{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T23:11:42Z","timestamp":1771629102132,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,23]],"date-time":"2020-09-23T00:00:00Z","timestamp":1600819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009874","name":"Universit\u00e9 de Sherbrooke","doi-asserted-by":"publisher","award":["XXXX"],"award-info":[{"award-number":["XXXX"]}],"id":[{"id":"10.13039\/100009874","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Gossans are surficial deposits that form in host bedrock by the alteration of sulphides by acidic and oxidizing fluids. These deposits are typically a few meters to kilometers in size and they constitute important vectors to buried ore deposits. Hundreds of gossans have been mapped by field geologists in sparsely vegetated areas of the Canadian Arctic. However, due to Canada\u2019s vast northern landmass, it is highly probable that many existing occurrences have been missed. In contrast, a variety of remote sensing data has been acquired in recent years, allowing for a broader survey of gossans from orbit. These include band ratioing or methods based on principal component analysis. Spectrally, the 809 gossans used in this study show no significant difference from randomly placed points on the Landsat 8 imageries. To overcome this major issue, we propose a deep learning method based on convolutional neural networks and relying on geo big data (Landsat-8, Arctic digital elevation model lithological maps) that can be used for the detection of gossans. Its application in different regions in the Canadian Arctic shows great promise, with precisions reaching 77%. This first order approach could provide a useful precursor tool to identify gossans prior to more detailed surveys using hyperspectral imaging.<\/jats:p>","DOI":"10.3390\/rs12193123","type":"journal-article","created":{"date-parts":[[2020,9,23]],"date-time":"2020-09-23T09:28:08Z","timestamp":1600853288000},"page":"3123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Deep Learning Approach to the Detection of Gossans in the Canadian Arctic"],"prefix":"10.3390","volume":"12","author":[{"given":"\u00c9tienne","family":"Clabaut","sequence":"first","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myriam","family":"Lemelin","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1867-7530","authenticated-orcid":false,"given":"Micka\u00ebl","family":"Germain","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marie-Claude","family":"Williamson","sequence":"additional","affiliation":[{"name":"Natural Resources Canada, Ottawa, ON K1A 0E4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1475-8168","authenticated-orcid":false,"given":"\u00c9lo\u00efse","family":"Brassard","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.oregeorev.2013.01.008","article-title":"Supergene features and evolution of gossans capping massive sulphide deposits in the Iberian Pyrite Belt","volume":"53","author":"Velasco","year":"2013","journal-title":"Ore Geol. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1016\/j.pss.2009.05.011","article-title":"High Lake gossan deposit: An Arctic analogue for ancient Martian surficial processes?","volume":"57","author":"West","year":"2009","journal-title":"Planet. Space Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Harris, J.R., Williamson, M.-C., Percival, J.B., Behnia, P., and Macleod, R. (2015). Detecting and Mapping Gossans Using Remotely-Sensed Data, Environmental and Economic Significance of Gossans.","DOI":"10.4095\/296574"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s00126-011-0361-8","article-title":"A case study of the internal structures of gossans and weathering processes in the Iberian Pyrite Belt using magnetic fabrics and paleomagnetic dating","volume":"46","author":"Essalhi","year":"2011","journal-title":"Min. Depos."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.mineng.2015.12.002","article-title":"Precious metals in gossanous waste rocks from the Iberian Pyrite Belt","volume":"87","author":"Hunt","year":"2016","journal-title":"Miner. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.epsl.2014.05.010","article-title":"Gossan Hill, Victoria Island, Northwest Territories: An analogue for mine waste reactions within permafrost and implication for the subsurface mineralogy of Mars","volume":"400","author":"Peterson","year":"2014","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Williamson, M.