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Infrastructure across the circumpolar Arctic is exposed to permafrost thaw hazards caused by global warming and human activity, creating the risk of damage and economic losses. However, losses are underestimated in existing literature due to incomprehensive infrastructure maps. Here, we mapped infrastructure from 0.5 m resolution satellite imagery of 285 Alaskan communities with a deep learning detection model. Combined with OpenStreetMap, we mapped a statewide Alaskan building footprint of 53 M m2 and a road network of 50,477 km. With deep learning, we expanded the OpenStreetMap building footprint by 47% statewide and 86% on discontinuous and continuous permafrost. Doubling the amount found in existing literature by using our improved map, we estimated that building and road losses due to permafrost thaw could cost Alaska $37B to $51B under the SSP245 and SSP585 scenarios, respectively. Finally, we highlight shortcomings in U.S. national risk assessments, which do not account for Alaskan permafrost hazards. 

Ice wedges, which are ubiquitous in permafrost areas, play a significant role in the evolution of permafrost landscapes, influencing the topography and hydrology of these regions. In this paper, we combine a detailed multi-generational, interdisciplinary, and international literature review along with our own field experiences to explore the development of low-centered ice-wedge polygons and their orthogonal networks. Low-centered polygons, a type of ice-wedge polygonal ground characterized by elevated rims and lowered wet central basins, are critical indicators of permafrost conditions. The formation of these features has been subject to numerous inconsistencies and debates since their initial description in the 1800s. The development of elevated rims is attributed to different processes, such as soil bulging due to ice-wedge growth, differential frost heave, and the accumulation of vegetation and peat. The transition of low-centered polygons to flat-centered, driven by processes like peat accumulation, aggradational ice formation, and frost heave in polygon centers, has been generally overlooked. Low-centered polygons occur in deltas, on floodplains, and in drained-lake basins. There, they are often arranged in orthogonal networks that comprise a complex system. The prevailing explanation of their formation does not match with several field studies that practically remain unnoticed or ignored. By analyzing controversial subjects, such as the degradational or aggradational nature of low-centered polygons and the formation of orthogonal ice-wedge networks, this paper aims to clarify misconceptions and present a cohesive overview of lowland terrain ice-wedge dynamics. The findings emphasize the critical role of ice wedges in shaping Arctic permafrost landscapes and their vulnerability to ongoing climatic and landscape changes. 

This dataset, called DARTS, comprises footprints of retrogressive thaw slump (RTS) identified and quantified using an automated deep learning approach in RTS hotspots across the Arctic and Subarctic permafrost regions. We utilized multispectral PlanetScope imagery with a spatial resolution of ~3 meters (m), complemented by ArcticDEM (Digital Elevation Model) and derived datasets, including slope, relative elevation, and Landsat-derived change trends. The dataset covers an area of 1.6 million square-kilometers (km²), with at least one coverage between 2021 and 2023, and provides annual coverage for approximately 900,000 km². In several highly active key sites, such as Banks Island, Peel Plateau, and Novaya Zemlya, we extended the data frequency and temporal coverage to 2018-2023. We mapped a total of more than 43,000 individual RTS and ALD, many of them multiple times. We offer two levels of datasets; Level 1: RTS footprints per image with timestamps; and Level 2: annually aggregated RTS footprints. Essential metadata includes image footprints, dataset coverage, timestamps, and model-specific information. To enhance reproducibility and further use, the training labels, processing code, and model checkpoints are publicly available. This version, v1.1, is the revised first openly accessible release. The dataset will be maintained and continuously updated in both spatial and temporal extent. It can be used for mapping and quantifying RTS, analyzing spatio-temporal patterns of RTS dynamics, or serving as input for landscape dynamics models. 

Much of the Arctic tundra is underlain by a network of ice wedges that formed during millennia of repeated frost cracking on cold winter days and later infilling of snowmelt water. Growing ice wedges push the soil upwards, forming connected ridges on the ground surface and the ubiquitous ice-wedge polygon tundra. Melting of the top of the ice wedge causes the ground surface to collapse with the rims transforming into snow- and water-collecting troughs — a phenomenon observed at multiple sites across the Arctic tundra in a decade or less. Continued melt establishes a new drainage network only a metre or two wide and less than a half-metre deep, where a doubling of runoff and reduced surface water storage is possible without changes in precipitation. Across the Arctic, lakes are disappearing, while precipitation and river runoff are increasing. So far, the sub-metre microtopographical changes have not entered the scientific analyses encompassing regional and pan-Arctic hydrology. The data and technology are now here to quantify the network of ice wedges across large regions and, though individually small, the ice wedges add up to large numbers. What at first may appear as contradicting hydrological change (for example, shrinking lakes despite increasing precipitation) could be explained by a sudden evolution of the stream network where the new channels are narrow but bountiful: the capillaries of the Arctic tundra hydrological system. 

