Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Regions
2.1.1. Banks Island
2.1.2. Herschel Island
2.1.3. Horton Delta
2.1.4. Kolguev Island
2.1.5. Lena River
2.1.6. Tuktoyaktuk Peninsula
2.2. Data
2.2.1. PlanetScope
2.2.2. Arctic DEM
2.2.3. TCVIS
2.3. Methods
2.3.1. Slump Digitization
- little or no vegetation, surrounded by vegetation;
- presence of a headwall;
- “blue” signature of TCVIS layer, a transition from vegetation to wet soil;
- visible depression in ArcticDEM and derived slope dataset;
- visible thaw slump disturbance in VHR imagery;
- snow was considered as not being part of the RTS.
2.3.2. Deep Learning Model
General Setup
Hardware
Augmentation
Model Architecture
Training Details and Hyperparameters
Cross-Validation: Data Setup
Inference for Spatial Evaluation
3. Results
3.1. AI Model Performance
3.1.1. Train/Test/Cross-Validation Performance
3.1.2. Regional Comparison
3.1.3. Model Configurations
3.1.4. Computation Performance
3.2. Inference/Spatial Model Output
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
- Box, J.E.; Colgan, W.T.; Christensen, T.R.; Schmidt, N.M.; Lund, M.; Parmentier, F.-J.W.; Brown, R.; Bhatt, U.S.; Euskirchen, E.S.; Romanovsky, V.E.; et al. Key Indicators of Arctic Climate Change: 1971–2017. Environ. Res. Lett. 2019, 14, 045010. [Google Scholar] [CrossRef]
- Meredith, M.; Sommerkorn, M.; Cassotta, S.; Derksen, C.; Ekaykin, A.; Hollowed, A.; Kofinas, G.; Mackintosh, A.; Melbourne-Thomas, J.; Muelbert, M.M.C. Polar Regions. Chapter 3, IPCC Special Report on the Ocean and Cryosphere in a Changing Climate; WMO: Geneva, Switzerland, 2019. [Google Scholar]
- Biskaborn, B.K.; Smith, S.L.; Noetzli, J.; Matthes, H.; Vieira, G.; Streletskiy, D.A.; Schoeneich, P.; Romanovsky, V.E.; Lewkowicz, A.G.; Abramov, A.; et al. Permafrost Is Warming at a Global Scale. Nat. Commun. 2019, 10, 264. [Google Scholar] [CrossRef] [Green Version]
- Nitzbon, J.; Westermann, S.; Langer, M.; Martin, L.C.P.; Strauss, J.; Laboor, S.; Boike, J. Fast Response of Cold Ice-Rich Permafrost in Northeast Siberia to a Warming Climate. Nat. Commun. 2020, 11, 2201. [Google Scholar] [CrossRef] [PubMed]
- Vasiliev, A.A.; Drozdov, D.S.; Gravis, A.G.; Malkova, G.V.; Nyland, K.E.; Streletskiy, D.A. Permafrost Degradation in the Western Russian Arctic. Environ. Res. Lett. 2020, 15, 045001. [Google Scholar] [CrossRef]
- Hugelius, G.; Strauss, J.; Zubrzycki, S.; Harden, J.W.; Schuur, E.A.G.; Ping, C.-L.; Schirrmeister, L.; Grosse, G.; Michaelson, G.J.; Koven, C.D.; et al. Estimated Stocks of Circumpolar Permafrost Carbon with Quantified Uncertainty Ranges and Identified Data Gaps. Biogeosciences 2014, 11, 6573–6593. [Google Scholar] [CrossRef] [Green Version]
- Strauss, J.; Schirrmeister, L.; Grosse, G.; Wetterich, S.; Ulrich, M.; Herzschuh, U.; Hubberten, H. The Deep Permafrost Carbon Pool of the Yedoma Region in Siberia and Alaska. Geophys. Res. Lett. 2013, 40, 6165–6170. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schuur, E.A.G.; McGuire, A.D.; Schädel, C.; Grosse, G.; Harden, J.W.; Hayes, D.J.; Hugelius, G.; Koven, C.D.; Kuhry, P.; Lawrence, D.M.; et al. Climate Change and the Permafrost Carbon Feedback. Nature 2015, 520, 171–179. [Google Scholar] [CrossRef]