-C. (2015). Environmental and Economic Significance of Gossans.","DOI":"10.4095\/296571"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"S307","DOI":"10.1180\/minmag.2017.081.063","article-title":"Supergene gold enrichment in the Castromil-Serra da Quinta gold deposit, NW Portugal","volume":"82","author":"Cruz","year":"2018","journal-title":"Mineral. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6002","DOI":"10.1007\/s11356-015-4776-0","article-title":"Characterization of water reservoirs affected by acid mine drainage: Geochemical, mineralogical, and biological (diatoms) properties of the water","volume":"23","author":"Valente","year":"2016","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1007\/s11368-015-1068-8","article-title":"Chemical quality of leachates and enzymatic activities in Technosols with gossan and sulfide wastes from the S\u00e3o Domingos mine","volume":"16","author":"Santos","year":"2016","journal-title":"J. Soils Sediments"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1007\/s11368-016-1518-y","article-title":"Potential environmental impact of technosols composed of gossan and sulfide-rich wastes from S\u00e3o Domingos mine: Assay of simulated leaching","volume":"17","author":"Santos","year":"2017","journal-title":"J. Soils Sediments"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/j.chemosphere.2019.02.172","article-title":"Rehabilitation of mining areas through integrated biotechnological approach: Technosols derived from organic\/inorganic wastes and autochthonous plant development","volume":"224","author":"Santos","year":"2019","journal-title":"Chemosphere"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shuster, J., Reith, F., Izawa, M., Flemming, R., Banerjee, N., and Southam, G. (2017). Biogeochemical Cycling of Silver in Acidic, Weathering Environments. Minerals, 7.","DOI":"10.3390\/min7110218"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hedrich, S., and Schippers, A. (2020). Distribution of Acidophilic Microorganisms in Natural and Man-made Acidic Environments. Curr. Issues Mol. Biol., 25\u201348.","DOI":"10.21775\/cimb.040.025"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1089\/ast.2017.1745","article-title":"Metabolic Processes Preserved as Biosignatures in Iron-Oxidizing Microorganisms: Implications for Biosignature Detection on Mars","volume":"19","author":"Floyd","year":"2019","journal-title":"Astrobiology"},{"key":"ref_16","first-page":"3","article-title":"Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy","volume":"Volume 3","author":"Andrew","year":"1999","journal-title":"Manual of Remote Sensing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2016.02.004","article-title":"Enhanced detection of gossans using hyperspectral data: Example from the Cape Smith Belt of northern Quebec, Canada","volume":"114","author":"Laakso","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Beiranvand Pour, A., S Park, T.Y., Park, Y., Hong, J.K., M Muslim, A., L\u00e4ufer, A., Crispini, L., Pradhan, B., Zoheir, B., and Rahmani, O. (2019). Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer, and WorldView-3 Multispectral Satellite Imagery for Prospecting Copper-Gold Mineralization in the Northeastern Inglefield Mobile Belt (IMB), Northwest Greenland. Remote Sens., 11.","DOI":"10.3390\/rs11202430"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1130\/0091-7613(1977)5<713:MOHAIT>2.0.CO;2","article-title":"Mapping of hydrothermal alteration in the Cuprite mining district, Nevada, using aircraft scanner images for the spectral region 0.46 to 2.36 \u00b5m","volume":"5","author":"Abrams","year":"1977","journal-title":"Geology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"591","DOI":"10.2113\/gsecongeo.78.4.591","article-title":"Remote sensing for porphyry copper deposits in southern Arizona","volume":"78","author":"Abrams","year":"1983","journal-title":"Econ. Geol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s11053-017-9341-8","article-title":"Detection of Gossan Zones in Arid Regions Using Landsat 8 OLI Data: Implication for Mineral Exploration in the Eastern Arabian Shield, Saudi Arabia","volume":"27","author":"Gahlan","year":"2018","journal-title":"Nat. Resour. