{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T07:42:09Z","timestamp":1782632529598,"version":"3.54.5"},"reference-count":81,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["77342"],"award-info":[{"award-number":["77342"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Infrastructure expands rapidly in the Arctic due to industrial development. At the same time, climate change impacts are pronounced in the Arctic. Ground temperatures are, for example, increasing as well as coastal erosion. A consistent account of the current human footprint is needed in order to evaluate the impact on the environments as well as risk for infrastructure. Identification of roads and settlements with satellite data is challenging due to the size of single features and low density of clusters. Spatial resolution and spectral characteristics of satellite data are the main issues regarding their separation. The Copernicus Sentinel-1 and -2 missions recently provided good spatial coverage and at the same time comparably high pixel spacing starting with 10 m for modes available across the entire Arctic. The purpose of this study was to assess the capabilities of both, Sentinel-1 C-band Synthetic Aperture Radar (SAR) and the Sentinel-2 multispectral information for Arctic focused mapping. Settings differ across the Arctic (historic settlements versus industrial, locations on bedrock versus tundra landscapes) and reference data are scarce and inconsistent. The type of features and data scarcity demand specific classification approaches. The machine learning approaches Gradient Boosting Machines (GBM) and deep learning (DL)-based semantic segmentation have been tested. Records for the Alaskan North Slope, Western Greenland, and Svalbard in addition to high-resolution satellite data have been used for validation and calibration. Deep learning is superior to GBM with respect to users accuracy. GBM therefore requires comprehensive postprocessing. SAR provides added value in case of GBM. VV is of benefit for road identification and HH for detection of buildings. Unfortunately, the Sentinel-1 acquisition strategy is varying across the Arctic. The majority is covered in VV+VH only. DL is of benefit for road and building detection but misses large proportions of other human-impacted areas, such as gravel pads which are typical for gas and oil fields. A combination of results from both GBM (Sentinel-1 and -2 combined) and DL (Sentinel-2; Sentinel-1 optional) is therefore suggested for circumpolar mapping.<\/jats:p>","DOI":"10.3390\/rs12152368","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T11:26:01Z","timestamp":1595503561000},"page":"2368","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3737-7931","authenticated-orcid":false,"given":"Annett","family":"Bartsch","sequence":"first","affiliation":[{"name":"b.geos, 2100 Korneuburg, Austria"},{"name":"Austrian Polar Research Institute, c\/o Universit\u00e4t Wien, 1010 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2539-3827","authenticated-orcid":false,"given":"Georg","family":"Pointner","sequence":"additional","affiliation":[{"name":"b.geos, 2100 Korneuburg, Austria"},{"name":"Austrian Polar Research Institute, c\/o Universit\u00e4t Wien, 1010 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0776-4869","authenticated-orcid":false,"given":"Thomas","family":"Ingeman-Nielsen","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5153-7041","authenticated-orcid":false,"given":"Wenjun","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,23]]},"reference":[{"key":"ref_1","unstructured":"IPCC (2020, July 22). IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, Available online: https:\/\/www.ipcc.ch\/srocc\/chapter\/chapter-3-2\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.3390\/rs4041046","article-title":"Dynamics of a Coupled System: Multi-Resolution Remote Sensing in Assessing Social-Ecological Responses during 25 Years of Gas Field Development in Arctic Russia","volume":"4","author":"Kumpula","year":"2012","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1111\/gcb.12500","article-title":"Cumulative geoecological effects of 62 years of infrastructure and climate change in ice-rich permafrost landscapes, Prudhoe Bay Oilfield, Alaska","volume":"20","author":"Raynolds","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.1080\/014311697218061","article-title":"Monitoring changes in land cover induced by atmospheric pollution in the Kola Peninsula, Russia, using Landsat-MSS data","volume":"18","author":"Rees","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/S0034-4257(03)00014-2","article-title":"Monitoring vegetation changes in Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS\/TM data","volume":"85","author":"Tommervik","year":"2003","journal-title":"Remote. Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2979","DOI":"10.1080\/014311699211561","article-title":"Remote sensing of industrial impact on Arctic vegetation around Norilsk, northern Siberia: Preliminary results","volume":"20","author":"Toutoubalina","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","unstructured":"Crawford, R.M.M. (1997). Usinsk oil spill\u2014Environmental catastrophe or routine event?. Disturbance and Recovery in Arctic Lands: An Ecological Perspective, Kluwer."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/j.ecolind.2008.09.008","article-title":"Multiple indicators of human impacts on the environment in the Pechora Basin, north-eastern European Russia","volume":"9","author":"Walker","year":"2009","journal-title":"Ecol. Indic."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/S0269-7491(02)00186-0","article-title":"Satellite image analysis of human caused changes in the tundra vegetation around the city of Vorkuta, north-European Russia","volume":"120","author":"Virtanen","year":"2002","journal-title":"Environ. Pollut."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3402\/polar.v23i1.6266","article-title":"Satellite image based vegetation classification of a large area using limited ground reference data: A case study in the Usa Basin, north-east European Russia","volume":"23","author":"Virtanen","year":"2004","journal-title":"Pol. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1002\/hyp.13688","article-title":"Impact of heat advection on the thermal regime of roads built on permafrost","volume":"34","author":"Chen","year":"2020","journal-title":"Hydrol. Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1657\/1938-4246-44.3.368","article-title":"Permafrost, Infrastructure, and Climate Change: A GIS-Based Landscape Approach to Geotechnical Modeling","volume":"44","author":"Streletskiy","year":"2012","journal-title":"Arct. Antarct. Alp. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5147","DOI":"10.1038\/s41467-018-07557-4","article-title":"Degrading permafrost puts Arctic infrastructure at risk by mid-century","volume":"9","author":"Hjort","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1080\/1088937X.2019.1686082","article-title":"Assessment of the cost of climate change impacts on critical infrastructure in the circumpolar Arctic","volume":"42","author":"Suter","year":"2019","journal-title":"Pol. Geogr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1139\/as-2017-0041","article-title":"Impacts of past and future coastal changes on the Yukon coast\u2014Threats for cultural sites, infrastructure, and travel routes","volume":"5","author":"Irrgang","year":"2019","journal-title":"Arct. Sci."},{"key":"ref_16","unstructured":"Wang, P., Huang, C., Brown de Colstoun, E., Tilton, J., and Tan, B. (2017). Global Human Built-Up and Settlement Extent (HBASE) Dataset from Landsat, NASA Socioeconomic Data and Applications Center (SEDAC)."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Esch, T., Bachofer, F., Heldens, W., Hirner, A., Marconcini, M., Palacios-Lopez, D., Roth, A., \u00dcreyen, S., Zeidler, J., and Dech, S. (2018). Where We Live\u2014A Summary of the Achievements and Planned Evolution of the Global Urban Footprint. Remote Sens., 10.","DOI":"10.3390\/rs10060895"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Besussi, E., Chin, N., Batty, M., and Longley, P. (2010). The Structure and Form of Urban Settlements. Remote Sensing of Urban and Suburban Areas, Springer.","DOI":"10.1007\/978-1-4020-4385-7_2"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bartsch, A., H\u00f6fler, A., Kroisleitner, C., and Trofaier, A.M. (2016). Land Cover Mapping in Northern High Latitude Permafrost Regions with Satellite Data: Achievements and Remaining Challenges. Remote Sens., 8.","DOI":"10.3390\/rs8120979"},{"key":"ref_20","unstructured":"Brown de Colstoun, E., Huang, C., Wang, P., Tilton, J., Tan, B., Phillips, J., Niemczura, S., Ling, P.Y., and Wolfe, R. (2017). Global Man-Made Impervious Surface (GMIS) Dataset from Landsat, NASA Socioeconomic Data and Applications Center (SEDAC)."