Nan Jiang

With climate change and urbanization accelerating, cities face increasing disaster risks. As critical facilities for safeguarding residents’ safety, emergency shelters have attracted growing attention due to spatial supply–demand mismatches. From the perspective of the 15-min community life circle, this study aims to develop a fine-scale analytical framework for assessing emergency shelter accessibility and inequality, using Pidu District, Chengdu, China, as a case study. The three-step floating catchment area (3SFCA) method is applied to assess emergency shelter accessibility, while the Gini coefficient and Kolm–Pollak equally distributed equivalent (EDE) are introduced to evaluate accessibility inequality across subdistricts and communities. The results show that: (1) compared with the traditional Gaussian two-step floating catchment area (G2SFCA) method, 3SFCA captures clearer accessibility differentiation and stronger local sensitivity; (2) emergency shelter accessibility shows marked spatial differentiation, with pronounced local clustering in the walking mode and a polycentric pattern in the cycling mode; (3) the Gini coefficient reveals distributional imbalance in emergency shelter accessibility, showing a clear “center–periphery” pattern in which urban areas generally exhibit lower inequality than suburban and rural areas; and (4) the Kolm–Pollak EDE emphasizes lower-tail welfare and helps distinguish “low but even” areas from “high but polarized” areas. These findings support equity-oriented strategies for emergency shelter planning and resource allocation. The study provides methodological and practical implications for fine-scale emergency shelter planning and resilient urban governance, contributing to more equitable, inclusive, resilient, and sustainable urban environments.

Terrestrial laser scanning (TLS) provides dense point clouds for landslide monitoring, yet occlusion, heterogeneous point density, and seasonal vegetation introduce noise and unstable deformation boundaries in multi-temporal change detection. To overcome the limitations of the multiscale model-to-model cloud comparison (M3C2) method under dominant downslope tangential motion and vegetation disturbance, we propose a block-wise ICP method to retrieve 3D displacement vectors. The scene is partitioned into local sub-blocks; rigid registration is performed within each sub-block, and the estimated translation is assigned to the sub-block center. A two-stage matching and quality control procedure removes under-constrained sub-blocks, enabling the direct retrieval of 3D displacement vectors and interpretable boundaries. Applied to the Longxigou landslide in Wenchuan using RIEGL VZ-2000i surveys on 1 November 2023 and 23 May 2024, the proposed method produces a more continuous displacement field and clearer boundaries than M3C2. For a tower target, manual measurements indicate a displacement of 0.41–0.63 m; our estimates are within 0.33–0.40 m, whereas M3C2 mostly falls between −0.25 and 0.25 m. In a seasonal vegetation change scene, we detect a canopy envelope expansion of approximately 0.20–0.40 m, while M3C2 shows scattered canopy responses that hinder boundary interpretation. A sensitivity analysis indicates a block-scale trade-off between boundary stability and peak preservation, motivating adaptive multi-scale blocking and uncertainty quantification.

Toppling failure is a fundamental mode of instability in rock slopes and occurs predominantly in reservoir bank anti-dip bedded rock masses. Reservoir impoundment changes seepage conditions and weakens slopes, whereas discontinuity non-persistence introduces uncertainty and complicates the identification of coupled toppling–sliding mechanisms. To address this, a probabilistic framework using the Goodman–Bray limit equilibrium method is developed. Equivalent strength parameters are introduced to unify the strength contrast between unsaturated and saturated segments along a common basal surface. Basal discontinuity connectivity is modeled as a random variable, and a Monte Carlo simulation is used to derive failure mode probabilities and a probability-weighted factor of safety. The framework is applied to the Huangcaoping anti-dip slope in the Dagangshan reservoir area at a normal water level of 1130 m. The most probable scenario has a probability of 0.116, involving sliding at 1120–1420 m and toppling at 1420–1550 m, with a probability-weighted mean factor of safety of 0.978. Predicted failure characteristics and deformation intervals are consistent with engineering observations, confirming the method’s effectiveness. This integration enables the simultaneous characterization of stability levels and the evolution mechanism. The approach provides mechanism-explicit mode likelihoods and a robust stability metric to support hazard assessment, monitoring placement, and reinforcement design.

