Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space–time disaggregation.

Pavement crack detection is of significant importance in ensuring road safety and smooth traffic flow. However, pavement cracks come in various shapes and forms which exhibit spatial continuity, and algorithms need to adapt to different types of cracks while preserving their continuity. To address these challenges, an innovative crack detection framework, CrackDiff, based on the generative diffusion model, is proposed. It leverages the learning capabilities of the generative diffusion model for the data distribution and latent spatial relationships of cracks across different sample timesteps and generates more accurate and continuous crack segmentation results. CrackDiff uses crack images as guidance for the diffusion model and employs a multi-task UNet architecture to predict mask and noise simultaneously at each sampling step, enhancing the robustness of generations. Compared to other models, CrackDiff generates more accurate and stable results. Through experiments on the Crack500 and DeepCrack pavement datasets, CrackDiff achieves the best performance (F1 = 0.818 and mIoU = 0.841 on Crack500, and F1 = 0.841 and mIoU = 0.862 on DeepCrack).

In recent years, point cloud segmentation technology has increasingly played a pivotal role in tunnel construction and maintenance. Currently, traditional methods for segmenting point clouds in tunnel scenes often rely on a multitude of attribute information, including spatial distribution, color, normal vectors, intensity, and density. However, the underground tunnel scenes show greater complexity than road tunnel scenes, such as dim light, indistinct boundaries of tunnel walls, and disordered pipelines. Furthermore, issues pertaining to data quality, such as the lack of color information and insufficient annotated data, contribute to the subpar performance of conventional point cloud segmentation algorithms. To address this issue, a 3D point cloud segmentation framework specifically for underground tunnels is proposed based on the Segment Anything Model (SAM). This framework effectively leverages the generalization capability of the visual foundation model to automatically adapt to various scenes and perform efficient segmentation of tunnel point clouds. Specifically, the tunnel is first sliced along its direction on the tunnel line. Then, each sliced point cloud is projected onto a two-dimensional plane. Various projection methods and point cloud coloring techniques are employed to enhance SAM’s segmentation performance in images. Finally, the semantic segmentation of the entire underground tunnel is achieved by a small set of manually annotated semantic labels used as prompts in a progressive and recursive manner. The key feature of this method lies in its independence from model training, as it directly and efficiently addresses tunnel point cloud segmentation challenges by capitalizing on the generalization capability of foundation model. Comparative experiments against classical region growing algorithms and PointNet++ deep learning algorithms demonstrate the superior performance of our proposed algorithm.

The excavation tunnel model is an important reference for mine equipment control and tunnel deformation monitoring. Currently, tunnel models are mainly created manually, and point cloud reconstruction algorithms are difficult to directly apply to tunnel point clouds. To address these issues, this paper proposes a point cloud-based excavation tunnel modeling method. First, preprocessing algorithms such as point cloud coordinate transformation, tunnel point cloud extraction, and tunnel point cloud completion are used to filter out equipment point clouds inside the tunnel and repair occluded holes. Then, the tunnel centerline is extracted, and consistency optimization is performed on the point cloud normal vectors. Finally, a tunnel model is established based on the Poisson modeling algorithm, enabling high-precision tunnel modeling. The proposed algorithm’s accuracy and effectiveness are demonstrated through experiments on four different coal mine tunnels.

Point of Interest (POI) data play a critical role in enabling location-based services (LBS) by providing intrinsic attributes, including geographic coordinates and semantic categories, alongside a spatial context that reflects relationships among POIs. However, the inherent label sparsity in POI datasets poses significant challenges for traditional supervised learning approaches. To address this limitation, we propose MaskPOI, a novel self-supervised learning framework that combines the strengths of graph neural networks and masked modeling. MaskPOI incorporates two complementary modules: an edge mask-based graph autoencoder that models the spatial topology by predicting edge existence and uncovering hidden spatial relationships and a feature mask-based graph autoencoder that reconstructs masked node features to explore the rich attribute characteristics of POIs. Together, these modules enable MaskPOI to jointly capture the spatial and attribute information essential for robust representation learning. Extensive experiments demonstrate MaskPOI’s effectiveness in improving performance on downstream tasks such as functional zone classification and population density prediction. Ablation studies further validate the contributions of its components, highlighting MaskPOI as a powerful and versatile framework for POI representation learning.