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"165","DOI":"10.14430\/arctic972","article-title":"Remote Sensing and Local Knowledge of Hydrocarbon Exploitation: The Case of Bovanenkovo, Yamal Peninsula, West Siberia, Russia","volume":"63","author":"Kumpula","year":"2010","journal-title":"Arctic"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Blasco, J.M.D., Fitrzyk, M., Patruno, J., Ruiz-Armenteros, A.M., and Marconcini, M. (2020). Effects on the Double Bounce Detection in Urban Areas Based on SAR Polarimetric Characteristics. Remote Sens., 12.","DOI":"10.3390\/rs12071187"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chini, M., Pelich, R., Hostache, R., Matgen, P., and Lopez-Martinez, C. (2018). Towards a 20 m Global Building Map from Sentinel-1 SAR Data. Remote Sens., 10.","DOI":"10.3390\/rs10111833"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2209","DOI":"10.1109\/JSTARS.2019.2920678","article-title":"Urban Extent Extraction Combining Sentinel Data in the Optical and Microwave Range","volume":"12","author":"Iannelli","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/LGRS.2013.2272953","article-title":"Urban Footprint Processor\u2014Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission","volume":"10","author":"Esch","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1080\/01431161.2017.1392642","article-title":"Enhanced automatic detection of human settlements using Sentinel-1 interferometric coherence","volume":"39","author":"Corbane","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.A., and Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations\u2014A Review. Remote Sens., 12.","DOI":"10.3390\/rs12071135"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Stromann, O., Nascetti, A., Yousif, O., and Ban, Y. (2019). Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12010076"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2016.03.009","article-title":"Unsupervised polarimetric SAR urban area classification based on model-based decomposition with cross scattering","volume":"116","author":"Xiang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/JSTARS.2008.921099","article-title":"Spatial Indexes for the Extraction of Formal and Informal Human Settlements From High-Resolution SAR Images","volume":"1","author":"Stasolla","year":"2008","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","unstructured":"Woodhouse, I. (2006). Introduction to Microwave Remote Sensing, Taylor & Francis."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Radoux, J., Chom\u00e9, G., Jacques, D., Waldner, F., Bellemans, N., Matton, N., Lamarche, C., d\u2019Andrimont, R., and Defourny, P. (2016). Sentinel-2\u2019s Potential for Sub-Pixel Landscape Feature Detection. Remote Sens., 8.","DOI":"10.3390\/rs8060488"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/01431161.2010.481681","article-title":"Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach","volume":"1","author":"He","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4269","DOI":"10.1080\/01431160802039957","article-title":"A new index for delineating built-up land features in satellite imagery","volume":"29","author":"Xu","year":"2008","journal-title":"Int J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pesaresi, M., Corbane, C., Julea, A., Florczyk, A., Syrris, V., and Soille, P. (2016). Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas. Remote Sens., 8.","DOI":"10.3390\/rs8040299"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"567","DOI":"10.14358\/PERS.83.8.567","article-title":"Unsupervised Deep Feature Learning for Urban Village Detection from High-Resolution Remote Sensing Images","volume":"83","author":"Li","year":"2017","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/LGRS.2018.2803259","article-title":"Very High Resolution Object-Based Land Use\u2013Land Cover Urban Classification Using Extreme Gradient Boosting","volume":"15","author":"Georganos","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yuan, J., Chowdhury, P.K.R., McKee, J., Yang, H.L., Weaver, J., and Bhaduri, B. (2018). Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria. Sci. Data, 5.","DOI":"10.1038\/sdata.2018.217"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Herfort, B., Li, H., Fendrich, S., Lautenbach, S., and Zipf, A. (2019). Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning. Remote Sens., 11.","DOI":"10.3390\/rs11151799"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/JSTARS.2019.2954850","article-title":"OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation","volume":"13","author":"Zhao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining\u2014KDD 16, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mboga, N., Persello, C., Bergado, J., and Stein, A. (2017). Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks. Remote Sens., 9.","DOI":"10.3390\/rs9111106"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2325","DOI":"10.1109\/LGRS.2017.2763738","article-title":"Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images","volume":"14","author":"Persello","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.isprsjprs.2019.02.006","article-title":"Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks","volume":"150","author":"Wurm","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science, Springer International Publishing Switzerland.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1080\/22797254.2019.1694447","article-title":"A new road extraction method using Sentinel-1 SAR images based on the deep fully convolutional neural network","volume":"52","author":"Zhang","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lefebvre, A., Sannier, C., and Corpetti, T. (2016). Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree. Remote Sens., 8.","DOI":"10.3390\/rs8070606"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhou, T., Li, Z., and Pan, J. (2018). Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification. Sensors, 18.","DOI":"10.3390\/s18020373"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Schubert, A., Miranda, N., Geudtner, D., and Small, D. (2017). Sentinel-1A\/B Combined Product Geolocation Accuracy. Remote Sens., 9.","DOI":"10.3390\/rs9060607"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111515","DOI":"10.1016\/j.rse.2019.111515","article-title":"Feasibility of tundra vegetation height retrieval from Sentinel-1 and Sentinel-2 data","volume":"237","author":"Bartsch","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_52","unstructured":"ESA (2020, July 22). Sentinel-1. ESA\u2019s Radar Observatory Mission for GMES Operational Services, Available online: http:\/\/esamultimedia.esa.int\/multimedia\/publications\/SP-1322_1\/."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Widhalm, B., Bartsch, A., and Goler, R. (2018). Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments. Remote Sens., 10.","DOI":"10.3390\/rs10040551"},{"key":"ref_54","unstructured":"Lu, W., Aalberg, A., H\u00f8yland, K., Lubbad, R., L\u00f8set, S., and Ingeman-Nielsen, T. (2020, July 22). Available online: https:\/\/doi.pangaea.de\/10.1594\/PANGAEA.895950."},{"key":"ref_55","unstructured":"ESA (2020, July 22). Sentinel-2 User Handbook, Available online: https:\/\/sentinels.copernicus.eu\/documents\/247904\/685211\/Sentinel-2_User_Handbook."},{"key":"ref_56","unstructured":"Obu, J., Westermann, S., K\u00e4\u00e4b, A., and Bartsch, A. (2020, July 22). Available online: https:\/\/doi.pangaea.de\/10.1594\/PANGAEA.888600."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.earscirev.2019.04.023","article-title":"Northern Hemisphere permafrost map based on TTOP modelling for 2000-2016 at 1?km2 scale","volume":"193","author":"Obu","year":"2019","journal-title":"Earth-Sci. Rev."},{"key":"ref_58","unstructured":"Walker, D.A., Raynolds, M.K., Buchhorn, M., and Peirce, J.L. (2014). Landscape and Permafrost Changes in the Prudhoe Bay Oilfield, Alaska, Alaska Geobotany Center. Alaska Geobotany Center Publication AGC 14-01."},{"key":"ref_59","unstructured":"lorczyk, A.J., Corbane, C., Ehrlich, D., Freire, S., Kemper, T., Maffenini, L., Melchiorri, M., Pesaresi, M., Politis, P., and Schiavina, M. (2019). GHSL Data Package 2019, Publications Office of the European Union. Technical Report."},{"key":"ref_60","unstructured":"Ingeman-Nielsen, T., and Vakulenko, I. (2020, July 22). Available online: https:\/\/doi.pangaea.de\/10.1594\/PANGAEA.895949."},{"key":"ref_61","unstructured":"Chollet, F. (2017). Deep Learning with Python, Manning."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.isprsjprs.2018.09.018","article-title":"Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network","volume":"146","author":"Lanaras","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/JSTARS.2017.2787650","article-title":"Methods to Remove the Border Noise From Sentinel-1 Synthetic Aperture Radar Data: Implications and Importance For Time-Series Analysis","volume":"11","author":"Ali","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1080\/07038992.2019.1711366","article-title":"Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform","volume":"46","author":"Mahdianpari","year":"2020","journal-title":"Can. J. Remote Sens."},{"key":"ref_65","first-page":"4","article-title":"The fragmented nature of tundra landscape","volume":"27","author":"Virtanen","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_66","unstructured":"Jasotani, N.R. (2020). Adopting TensorFlow for Real-World AI: A Practical Approach\u2014TensorFlow v2.2, Independently Published."