Light detection and ranging (LiDAR) data of 3D point clouds acquired from laser sensors is a crucial form of geospatial data for recognition of complex objects since LiDAR data provides geometric information in terms of 3D coordinates with additional attributes such as intensity and multiple returns. In this paper, we focused on utilizing multiple returns in the training data for semantic segmentation, in particular building extraction using PointNet++. PointNet++ is known as one of the efficient and robust deep learning (DL) models for processing 3D point clouds. On most building boundaries, two returns of the laser pulse occur. The experimental results demonstrated that the proposed approach could improve building extraction by adding two returns to the training datasets. Specifically, the recall value of the predicted building boundaries for the test data was improved from 0.7417 to 0.7948 for the best case. However, no significant improvement was achieved for the new data because the new data had relatively lower point density compared to the training and test data.

The utilization of remote sensing technologies for archaeology was motivated by their ability to map large areas within a short time at a reasonable cost. With recent advances in platform and sensing technologies, uncrewed aerial vehicles (UAV) equipped with imaging and Light Detection and Ranging (LiDAR) systems have emerged as a promising tool due to their low cost, ease of deployment/operation, and ability to provide high-resolution geospatial data. In some cases, archaeological sites might be covered with vegetation, which makes the identification of below-canopy structures quite challenging. The ability of LiDAR energy to travel through gaps within vegetation allows for the derivation of returns from hidden structures below the canopy. This study deals with the development and deployment of a UAV system equipped with imaging and LiDAR sensing technologies assisted by an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) for the archaeological mapping of Dana Island, Turkey. Data processing strategies are also introduced for the detection and visualization of underground structures. More specifically, a strategy has been developed for the robust identification of ground/terrain surface in a site characterized by steep slopes and dense vegetation, as well as the presence of numerous underground structures. The derived terrain surface is then used for the automated detection/localization of underground structures, which are then visualized through a web portal. The proposed strategy has shown a promising detection ability with an F1-score of approximately 92%.

The present study investigates indirect effects of communication about suicide in interpersonal relationships and patient-provider relationships on suicidal intention via attitude, descriptive norm, injunctive norm, and self-efficacy in committing suicide among Korean adults, using longitudinal survey data. Moderation effects of perceived social support are also tested. As a result of path analysis (N = 984), communication about suicide with family, friends, and coworkers is significantly related to pro-suicide descriptive norm and pro-suicide injunctive norm. Positive attitude toward suicide, pro-suicide injunctive norm, and high self-efficacy in committing suicide are significantly related to suicidal intention. Interpersonal communication is positively related to pro-suicide injunctive norm, which in turn links to suicidal intention. Communication about suicide with healthcare professionals, however, did not show significant direct and indirect effects. Moderation effects of perceived social support are detected between interpersonal communication and pro-suicide attitude and pro-suicide injunctive norm, as well as between patient-provider communication and pro-suicide attitude.

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%.