- Grosse, G.; Harden, J.; Turetsky, M.; McGuire, A.D.; Camill, P.; Tarnocai, C.; Frolking, S.; Schuur, E.A.G.; Jorgenson, T.; Marchenko, S.; et al. Vulnerability of High-Latitude Soil Organic Carbon in North America to Disturbance. J. Geophys. Res. Biogeosci. 2011, 116. [Google Scholar] [CrossRef] [Green Version]
- Burn, C.R.; Lewkowicz, A.G. CANADIAN LANDFORM EXAMPLES - 17 RETROGRESSIVE THAW SLUMPS. Can. Geogr. Géographe Can. 1990, 34, 273–276. [Google Scholar] [CrossRef]
- Lacelle, D.; Bjornson, J.; Lauriol, B. Climatic and Geomorphic Factors Affecting Contemporary (1950–2004) Activity of Retrogressive Thaw Slumps on the Aklavik Plateau, Richardson Mountains, NWT, Canada: Climatic and Geomorphic Factors Affecting Thaw Slump Activity. Permafr. Periglac. Process. 2010, 21, 1–15. [Google Scholar] [CrossRef]
- Lantuit, H.; Pollard, W.H. Fifty Years of Coastal Erosion and Retrogressive Thaw Slump Activity on Herschel Island, Southern Beaufort Sea, Yukon Territory, Canada. Geomorphology 2008, 95, 84–102. [Google Scholar] [CrossRef]
- Leibman, M.; Khomutov, A.; Kizyakov, A. Cryogenic Landslides in the West-Siberian Plain of Russia: Classification, Mechanisms, and Landforms. In Landslides in Cold Regions in the Context of Climate Change; Shan, W., Guo, Y., Wang, F., Marui, H., Strom, A., Eds.; Environmental Science and Engineering; Springer International Publishing: Cham, Germany, 2014; pp. 143–162. ISBN 978-3-319-00867-7. [Google Scholar]
- Ashastina, K.; Schirrmeister, L.; Fuchs, M.; Kienast, F. Palaeoclimate Characteristics in Interior Siberia of MIS 6–2: First Insights from the Batagay Permafrost Mega-Thaw Slump in the Yana Highlands. Clim. Past 2017, 13, 795–818. [Google Scholar] [CrossRef] [Green Version]
- Lantz, T.C.; Kokelj, S.V. Increasing Rates of Retrogressive Thaw Slump Activity in the Mackenzie Delta Region, N.W.T., Canada. Geophys. Res. Lett. 2008, 35, L06502. [Google Scholar] [CrossRef]
- Lewkowicz, A.G.; Way, R.G. Extremes of Summer Climate Trigger Thousands of Thermokarst Landslides in a High Arctic Environment. Nat. Commun. 2019, 10, 1329. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Segal, R.A.; Lantz, T.C.; Kokelj, S.V. Acceleration of Thaw Slump Activity in Glaciated Landscapes of the Western Canadian Arctic. Environ. Res. Lett. 2016, 11, 034025. [Google Scholar] [CrossRef]
- Ward Jones, M.K.; Pollard, W.H.; Jones, B.M. Rapid Initialization of Retrogressive Thaw Slumps in the Canadian High Arctic and Their Response to Climate and Terrain Factors. Environ. Res. Lett. 2019, 14, 055006. [Google Scholar] [CrossRef]
- Kokelj, S.V.; Lantz, T.C.; Kanigan, J.; Smith, S.L.; Coutts, R. Origin and Polycyclic Behaviour of Tundra Thaw Slumps, Mackenzie Delta Region, Northwest Territories, Canada. Permafr. Periglac. Process. 2009, 20, 173–184. [Google Scholar] [CrossRef]
- Balser, A.W.; Jones, J.B.; Gens, R. Timing of Retrogressive Thaw Slump Initiation in the Noatak Basin, Northwest Alaska, USA. J. Geophys. Res. Earth Surf. 2014, 119, 1106–1120. [Google Scholar] [CrossRef]
- Kokelj, S.V.; Lantz, T.C.; Tunnicliffe, J.; Segal, R.; Lacelle, D. Climate-Driven Thaw of Permafrost Preserved Glacial Landscapes, Northwestern Canada. Geology 2017, 45, 371–374. [Google Scholar] [CrossRef] [Green Version]