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1007\/s11704-018-7195-8","article-title":"Learning deep representations for semantic image parsing: A comprehensive overview","volume":"12","author":"Huang","year":"2018","journal-title":"Front. Comput. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A Review on Deep Learning Techniques Applied to Semantic Segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Deng, L., Yang, M., Qian, Y., Wang, C., and Wang, B. (2017, January 11\u201314). CNN based semantic segmentation for urban traffic scenes using fisheye camera. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, CA, USA.","DOI":"10.1109\/IVS.2017.7995725"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Romera, E., Bergasa, L.M., Alvarez, J.M., and Trivedi, M. (2017, January 11\u201314). Train Here, Deploy There: Robust Segmentation in Unseen Domains. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, CA, USA.","DOI":"10.1109\/IVS.2018.8500561"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1080\/20964471.2017.1397899","article-title":"Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping","volume":"1","author":"Corbane","year":"2017","journal-title":"Big Earth Data"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1109\/JSTARS.2017.2782180","article-title":"SAR-Based Urban Extents Extraction: From ENVISAT to Sentinel-1","volume":"11","author":"Lisini","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Fernandez-Moral, E., Martins, R., Wolf, D., and Rives, P. (2017, January 11\u201314). A new metric for evaluating semantic segmentation: Leveraging global and contour accuracy. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, CA, USA.","DOI":"10.1109\/IVS.2018.8500497"},{"key":"ref_74","unstructured":"Opitz, J., and Burst, S. (2019). Macro F1 and Macro F1. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Strozzi, T., Antonova, S., G\u00fcnther, F., M\u00e4tzler, E., Vieira, G., Wegm\u00fcller, U., Westermann, S., and Bartsch, A. (2018). Sentinel-1 SAR Interferometry for Surface Deformation Monitoring in Low-Land Permafrost Areas. Remote Sens., 10.","DOI":"10.3390\/rs10091360"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Bartsch, A., Leibman, M., Strozzi, T., Khomutov, A., Widhalm, B., Babkina, E., Mullanurov, D., Ermokhina, K., Kroisleitner, C., and Bergstedt, H. (2019). Seasonal Progression of Ground Displacement Identified with Satellite Radar Interferometry and the Impact of Unusually Warm Conditions on Permafrost at the Yamal Peninsula in 2016. Remote Sens., 11.","DOI":"10.3390\/rs11161865"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Brunner, D., Bruzzone, L., Ferro, A., and Lemoine, G. (2009, January 4\u20138). Analysis of the reliability of the double bounce scattering mechanism for detecting buildings in VHR SAR images. Proceedings of the 2009 IEEE Radar Conference, Pasadena, CA, USA.","DOI":"10.1109\/RADAR.2009.4976983"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.geomorph.2016.09.013","article-title":"Detection of tundra trail damage near Barrow, Alaska using remote imagery","volume":"293","author":"Hinkel","year":"2017","journal-title":"Geomorphology"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"9563","DOI":"10.5194\/acp-16-9563-2016","article-title":"Trends in normalized difference vegetation index (NDVI) associated with urban development in northern West Siberia","volume":"16","author":"Esau","year":"2016","journal-title":"Atmos. Chem. Phys."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Miles, V., and Esau, I. (2017). Seasonal and Spatial Characteristics of Urban Heat Islands (UHIs) in Northern West Siberian Cities. Remote Sens., 9.","DOI":"10.3390\/rs9100989"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"5423","DOI":"10.1038\/s41467-018-07663-3","article-title":"Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic","volume":"9","author":"Nitze","year":"2018","journal-title":"Nat. Commun."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/15\/2368\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:51:12Z","timestamp":1760176272000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/15\/2368"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,23]]},"references-count":81,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["rs12152368"],"URL":"https:\/\/doi.org\/10.3390\/rs12152368","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,23]]}}}