring during night-time or adverse weather conditions. Our results indicate that the detection model demonstrates a high average precision (AP50) of 0.986 on the optical dataset and 0.903 on the SAR dataset. The association accuracy of the multi-feature association algorithm is 91.74% in optical image and AIS data matching, and 91.33% in SAR image and AIS data matching. The association rate reaches 96.03% (optical) and 74.24% (SAR), respectively. This study provides an efficient technical tool for maritime safety regulation through multi-source data fusion and algorithm innovation.

Maritime unmanned aerial vehicle (UAV) ship detection faces challenges including variations in ship pose and appearance under multiple viewpoints, occlusion and confusion in dense scenes, complex backgrounds, and the scarcity of ship datasets from UAV tilted perspectives. To overcome these obstacles, this study introduces a high-quality dataset named Marship-OBB9, comprising 11,268 drone-captured images and 18,632 instances spanning nine typical ship categories. The dataset systematically reflects the characteristics of maritime scenes under diverse scales, viewpoints, and environmental conditions. Based upon this dataset, we propose a novel detection network named YOLO11-UAVShip. First, an oriented bounding box detection mechanism is incorporated to precisely fit ship contours and reduce background interference. Second, a newly designed CK_DCNv4 module, integrating deformable convolution v4 (DCNv4) and a C3k2 backbone structure, is developed to enhance geometric feature extraction under aerial oblique view. Additionally, for ships with large aspect ratios, SGKLD effectively addresses the localization challenges in dense environments, achieving robust position regression. Comprehensive experimental evaluation demonstrates that the proposed method yields a 2.1% improvement in mAP@0.5 and a 2.3% increase in recall relative to baseline models on the Marship-OBB9 dataset. While maintaining real-time inference speed, our approach greatly enhances detection accuracy and robustness. This work provides a practical and deployable solution for intelligent ship detection in UAV imagery.

Earthquakes have obvious influences on the spatiotemporal changes in soil erosion intensity in earthquake-stricken areas. However, fewer studies have been conducted to evaluate the spatiotemporal changes in soil erosion before and after the Wenchuan earthquake and its dominant factors in different periods. In order to explore the above issue, this study quantitatively analyzed the spatiotemporal variation characteristics of soil erosion in the Wenchuan earthquake-stricken area from 2000 to 2019 based on the RUSLE model, gravity center model, and its dominant factors in different periods were determined using Geodetector. The research results indicated that: (1) The amount of mean total erosion in the Wenchuan earthquake-stricken area during 2000–2019 was 10.05 × 10⁸ t, with an average soil erosion modulus of 2038.2 t/(km²·a), indicating mild erosion. (2) The spatiotemporal patterns of soil erosion changed greatly in the Wenchuan earthquake-stricken areas during 2000–2019. Areas with intensified soil erosion were mainly distributed in Lixian, Wenchuan, Xiaojin, and other areas near the Longmenshan fault zones. (3) Landslides, debris flows, and floods caused by the Wenchuan earthquake contributed to aggravating the soil erosion intensity in the stricken area. (4) During 2000–2019, the soil erosion intensity showed an overall decreasing trend, while the soil erosion intensity showed an increasing trend around 2008 due to the Wenchuan earthquake. (5) During 2000–2019, soil erosion in the Wenchuan earthquake-stricken area has been greatly affected by vegetation, terrain, and land use types. The research results could provide important decision-making support for soil erosion prevention and ecosystem restoration in the Wenchuan earthquake-stricken area. In addition, these results would be conducive to revealing and understanding the interactive process of “Water–Soil–Vegetation” in mountainous regions all over the world.