- Andreev, A.A.; Grosse, G.; Schirrmeister, L.; Kuznetsova, T.V.; Kuzmina, S.A.; Bobrov, A.A.; Tarasov, P.E.; Novenko, E.Y.; Meyer, H.; Derevyagin, A.Y.; et al. Weichselian and Holocene Palaeoenvironmental History of the Bol’shoy Lyakhovsky Island, New Siberian Archipelago, Arctic Siberia. Boreas 2009, 38, 72–110. [Google Scholar] [CrossRef] [Green Version]
- Lantuit, H.; Atkinson, D.; Paul Overduin, P.; Grigoriev, M.; Rachold, V.; Grosse, G.; Hubberten, H.-W. Coastal Erosion Dynamics on the Permafrost-Dominated Bykovsky Peninsula, North Siberia, 1951–2006. Polar Res. 2011, 30, 7341. [Google Scholar] [CrossRef]
- Kokelj, S.V.; Tunnicliffe, J.; Lacelle, D.; Lantz, T.C.; Chin, K.S.; Fraser, R. Increased Precipitation Drives Mega Slump Development and Destabilization of Ice-Rich Permafrost Terrain, Northwestern Canada. Glob. Planet. Chang. 2015, 129, 56–68. [Google Scholar] [CrossRef] [Green Version]
- Swanson, D.K. Permafrost Thaw-related Slope Failures in Alaska’s Arctic National Parks. 1980–2019. Permafr. Periglac. Process. 2021, 32, 392–406. [Google Scholar] [CrossRef]
- Babkina, E.A.; Leibman, M.O.; Dvornikov, Y.A.; Fakashchuk, N.Y.; Khairullin, R.R.; Khomutov, A.V. Activation of Cryogenic Processes in Central Yamal as a Result of Regional and Local Change in Climate and Thermal State of Permafrost. Russ. Meteorol. Hydrol. 2019, 44, 283–290. [Google Scholar] [CrossRef]
- Kizyakov, A.; Zimin, M.; Leibman, M.; Pravikova, N. Monitoring of the Rate of Thermal Denudation and Thermal Abrasion on the Western Coast of Kolguev Island, Using High Resolution Satellite Images. Kriosf. Zemli 2013, 17, 36–47. [Google Scholar]
- Vadakkedath, V.; Zawadzki, J.; Przeździecki, K. Multisensory Satellite Observations of the Expansion of the Batagaika Crater and Succession of Vegetation in Its Interior from 1991 to 2018. Environ. Earth Sci. 2020, 79, 150. [Google Scholar] [CrossRef] [Green Version]
- Obu, J.; Lantuit, H.; Grosse, G.; Günther, F.; Sachs, T.; Helm, V.; Fritz, M. Coastal Erosion and Mass Wasting along the Canadian Beaufort Sea Based on Annual Airborne LiDAR Elevation Data. Geomorphology 2017, 293, 331–346. [Google Scholar] [CrossRef] [Green Version]
- Swanson, D.; Nolan, M. Growth of Retrogressive Thaw Slumps in the Noatak Valley, Alaska, 2010–2016, Measured by Airborne Photogrammetry. Remote Sens. 2018, 10, 983. [Google Scholar] [CrossRef] [Green Version]
- van der Sluijs, J.; Kokelj, S.; Fraser, R.; Tunnicliffe, J.; Lacelle, D. Permafrost Terrain Dynamics and Infrastructure Impacts Revealed by UAV Photogrammetry and Thermal Imaging. Remote Sens. 2018, 10, 1734. [Google Scholar] [CrossRef] [Green Version]
- Nitze, I.; Grosse, G.; Jones, B.M.; Romanovsky, V.E.; Boike, J. Remote Sensing Quantifies Widespread Abundance of Permafrost Region Disturbances across the Arctic and Subarctic. Nat. Commun. 2018, 9, 5423. [Google Scholar] [CrossRef] [PubMed]
- Lara, M.J.; Chipman, M.L.; Hu, F.S. Automated Detection of Thermoerosion in Permafrost Ecosystems Using Temporally Dense Landsat Image Stacks. Remote Sens. Environ. 2019, 221, 462–473. [Google Scholar] [CrossRef]
- Runge, A.; Nitze, I.; Grosse, G. Remote Sensing Annual Dynamics of Rapid Permafrost Thaw Disturbances with LandTrendr. Remote Sens. Environ. accepted.
