National Administration of Surveying, Mapping and Geoinformation of China

Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation.

Due to the underground or enclosed nature of subway systems, satellite navigation signals are often difficult to receive. Without reliable signals, mobile measurement systems are prone to significant positioning errors. To address this challenge, this study proposes an improved method that leverages control points, dynamic calibration, and advanced data fusion. A unified coordinate system is established for all sensors, ensuring that measurements from different devices, such as inertial navigation systems and laser scanners, can be integrated seamlessly. System errors are identified and corrected through dynamic calibration to maintain long-term accuracy. Object detection technologies are used to identify subway control points, enabling trajectory correction through coordinate-assisted updates. The calibrated parameters are then applied to fuse inertial navigation data with laser scanning data, effectively reducing the accumulation of errors. This approach generates high-precision three-dimensional point clouds with absolute coordinates, suitable for applications such as subway inspection and tunnel monitoring in signal-denied environments. Results show that the method achieves deviations within 1 cm for horizontal measurements and 5 mm for vertical measurements. This significant improvement enhances the precision and reliability of laser scanning data in underground applications. The study also analyzes the spatial distribution of control points on point cloud quality, providing a practical solution for improving mobile measurement systems where satellite signals are unavailable.

Building change detection (CD) in high-resolution remote sensing imagery is crucial for urban sustainable development, rapid disaster response, and environmental monitoring. However, existing deep learning methods still struggle with the inherent complexity, dynamic change patterns, and high-noise characteristics of such imagery, particularly because conventional feature fusion approaches fail to effectively capture and decouple complex spatiotemporal evolution semantics between bitemporal images. To address these issues, this study proposes the global-local dual-domain co-aware and cross-spatiotemporal joint modeling network (GLCSNet), an efficient CD framework based on a hierarchical encoder–decoder mixed module. GLCSNet consists of two core modules. The global-local dual-domain co-aware block reduces computational cost through progressive spatial reduction while enhancing the discriminability and signal-to-noise ratio of local details by integrating a spatio-frequency dual-domain feature refinement network. The cross-spatiotemporal joint modeling block constructs heterogeneous relational views via spatiotemporal semantic reorganization, employs an omnidirectional scanning Mamba mechanism for efficient sequence modeling, and further applies temporal decomposition and difference fusion to model spatiotemporal evolution. Extensive experiments on urban datasets (LEVIR-CD and WHU-CD) and disaster datasets (CEBD and CWBD) demonstrate that GLCSNet consistently outperforms state-of-the-art methods across all metrics, for example, achieving 0.90%–5.36% IoU improvement on the LEVIR-CD dataset. Meanwhile, GLCSNet maintains a lightweight parameter scale and high computational efficiency, providing an effective solution for high-resolution remote sensing building CD.

Abstract Multiple hazard risks (MHRs) in coastal zones will continue to increase due to climate change and rising sea levels. These disproportionate impacts create shared challenges that require cross‐border mitigation efforts. However, understanding how coastal areas exhibit common risk patterns and how such patterns can inform risk‐based cooperation remains fairly limited. Here, we investigated international cooperation potential among 126 coastal countries from an integrated perspective of their risk similarity, geopolitical stance, and knowledge exchange. We conducted a high‐resolution assessment of multiple hazard risks (including earthquakes, landslides, flooding, and cyclones) and developed a bottom‐up similarity measure to identify common risk profiles across regions from both size and space. Our analysis revealed a notably high degree of risk similarity across country pairs, suggesting greater potential for cooperation than that previously recognized; 89% of country pairs with high risk similarity lacked strong partnerships in consensus‐building or knowledge‐sharing. This cooperation gap was even more pronounced in the Global South and small island developing states. Instead of relying solely on geographic proximity or existing alliances, we argue for a shift in focus toward partnerships grounded in shared risk challenges. This approach can help to build collective resilience to achieve Sustainable Development Goals for climate action (SDG 13) and partnerships for the goals (SDG 17).

Cultivated land concentration and contiguity, as a core element of agricultural modernization development, holds strategic significance for enhancing agricultural production efficiency and ensuring national food security. This study employs vector patches as research units and classifies spatial connections between patches into direct and indirect connections. We quantify six types of spatial relationships between patches using binary encoding, enabling precise delineation of concentrated contiguous cultivated land. A Patch Connectivity Index is proposed. Combined with the Patch Area Index and Patch Shape Index, an evaluation system for cultivated land concentration and contiguity is established. Using Suixi County as a case study, we investigate the spatiotemporal evolution of its cultivated land concentration and contiguity from 2019 to 2023. Overall, patch connectivity exhibits a “single-element dominant, multi-element complementary” structural pattern, while the evaluation grading of cultivated land concentration and contiguity follows a normal distribution. Between 2019 and 2023, the average patch area decreased while the average number of connections between patches increased, indicating significant improvement in cultivated land concentration and contiguity levels. By adjusting spatial relationships between patches, the effective integration and utilization of cultivated land resources can provide theoretical foundations and practical references for agricultural modernization development.

Abstract. Place names are important basic geographic information resources for surveying and mapping. They play a significant role in maintaining national security, national defense construction, economic development, and other aspects. The construction and development of global geographic information resources require the continuous, efficient acquisition, and integration and updating of place names data. This paper takes the boundaries of the lowest administrative units in the task area as the matching scope and employs a string edit distance algorithm based on the substitution rules of common name synonyms for the similarity calculation and integration and updating of place names. It also uses a multilingual translation method based on a transliteration rule library for foreign language translation, with the addition of collaborative processing verification. This ultimately improves the matching efficiency of multi-source place names elements and ensures the accuracy of foreign place names translation. The research findings have been tested in some national regions and can provide a reference for the production and updating of place names data worldwide.

Lane-level high-definition map (HDMap) automatic construction from aerial imagery can significantly enhance the efficiency of HDMap production. However, some problems limit the application of aerial imagery in HDMap construction. First, the bird’s eye view of aerial imagery often causes occlusion, which leads to interrupted map element predictions. In addition, existing methods for aerial imagery-based HDMap construction can only construct road-level map and the postprocessing is complicated. To solve these problems, we propose AerialHDMapper, an end-to-end framework that predicts lane-level vectorized map elements directly requiring no postprocessing. The framework improves prediction accuracy in occluded areas through the introduction of the directional attention (DirAttn) module. AerialHDMapper is mainly composed of two parts: the image encoder and the polyline generator. The image encoder integrates DirAttn modules, which leverage information along and perpendicular to the direction of a map element, thereby improving the representation of the current position. The polyline generator employs a modified transformer architecture to identify long-sequence irregular map elements. Experimental results demonstrate that AerialHDMapper outperforms existing methods on both the CARLA Simulator Dataset and the Aerial Argoverse2 Dataset we collected. Extensive ablation experiments further reveal that our approach exhibits strong robustness and generalization capabilities.