We collected data from approximately 500 tourism destinations and revenues of Xinjiang region, China, to assess tourism resources and improve tourism industry development. We analyzed the characteristics, geographical distributions, and environmental conditions of these tourism destinations, summarized changes in regional tourism revenue over the past 34 years, and proposed strategic suggestions for the region’s tourism development. Historical sites and architectures were found to be dominant, followed by natural scenic sites, with major hotspots located in the Altay Mountains, Urumqi-Turpan-Hami line, Ili region, and Aksu-Kashgar-Hotan line. A total of 65% of tourism destinations are located mainly in low altitudes (2,000 m). They correlate spatially with rivers and roads, often in areas with high heatwave index values. Although tourism revenues have grown rapidly in the past decades, with 1998, 2010, and 2015 as breakpoint years, Xinjiang’s tourism volume remains relatively low compared to China’s, especially in international tourism. In 2015, Xinjiang’s total tourism revenue made up 2.9% of China’s total, with international tourism accounting for just 0.5%. Based on the analysis, there is significant potential for tourism development in the Xinjiang region. Hence, we suggest developing mountainous forest tourism based on forest parks in middle and high-altitude areas with relatively lower heatwave index values, targeting the European market, expanding cultural tourism based on diverse ethnic groups, and creating “Silk Road” tourism routes with other western Chinese provinces.

As urbanization accelerates in China, the protection and renewal of historical and cultural districts have become key issues. The Beiyuanmen Historical and Cultural District in Xi’an, with its long history and cultural significance, is a prime example. This study uses Beiyuanmen as a case study, employing Baidu heatmap data, Point of Interest (POI) data, and space syntax theory to examine the district’s spatial layout, crowd activity distribution, and functional structure. The purpose is to quantify its vitality and spatial characteristics, providing a basis for scientific planning. The methods involve analyzing spatiotemporal crowd activity intensity via heatmaps, assessing street network configuration through integration and choice values, and comparing POI data from 2014 and 2024 to track functional evolution. The research identifies the distinctive spatiotemporal patterns of crowd activity, revealing not only a southeast concentration correlated with urban functions but also distinct diurnal rhythms—a bimodal pattern on weekdays versus a sustained leisure-oriented pattern on weekends, underscoring a functional shift. It also explores the directed permeability of the spatial structure, identifying streets like Miaohou Street that form a highly integrated “cross-shaped backbone”. Analysis of POI data shows that commercial services dominate and have expanded outward, with the growth rate of POI density in the control area surpassing that of the core, indicating a trend of functional diffusion. Finally, the study highlights Miaohou Street, Beiguangji Street, Damai Market Street, Beiyuanmen, and Sajinqiao as key areas, and it concludes by proposing integrated planning recommendations that focus on four strategic aspects—spatial and crowd activity distribution management, functional zoning guidance, enhancement of public services and cultural displays, and alignment with broader urban policies—for prioritized landscape enhancement and tourism.

Unmanned aerial vehicle (UAV) video and Automatic Identification System (AIS) data provide high-frequency visual observations and low-frequency identity/location messages, respectively. However, these two types of sensor data differ in spatiotemporal dimensions and pose engineering challenges for association and matching. This paper proposes a scene-adaptive trajectory association framework for airborne maritime perception. First, visual trajectories are mapped into a geospatial coordinate system using camera, gimbal, and flight platform metadata. Second, sparse AIS position sequences are filtered with physical constraints and locally interpolated to reconstruct continuous trajectories. Finally, based on target motion significance and local candidate complexity, the framework dynamically switches between Instantaneous Synchronization Multi-Feature Association (ISMA) and Spatiotemporal Constraint Weighted Dynamic Time Warping (STC-WDTW), leveraging kinematic features to deeply optimize the matching strategy, significantly enhancing robustness in trajectory association. This study collects data from the Port of Tianjin and adjacent coastal waters and evaluates the proposed method based on this dataset. Experimental results demonstrate that the proposed method achieves 94.34% IDP, 92.02% IDR, 93.17% IDF1, and 86.50% MOFA. The proposed method can be applied to marine dynamic monitoring systems, providing technical support and practical value for vessel traffic management in complex maritime areas and multi-source data fusion.