- Blaschke, T.; Lang, S.; Hay, G. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications; Springer Science & Business Media: Berlin/Heidelberg Germany, 2008. [Google Scholar]
- Zhu, X.X.; Tuia, D.; Mou, L.; Xia, G.-S.; Zhang, L.; Xu, F.; Fraundorfer, F. Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources. IEEE Geosci. Remote Sens. Mag. 2017, 5, 8–36. [Google Scholar] [CrossRef] [Green Version]
- Ghorbanzadeh, O.; Blaschke, T.; Gholamnia, K.; Meena, S.; Tiede, D.; Aryal, J. Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens. 2019, 11, 196. [Google Scholar] [CrossRef] [Green Version]
- Prakash, N.; Manconi, A.; Loew, S. A New Strategy to Map Landslides with a Generalized Convolutional Neural Network. Sci. Rep. 2021, 11, 9722. [Google Scholar] [CrossRef] [PubMed]
- Wang, H.; Zhang, L.; Yin, K.; Luo, H.; Li, J. Landslide Identification Using Machine Learning. Geosci. Front. 2021, 12, 351–364. [Google Scholar] [CrossRef]
- Wagner, F.; Dalagnol, R.; Tarabalka, Y.; Segantine, T.; Thomé, R.; Hirye, M. U-Net-Id, an Instance Segmentation Model for Building Extraction from Satellite Images—Case Study in the Joanópolis City, Brazil. Remote Sens. 2020, 12, 1544. [Google Scholar] [CrossRef]
- Yang, N.; Tang, H. GeoBoost: An Incremental Deep Learning Approach toward Global Mapping of Buildings from VHR Remote Sensing Images. Remote Sens. 2020, 12, 1794. [Google Scholar] [CrossRef]
- Zhao, W.; Du, S.; Emery, W.J. Object-Based Convolutional Neural Network for High-Resolution Imagery Classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10, 3386–3396. [Google Scholar] [CrossRef]
- Wagner, F.H.; Dalagnol, R.; Tagle Casapia, X.; Streher, A.S.; Phillips, O.L.; Gloor, E.; Aragão, L.E.O.C. Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images. Remote Sens. 2020, 12, 2225. [Google Scholar] [CrossRef]
- Abdalla, A.; Cen, H.; Abdel-Rahman, E.; Wan, L.; He, Y. Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm. Remote Sens. 2019, 11, 3001. [Google Scholar] [CrossRef] [Green Version]
- Bhuiyan, M.A.E.; Witharana, C.; Liljedahl, A.K.; Jones, B.M.; Daanen, R.; Epstein, H.E.; Kent, K.; Griffin, C.G.; Agnew, A. Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery. J. Imaging 2020, 6, 97. [Google Scholar] [CrossRef] [PubMed]
- Park, J.H.; Inamori, T.; Hamaguchi, R.; Otsuki, K.; Kim, J.E.; Yamaoka, K. RGB Image Prioritization Using Convolutional Neural Network on a Microprocessor for Nanosatellites. Remote Sens. 2020, 12, 3941. [Google Scholar] [CrossRef]
- Abolt, C.J.; Young, M.H.; Atchley, A.L.; Wilson, C.J. Brief Communication: Rapid Machine-Learning-Based Extraction and Measurement of Ice Wedge Polygons in High-Resolution Digital Elevation Models. Cryosphere 2019, 13, 237–245. [Google Scholar] [CrossRef] [Green Version]
- Bhuiyan, M.A.E.; Witharana, C.; Liljedahl, A.K. Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types. J. Imaging 2020, 6, 137. [Google Scholar] [CrossRef]
- Zhang, W.; Liljedahl, A.K.; Kanevskiy, M.; Epstein, H.E.; Jones, B.M.; Jorgenson, M.T.; Kent, K. Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images. Remote Sens. 2020, 12, 1085. [Google Scholar] [CrossRef] [Green Version]
