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Alaska Arctic Highways LiDAR dataset for advanced infrastructure modeling
Trevor Greene1, Nafiul Nawjis1, Muhammad Umair1
1Artificial Intelligence Research (AIR) Center, University of North Dakota, Grand Forks, North Dakota 58202, USA.
Abstract:
The Alaska Arctic Highways LiDAR Dataset (AAHLD) is a curated collection of high-resolution 3D point clouds acquired along approximately 95 km of Arctic roadway corridors in Alaska, including the Dalton, Steese, and Nome-Council highways. Collected between 2022 and 2024 using a combination of ground-vehicle and uncrewed-aircraft LiDAR platforms, the dataset comprises 826 spatial tiles per processing stage, organized into raw, segmentation-reference, and filter-reference products, for a total of 2478 LAZ files and approximately 16.14 GiB. The data were captured with a LiDAR USA Surveyor 32 system, which the manufacturer specifies to support vertical accuracy better than 2 cm and point densities exceeding 300 pts/m2 under appropriate acquisition conditions. The release also includes Nome-only CSV damage labels documenting 3906 unique damage instances across potholes, cracks, edge cracks, and washed-out road sections. The dataset is openly available through the University of North Dakota (UND) Commons repository at https://commons.und.edu/data/39/ under a Creative Commons Attribution-NonCommercial 4.0 International license. AAHLD is intended to support research in Arctic infrastructure monitoring, road segmentation, road damage detection, and machine learning model development in cold-region transportation environments.
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