Monitoring steep slopes in mountainous canyon areas has always been a challenging problem, especially during the construction of large hydropower projects. Effective monitoring is crucial for construction safety and operational security. However, under complex terrain conditions, existing monitoring methods have significant limitations and cannot comprehensively and accurately cover steep slopes. To address the above challenges, this study proposes a multi-temporal UAV-based photogrammetric offset tracking (POT) monitoring method assisted by terrestrial laser scanning (TLS), which is primarily applicable to rocky and texture-rich steep slopes. This method utilizes TLS point cloud data to provide supplementary ground control points (TLS-GCPs) for UAV image modeling, effectively overcoming the difficulty of deploying conventional RTK ground control points (RTK-GCPs) on high and steep slopes, thereby significantly improving the accuracy of UAV-based Structure-from-Motion (SfM) models. In a case study at a hydropower station, we employed TLS-assisted UAV modeling to produce high-precision UAV images. Using POT technology, we successfully identified signs of slope deformation between January 2024 and December 2024. Comparative experiments with traditional algorithms demonstrated that in areas where RTK-GCPs cannot be deployed, this method greatly enhances UAV modeling accuracy, fully meeting the monitoring requirements for steep slopes in complex terrains.

The task of landslide recognition focuses on extracting the location and extent of landslides over large areas, providing ample data support for subsequent landslide research. This study explores the use of UAV and deep learning technologies to achieve robust landslide recognition in a more rational, simpler, and faster manner. Specifically, the widely successful DeepLabV3+ model was used as a blueprint and a dual-encoder design was introduced to reconstruct a novel semantic segmentation model consisting of Encoder1, Encoder2, Mixer and Decoder modules. This model, named DeepLab for Landslide (DeepLab4LS), considers topographic information as a supplement to DeepLabV3+, and is expected to improve the efficiency of landslide recognition by extracting shape information from relative elevation, slope, and hillshade. Additionally, a novel loss function term—Positive Enhanced loss (PE loss)—was incorporated into the training of DeepLab4LS, significantly enhancing its ability to understand positive samples. DeepLab4LS was then applied to a UAV dataset of Baihetan reservoir, where comparative tests demonstrated its high performance in landslide recognition tasks. We found that DeepLab4LS has a stronger inference capability for landslides with less distinct boundary information, and delineates landslide boundaries more precisely. More specifically, in terms of evaluation metrics, DeepLab4LS achieved a mean intersection over union (mIoU) of 76.0% on the validation set, which is a substantial 5.5 percentage point improvement over DeepLabV3+. Moreover, the study also validated the rationale behind the dual-encoder design and the introduction of PE loss through ablation experiments. Overall, this research presents a robust semantic segmentation model for landslide recognition that considers both optical and topographic semantics of landslides, emulating the recognition pathways of human experts, and is highly suitable for landslide recognition based on UAV datasets.

Introduction: The conventional 3-D point cloud-based deformation analysis methods, such as the shortest distance (SD), cloud-to-cloud (C2C), and multiscale model-to-model cloud comparison (M3C2), essentially regard the closest distance between two periods of point cloud data as the deformation, rather than the true position of the same point in 3-D space before and after deformation.Methods: This paper proposes a method based on the ICP algorithm to calculate the differences between the chunked multi-period point clouds to recognizes the 3-D deformations.Results and discussion: The results show that the obtained results are very close to the GNSS data but with a much larger spatial monitoring range. The accuracy is higher than that of the SD method. Moreover, we analyze the statistical relationship between the point cloud block size and the deformation vector error and determine the optimal block size. The aim of this article is to optimize the deformation analysis method and improve its accuracy to provide techniques and ideas for the wider surface deformation monitoring research field. For instance, combining this method with data from contact methods constructs a 3D overall deformation model of the mountain, enabling real-time monitoring and early warning of debris flows.

Obtaining comprehensive and accurate terrain data is important for engineering construction. Unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are two widely used terrain modeling techniques. In mountainous areas, both techniques suffer limitations. These limitations occur in uninhabited areas, primarily caused by the steep terrain and inconvenient transportation conditions, resulting in poor data integrity and inadequate accuracy in UAV and TLS terrain mapping. In this article, we proposed a fusion modeling method based on UAV photogrammetry and TLS for high-precision terrain mapping in mountainous and canyon areas. The proposed method entails the use of TLS data to provide additional control points for UAV modeling, resulting in an improved accuracy of the modeling results. In addition, to quantify the optimization effect of this method, we proposed a 3D model deviation comparison method based on the iterative closest point (ICP) algorithm. This method can be employed to accurately depict the differences in distance and rotation angle between multiple terrain models. We applied this method to the Yebatan hydropower station in Southwest China, which increased the accuracy of the terrain data by 26% and expanded the effective range by over 100%.