- Bartsch, A.; Pointner, G.; Ingeman-Nielsen, T.; Lu, W. Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2. Remote Sens. 2020, 12, 2368. [Google Scholar] [CrossRef]
- Huang, L.; Luo, J.; Lin, Z.; Niu, F.; Liu, L. Using Deep Learning to Map Retrogressive Thaw Slumps in the Beiluhe Region (Tibetan Plateau) from CubeSat Images. Remote Sens. Environ. 2020, 237, 111534. [Google Scholar] [CrossRef]
- Huang, L.; Liu, L.; Luo, J.; Lin, Z.; Niu, F. Automatically Quantifying Evolution of Retrogressive Thaw Slumps in Beiluhe (Tibetan Plateau) from Multi-Temporal CubeSat Images. Int. J. Appl. Earth Obs. Geoinform. 2021, 102, 102399. [Google Scholar] [CrossRef]
- Lakeman, T.R.; England, J.H. Paleoglaciological Insights from the Age and Morphology of the Jesse Moraine Belt, Western Canadian Arctic. Quat. Sci. Rev. 2012, 47, 82–100. [Google Scholar] [CrossRef]
- Fraser, R.; Kokelj, S.; Lantz, T.; McFarlane-Winchester, M.; Olthof, I.; Lacelle, D. Climate Sensitivity of High Arctic Permafrost Terrain Demonstrated by Widespread Ice-Wedge Thermokarst on Banks Island. Remote Sens. 2018, 10, 954. [Google Scholar] [CrossRef] [Green Version]
- Walker, D.A.; Raynolds, M.K.; Daniëls, F.J.A.; Einarsson, E.; Elvebakk, A.; Gould, W.A.; Katenin, A.E.; Kholod, S.S.; Markon, C.J.; Melnikov, E.S.; et al. The Circumpolar Arctic Vegetation Map. J. Veg. Sci. 2005, 16, 267–282. [Google Scholar] [CrossRef]
- Obu, J.; Westermann, S.; Kääb, A.; Bartsch, A. Ground Temperature Map, 2000-2016, Northern Hemisphere Permafrost in Earth & Environmental Science; PANGAEA, 2018. [Google Scholar] [CrossRef]
- Ramage, J.L.; Irrgang, A.M.; Herzschuh, U.; Morgenstern, A.; Couture, N.; Lantuit, H. Terrain Controls on the Occurrence of Coastal Retrogressive Thaw Slumps along the Yukon Coast, Canada: Coastal RTSs Along the Yukon Coast. J. Geophys. Res. Earth Surf. 2017, 122, 1619–1634. [Google Scholar] [CrossRef]
- Fritz, M.; Wetterich, S.; Meyer, H.; Schirrmeister, L.; Lantuit, H.; Pollard, W.H. Origin and Characteristics of Massive Ground Ice on Herschel Island (Western Canadian Arctic) as Revealed by Stable Water Isotope and Hydrochemical Signatures: Origin and Characteristics of Massive Ground Ice on Herschel Island. Permafr. Periglac. Process. 2011, 22, 26–38. [Google Scholar] [CrossRef] [Green Version]
- Stauch, G.; Lehmkuhl, F. Quaternary Glaciations in the Verkhoyansk Mountains, Northeast Siberia. Quat. Res. 2010, 74, 145–155. [Google Scholar] [CrossRef]
- Burn, C.R.; Kokelj, S.V. The Environment and Permafrost of the Mackenzie Delta Area. Permafr. Periglac. Process. 2009, 20, 83–105. [Google Scholar] [CrossRef]
- Olthof, I.; Fraser, R.H.; Schmitt, C. Landsat-Based Mapping of Thermokarst Lake Dynamics on the Tuktoyaktuk Coastal Plain, Northwest Territories, Canada since 1985. Remote Sens. Environ. 2015, 168, 194–204. [Google Scholar] [CrossRef]
- Plug, L.J.; Walls, C.; Scott, B.M. Tundra Lake Changes from 1978 to 2001 on the Tuktoyaktuk Peninsula, Western Canadian Arctic. Geophys. Res. Lett. 2008, 35, L03502. [Google Scholar] [CrossRef]
- Planet Team. Planet Application Program Interface: In Space for Life on Earth. Planet. 2017. Available online: https://api.planet.com (accessed on 16 July 2021).
