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1. Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery

   https://doi.org/10.3390/rs14174132  

   Witharana, Chandi; Udawalpola, Mahendra R.; Liljedahl, Anna K.; Jones, Melissa K.; Jones, Benjamin M.; Hasan, Amit; Joshi, Durga; Manos, Elias (September 2022, Remote Sensing)

   Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to develop a deep learning convolutional neural net (CNN) model (a UNet-based workflow) to automatically detect and characterize RTSs from VHSR imagery. We aimed to understand: (1) the optimal combination of input image tile size (array size) and the CNN network input size (resizing factor/spatial resolution) and (2) the interoperability of the trained UNet models across heterogeneous study sites based on a limited set of training samples. Hand annotation of RTS samples, CNN model training and testing, and interoperability analyses were based on two study areas from high-Arctic Canada: (1) Banks Island and (2) Axel Heiberg Island and Ellesmere Island. Our experimental results revealed the potential impact of image tile size and the resizing factor on the detection accuracies of the UNet model. The results from the model transferability analysis elucidate the effects on the UNet model due the variability (e.g., shape, color, and texture) associated with the RTS training samples. Overall, study findings highlight several key factors that we should consider when operationalizing CNN-based RTS mapping over large geographical extents. 

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2. Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps

   https://doi.org/10.3390/rs13214294  

   Nitze, Ingmar; Heidler, Konrad; Barth, Sophia; Grosse, Guido (November 2021, Remote Sensing)

   null (Ed.)

   In a warming Arctic, permafrost-related disturbances, such as retrogressive thaw slumps (RTS), are becoming more abundant and dynamic, with serious implications for permafrost stability and bio-geochemical cycles on local to regional scales. Despite recent advances in the field of earth observation, many of these have remained undetected as RTS are highly dynamic, small, and scattered across the remote permafrost region. Here, we assessed the potential strengths and limitations of using deep learning for the automatic segmentation of RTS using PlanetScope satellite imagery, ArcticDEM and auxiliary datasets. We analyzed the transferability and potential for pan-Arctic upscaling and regional cross-validation, with independent training and validation regions, in six different thaw slump-affected regions in Canada and Russia. We further tested state-of-the-art model architectures (UNet, UNet++, DeepLabv3) and encoder networks to find optimal model configurations for potential upscaling to continental scales. The best deep learning models achieved mixed results from good to very good agreement in four of the six regions (maxIoU: 0.39 to 0.58; Lena River, Horton Delta, Herschel Island, Kolguev Island), while they failed in two regions (Banks Island, Tuktoyaktuk). Of the tested architectures, UNet++ performed the best. The large variance in regional performance highlights the requirement for a sufficient quantity, quality and spatial variability in the training data used for segmenting RTS across diverse permafrost landscapes, in varying environmental conditions. With our highly automated and configurable workflow, we see great potential for the transfer to active RTS clusters (e.g., Peel Plateau) and upscaling to much larger regions. 

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3. DARTS: Multi-year database of AI-detected retrogressive thaw slumps in the circum-arctic permafrost region

   https://doi.org/10.1038/s41597-025-05810-2  

   Nitze, Ingmar; Heidler, Konrad; Nesterova, Nina; Küpper, Jonas; Schütt, Emma; Hölzer, Tobias; Barth, Sophia; Lara, Mark J; Liljedahl, Anna K; Grosse, Guido (December 2025, Scientific Data)

   Abstract Retrogressive Thaw Slumps (RTS) are widespread mass-wasting hillslope failures triggered by thawing permafrost. While regional studies have provided insights into the spatial distribution and dynamics of RTS, a consistent and unbiased quantification and monitoring remains unsolved at pan-arctic scales. We present the Database of AI-detected Arctic RTS footprints (DARTS), comprising ~43,000 individual footprints of active RTS or active areas within larger RTS landforms. DARTS spans ~1.6 million km²from 2018–2023, with at least annual coverage from 2021–2023 across a ~900,000 km²region. The database is freely available in two processing levels: sub-annual and annually aggregated polygon footprints including spatial and tabular metadata. DARTS uses a highly automated workflow based on deep learning segmentation of PlanetScope multi-spectral satellite imagery (3–5 m resolution) and elevation data. Validation against different regional RTS datasets yielded F1 scores ranging from 0.263 to 0.700, with higher accuracy in areas of intense RTS activity. DARTS provides a valuable resource for systematically mapping, quantifying, and analyzing active hillslope thermokarst distribution and changes over time across the circum-arctic permafrost region. 

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4. A multi-objective comparison of CNN architectures in Arctic human-built infrastructure mapping from sub-meter resolution satellite imagery

   https://doi.org/10.1080/01431161.2023.2287563  

   Manos, Elias; Witharana, Chandi; Perera, Amal S; Liljedahl, Anna K (December 2023, International Journal of Remote Sensing)

   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. 

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5. Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery

   https://doi.org/10.3390/rs14112719  

   Manos, Elias; Witharana, Chandi; Udawalpola, Mahendra Rajitha; Hasan, Amit; Liljedahl, Anna K. (June 2022, Remote Sensing)

   Rapid global warming is catalyzing widespread permafrost degradation in the Arctic, leading to destructive land-surface subsidence that destabilizes and deforms the ground. Consequently, human-built infrastructure constructed upon permafrost is currently at major risk of structural failure. Risk assessment frameworks that attempt to study this issue assume that precise information on the location and extent of infrastructure is known. However, complete, high-quality, uniform geospatial datasets of built infrastructure that are readily available for such scientific studies are lacking. While imagery-enabled mapping can fill this knowledge gap, the small size of individual structures and vast geographical extent of the Arctic necessitate large volumes of very high spatial resolution remote sensing imagery. Transforming this ‘big’ imagery data into ‘science-ready’ information demands highly automated image analysis pipelines driven by advanced computer vision algorithms. Despite this, previous fine resolution studies have been limited to manual digitization of features on locally confined scales. Therefore, this exploratory study serves as the first investigation into fully automated analysis of sub-meter spatial resolution satellite imagery for automated detection of Arctic built infrastructure. We tasked the U-Net, a deep learning-based semantic segmentation model, with classifying different infrastructure types (residential, commercial, public, and industrial buildings, as well as roads) from commercial satellite imagery of Utqiagvik and Prudhoe Bay, Alaska. We also conducted a systematic experiment to understand how image augmentation can impact model performance when labeled training data is limited. When optimal augmentation methods were applied, the U-Net achieved an average F1 score of 0.83. Overall, our experimental findings show that the U-Net-based workflow is a promising method for automated Arctic built infrastructure detection that, combined with existing optimized workflows, such as MAPLE, could be expanded to map a multitude of infrastructure types spanning the pan-Arctic. 

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