Ice-wedge polygon (IWP) is a landform found in landscapes underlain by permafrost. IWPs form due to the development of ice wedges, where each IWP is bounded by ice wedges. Ice wedges form due to repeated cracking of the soil during winter and by snowmelt water infiltrating into the cracks and freezing. Repeated over thousands of years, the process results in ice wedges several 10s of feet deep. The melting of the top of the ice wedge results in ground subsidence and depending how extensive the thaw is across the landscape, new ponds or lateral drainage channels form. This data collection supported an assessment of the length of the ice wedge network in the Barnard River watershed (10,540 km2), Banks Island, Canada. The data collection is derived from the pan-Arctic map of ice-wedge polygons (Witharana et al. 2023, Ice-wedge polygon detection in satellite imagery from pan-Arctic regions, Permafrost Discovery Gateway, 2001-2021. Arctic Data Center. doi:10.18739/A2KW57K57), which used Maxar satellite imagery from 2010-2020 for Banks Island. Two types of datasets are included: (1) Polyline shapefile of mapped ice wedge centerlines. This dataset was produced with an approach adopted from Ulrich, Mathias, et al. "Quantifying wedge‐ice volumes in Yedoma and thermokarst basin deposits." Permafrost and Periglacial Processes 25.3 (2014): 151-161. A buffer that represents widths at the top of ice wedges is created around each IWP. A buffer width of 5 meters was chosen, since this allowed buffers of adjacent polygons to overlap. These buffers are then skeletonized in order to trace their centerlines, which ultimately represents the network of ice-wedges that form the IWPs in a landscape. (2) Polygon shapefile of IWP coverage (as percentage of land cover within 1 kilometer (km) x 1 km rectangular grid cells) across the 10,540 km2 Bernard River Watershed, Banks Island, Canada. Code for ice-wedge centerline extraction can be found at https://github.com/PermafrostDiscoveryGateway/IW-Network-Extraction. This data collection accompanies the manuscript published in Nature Water (Liljedahl, A.K., Witharana, C., and Manos, E., 2024. The Capillaries of the Arctic Tundra. Nature Water, doi:10.1038/s44221-024-00276-9) and the geospatial data is available to view in the Permafrost Discovery Gateway. 

Climate change pressure on the Arctic permafrost is rising alarmingly, creating a decisive need to produce Pan-Arctic scale permafrost landform and thaw disturbance information from remote sensing (RS) data. Very high spatial resolution (VHSR) satellite images can be utilized to detect ice-wedge polygons (IWPs) – the most important and widespread landform in the Arctic tundra region - across the Arctic without compromising spatial details. Automated analysis of peta-byte scale VHSR imagery covering millions of square kilometers is a computationally challenging task. Traditional semantic segmentation requires the use of task specific feature extraction with conventional classification techniques. Semantic complexity of VHSR images coupled with landscape heterogeneity makes it difficult to use conventional classification approaches to produce Pan-Arctic scale geospatial products. This leads to adapting deep convolutional neural network (DLCNN) approaches that have excelled in computer vision (CV) applications. Transitioning domains from everyday image understanding to remote sensing image analysis is challenging. This study aims to systematically investigate two main obstacles confronted when adapting DLCNNs in large-scale RS image analysis tasks; 1) the limited availability labeled data sets and 2) the prohibitive nature of hyperparameter tunning when designing DLCNNs that can capture the rich characteristics embedded in remotely-sensed images. With a case study on the production of the first pan-Arctic ice-wedge polygon map using thousands of VHSR images, we demonstrate the use of transfer learning and the impact of hyperparameter tuning with a 16% improvement of the Mean Average Precision (mAP50). 

Risk assessment of infrastructure exposed to ice-rich permafrost hazards is essential for climate change adaptation in the Arctic. As this process requires up-to-date, comprehensive, high-resolution maps of human-built infrastructure, gaps in such geospatial information and knowledge of the applications required to produce it must be addressed. Therefore, this study highlights the ongoing development of a deep learning approach to efficiently map the Arctic built environment by detecting nine different types of structures (detached houses, row houses, multi-story blocks, non-residential buildings, roads, runways, gravel pads, pipelines, and storage tanks) from recently-acquired Maxar commercial satellite imagery (<1 m resolution). We conducted a multi-objective comparison, focusing on generalization performance and computational cost, of nine different semantic segmentation architectures. K-fold cross validation was used to estimate the average F1-score of each architecture and the Friedman Aligned Ranks test with the Bergmann-Hommel posthoc procedure was applied to test for significant differences in generalization performance. ResNet-50-UNet++ performs significantly better than five out of the other eight candidate architectures; no significant difference was found in the pairwise comparisons of ResNet-50-UNet++ to ResNet-50-MANet, ResNet-101-MANet, and ResNet-101-UNet++. We then conducted a high-performance computing scaling experiment to compare the number of service units and runtime required for model inferencing on a hypothetical pan- Arctic scale dataset. We found that the ResNet-50-UNet++ model could save up to ~ 54% on service unit expenditure, or ~ 18% on runtime, when considering operational deployment of our mapping approach. Our results suggest that ResNet-50-UNet++ could be the most suitable architecture (out of the nine that were examined) for deep learning-enabled Arctic infrastructure mapping efforts. Overall, our findings regarding the differences between the examined CNN architectures and our methodological framework for multi-objective architecture comparison can provide a foundation that may propel future pan-Arctic GeoAI mapping efforts of infrastructure. 

Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryo- spheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM’s applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 B´arðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m3. Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 103 m3 and a total area of 0.025 km2. ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m3 and 1,160,000 ± 85,000 m3, highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM’s potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of 0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.