- Porter, C.; Morin, P.; Howat, I.; Noh, M.-J.; Bates, B.; Peterman, K.; Keesey, S.; Schlenk, M.; Gardiner, J.; Tomko, K.; et al. ArcticDEM [Data set]; Harvard Dataverse, 2018. [Google Scholar] [CrossRef]
- Li, J.; Roy, D. A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote Sens. 2017, 9, 902. [Google Scholar] [CrossRef] [Green Version]
- Roy, S.; Swetnam, T.L. Tyson-Swetnam/Porder: Porder: Simple CLI for Planet OrdersV2 API; Zenodo, 2020. [Google Scholar]
- Nitze, I.; Grosse, G.; Jones, B.; Arp, C.; Ulrich, M.; Fedorov, A.; Veremeeva, A. Landsat-Based Trend Analysis of Lake Dynamics across Northern Permafrost Regions. Remote Sens. 2017, 9, 640. [Google Scholar] [CrossRef] [Green Version]
- Nitze, I.; Grosse, G. Detection of Landscape Dynamics in the Arctic Lena Delta with Temporally Dense Landsat Time-Series Stacks. Remote Sens. Environ. 2016, 181, 27–41. [Google Scholar] [CrossRef]
- Huang, C.; Wylie, B.; Yang, L.; Homer, C.; Zylstra, G. Derivation of a Tasselled Cap Transformation Based on Landsat 7 At-Satellite Reflectance. Int. J. Remote Sens. 2002, 23, 1741–1748. [Google Scholar] [CrossRef]
- QGIS Development Team. QGIS Geographic Information System; QGIS Association, 2021. [Google Scholar]
- Nextgis/Quickmapservices. 2021. [Python]. NextGIS. (Original Work Published 2014). Available online: https://github.com/nextgis/quickmapservices (accessed on 7 July 2021).
- Segal, R.; Kokelj, S.; Lantz, T.; Durkee, K.; Gervais, S.; Mahon, E.; Snijders, M.; Buysse, J.; Schwarz, S. Broad-Scale Mapping of Terrain Impacted by Retrogressive Thaw Slumping in Northwestern Canada. NWT Open Rep. 2016, 8, 1–17. [Google Scholar]
- Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. ArXiv191201703 Cs Stat 2019. [Google Scholar]
- Buslaev, A.; Iglovikov, V.I.; Khvedchenya, E.; Parinov, A.; Druzhinin, M.; Kalinin, A.A. Albumentations: Fast and Flexible Image Augmentations. Information 2020, 11, 125. [Google Scholar] [CrossRef] [Green Version]
- He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778. [Google Scholar]
- Ronneberger, O. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention; Springer: Cham, Germany, 2015; pp. 234–241. [Google Scholar]
- Zhou, Z.; Rahman Siddiquee, M.M.; Tajbakhsh, N.; Liang, J. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support; Stoyanov, D., Taylor, Z., Carneiro, G., Syeda-Mahmood, T., Martel, A., Maier-Hein, L., Tavares, J.M.R.S., Bradley, A., Papa, J.P., Belagiannis, V., et al., Eds.; Lecture Notes in Computer Science; Springer International Publishing: Cham, Germany, 2018; Volume 11045, pp. 3–11. ISBN 978-3-030-00888-8. [Google Scholar]
- Chen, L.-C.; Papandreou, G.; Schroff, F.; Adam, H. Rethinking Atrous Convolution for Semantic Image Segmentation. ArXiv170605587 Cs 2017. [Google Scholar]
- Kingma, D.P.; Ba, J. Adam: A Method for Stochastic Optimization. ArXiv14126980 Cs 2017. [Google Scholar]
- Zhou, Z.; Siddiquee, M.M.R.; Tajbakhsh, N.; Liang, J. UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation. IEEE Trans. Med. Imaging 2020, 39, 1856–1867. [Google Scholar] [CrossRef] [Green Version]
- Zhou, W.; Newsam, S.; Li, C.; Shao, Z. PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval. ISPRS J. Photogramm. Remote Sens. 2018, 145, 197–209. [Google Scholar] [CrossRef] [Green Version]
- Helber, P.; Bischke, B.; Dengel, A.; Borth, D. EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 2217–2226. [Google Scholar] [CrossRef] [Green Version]
- Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; Fei-Fei, L. ImageNet: A Large-Scale Hierarchical Image Database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June 2009; pp. 248–255. [Google Scholar]
- Jiang, Z.; Von Ness, K.; Loisel, J.; Wang, Z. ArcticNet: A Deep Learning Solution to Classify the Arctic Area. In Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA, 16–17 June 2019; pp. 38–47. [Google Scholar]
- Xia, Z.; Huang, L.; Liu, L. An Inventory of Retrogressive Thaw Slumps Along the Vulnerable Qinghai-Tibet Engineering Corridor. 2021. Available online: https://doi.pangaea.de/10.1594/PANGAEA.933957 (accessed on 19 October 2021).
