LiDAR Dataset for Predictive Analytics in Arctic Roadway Resilience: Point Cloud Data Visualizing 72km of the Dalton Highway with Multiple Bridges (Hammon River - Chandalar), 2024

Remote sensing makes it possible to gather data rapidly, accurately, and non-destructively, allowing for access to remote areas in near real-time. LiDAR sensor data were collected along Alaska's Dalton Highway as part of continued efforts to provide more geospatial data in Arctic regions relevant to cold region research. The Dalton Highway, also referred to as Alaska Route 11, is a remote and vital transportation corridor extending approximately 414 miles from Livengood to Deadhorse near the Arctic Ocean. The highway plays a critical role in facilitating access to the Prudhoe Bay Oil Fields and is a key logistical route for energy development in the region. The survey was conducted in August 2024; the corresponding point cloud data start point is about 3.2 km from the Hammon River and shows evidence of road and bridge degradation as well as other features of the environment, offering information for predictive and terrain analytics.遥感技术可实现快速、精准且无损伤的数据采集，支持近乎实时地获取偏远区域的观测数据。作为持续为北极地区提供更多适配寒区研究的地理空间数据工作的一部分，研究团队沿美国阿拉斯加州道尔顿公路（Dalton Highway）采集了激光雷达（LiDAR）传感器数据。道尔顿公路又称阿拉斯加11号公路，是一条偏远却至关重要的交通廊道，全长约414英里，起于利文古德，止于北冰洋沿岸的戴德霍斯。该公路为普鲁德霍湾油田的通行提供关键便利，亦是本地区能源开发的核心后勤通道。本次数据采集工作于2024年8月开展，对应的点云（point cloud）数据的起始点距哈蒙河约3.2公里，该数据集记录了道路与桥梁的退化迹象及其他环境特征，可为预测分析与地形分析提供数据支撑。