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"63","DOI":"10.2113\/gssajg.119.1.63","article-title":"Multi- and hyperspectral spaceborne remote sensing of the Aggeneys base metal sulphide mineral deposit sites in the Lower Orange River region, South Africa","volume":"119","author":"Mielke","year":"2016","journal-title":"S. Afr. J. Geol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6790","DOI":"10.3390\/rs6086790","article-title":"Spaceborne Mine Waste Mineralogy Monitoring in South Africa, Applications for Modern Push-Broom Missions: Hyperion\/OLI and EnMAP\/Sentinel-2","volume":"6","author":"Mielke","year":"2014","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_26","unstructured":"Lin, M., Chen, Q., and Yan, S. (2014, March 04). Network in Network. Available online: https:\/\/arxiv.org\/pdf\/1312.4400.pdf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dai, J., He, K., and Sun, J. (2016, January 27\u201330). Instance-Aware Semantic Segmentation via Multi-task Network Cascades. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.343"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","article-title":"When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1080\/2150704X.2019.1693071","article-title":"DeepSat V2: Feature augmented convolutional neural nets for satellite image classification","volume":"11","author":"Liu","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1590\/2317-4889201620160023","article-title":"de Mapping iron oxides with Landsat-8\/OLI and EO-1\/Hyperion imagery from the Serra Norte iron deposits in the Caraj\u00e1s Mineral Province, Brazil","volume":"46","author":"Ducart","year":"2016","journal-title":"Braz. J. Geol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2015.10.012","article-title":"Geospatial big data handling theory and methods: A review and research challenges","volume":"115","author":"Li","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","unstructured":"(2019, April 22). GEM: Geo-mapping for Energy and Minerals. Available online: https:\/\/www.nrcan.gc.ca\/earth-sciences\/resources\/federal-programs\/geomapping-energy-minerals\/18215."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Harrison, J., St-Onge, M., Petrov, O., Strelnikov, S., Lopatin, B., Wilson, F., Tella, S., Paul, D., Lynds, T., and Shokalsky, S. (2011). Geological Map of the Arctic.","DOI":"10.4095\/287868"},{"key":"ref_36","unstructured":"Zanter, K. (2019). Landsat 8 Surface Reflectance Code (LASRC) Product Guide."},{"key":"ref_37","unstructured":"(2020, January 29). Canadian Digital Elevation Model, 1945\u20132011. Available online: https:\/\/open.canada.ca\/data\/en\/dataset\/7f245e4d-76c2-4caa-951a-45d1d2051333."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lechevallier, Y., and Saporta, G. (2010, January 22\u201327). Large-Scale Machine Learning with Stochastic Gradient Descent. Proceedings of the COMPSTAT\u20192010, Paris, France.","DOI":"10.1007\/978-3-7908-2604-3"},{"key":"ref_39","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_40","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","unstructured":"Adivarekar, B. (2020, January 15). Simple Keras CNN with 95.3% Accuracy. Available online: https:\/\/www.kaggle.com\/bhumitadivarekar\/simple-keras-cnn-with-95-13-accuracy."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A transformation for ordering multispectral data in terms of image quality with implications for noise removal","volume":"26","author":"Green","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1007\/978-3-642-15567-3_16","article-title":"Learning to Detect Roads in High-Resolution Aerial Images","volume":"Volume 6316","author":"Daniilidis","year":"2010","journal-title":"Computer Vision \u2013 ECCV 2010"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., and Nemani, R. (2015, January 3\u20136). DeepSat: A learning framework for satellite imagery. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS \u201915, Seattle, WA, USA.","DOI":"10.1145\/2820783.2820816"},{"key":"ref_45","unstructured":"Perez, L., and Wang, J. (2017, December 13). The Effectiveness of Data Augmentation in Image Classification using Deep Learning. Available online: https:\/\/arxiv.org\/pdf\/1712.04621.pdf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3123\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:12:53Z","timestamp":1760177573000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3123"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,23]]},"references-count":45,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12193123"],"URL":"https:\/\/doi.org\/10.3390\/rs12193123","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,23]]}}}