Study Site | Center Coordinates | Region | # of Images | # of Image Dates |
---|---|---|---|---|
Banks Island 01 | 119.50° W; 72.84° N; | NW Canada | 12 | 5 |
Banks Island 02 | 118.20° W; 73.04° N | NW Canada | 15 | 4 |
Herschel Island | 139.00° W; 69.60° N | NW Canada | 10 | 5 |
Horton Delta 01 | 126.75° W; 69.75° N; | NW Canada | 10 | 4 |
Horton Delta 02 | 126.60° W; 69.64° N | NW Canada | 13 | 6 |
Kolguev Island 01 | 48.35° E; 69.22° N | NW Siberia | 29 | 14 |
Kolguev Island 02 | 48.51° E; 69.35° N | NW Siberia | 20 | 8 |
Lena River | 124.40° E; 69.12° N | E Siberia | 47 | 22 |
Tuktoyaktuk Pen. | 133.80° W; 69.12° N | NW Canada | 19 | 9 |
Study Site | # of Total Individual RTS Objects | # of Individual RTS per Date 1, 2 | Median Object Size (m²) |
---|---|---|---|
Banks Island 01 | 397 | 65–103 | 22,032 |
Banks Island 02 | 151 | 24–53 | 22,203 |
Herschel Island | 148 | 15–40 | 5175 |
Horton Delta 01 | 180 | 36–52 | 5562 |
Horton Delta 02 | 354 | 35–67 | 7981 |
Kolguev Island 01 | 44 | 3–5 | 78,786 |
Kolguev Island 02 | 275 | 25–41 | 13,840 |
Lena River | 238 | 5–13 | 14,470 |
Tuktoyaktuk Pen. | 385 | 30–55 | 2229 |
Input Data | Raw/Derived | Native Resolution (m) | # Bands |
---|---|---|---|
PlanetScope Scene (SR) | Raw | 3 | 3 |
PlanetScope NDVI | Derived | 3 | 1 |
ArcticDEM relative elevation | Derived | 2 | 1 |
ArcticDEM slope | Derived | 2 | 1 |
TCVIS | Derived | 30 | 3 |
Output Data | Format | Resolution (m) |
---|---|---|
Polygon vector | ESRI Shapefile | - |
Binary raster | GTiff | 3 |
Probability raster | GTiff | 3 |
Study Site | Model Config. | IoU1 | IoU5 | IoU10 | Prec1 | Recall1 | F11 |
---|---|---|---|---|---|---|---|
Banks Island | U++Rn50 | 0.39 | 0.13 | 0.08 | 0.80 | 0.38 | 0.52 |
Herschel | DLv3Rn34 | 0.39 | 0.33 | 0.32 | 0.50 | 0.63 | 0.56 |
Horton | U++Rn101 | 0.55 | 0.54 | 0.51 | 0.78 | 0.77 | 0.71 |
Kolguev | U++Rn101 | 0.48 | 0.45 | 0.43 | 0.67 | 0.63 | 0.64 |
Lena | U++Rn34 | 0.58 | 0.51 | 0.50 | 0.83 | 0.65 | 0.73 |
Tuktoyaktuk | U++Rn50 | 0.15 | 0.09 | 0.08 | 0.42 | 0.18 | 0.25 |
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Nitze, I.; Heidler, K.; Barth, S.; Grosse, G. Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps. Remote Sens. 2021, 13, 4294. https://doi.org/10.3390/rs13214294
Nitze I, Heidler K, Barth S, Grosse G. Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps. Remote Sensing. 2021; 13(21):4294. https://doi.org/10.3390/rs13214294
Chicago/Turabian StyleNitze, Ingmar, Konrad Heidler, Sophia Barth, and Guido Grosse. 2021. "Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps" Remote Sensing 13, no. 21: 4294. https://doi.org/10.3390/rs13214294