Item talk:Q267971
From geokb
{
"USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "Where\u2019s the rock: Using convolutional neural networks to improve land cover classification", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70217821", "url": "https://pubs.usgs.gov/publication/70217821" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70217821 }, { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.3390/rs11192211", "url": "https://doi.org/10.3390/rs11192211" } ], "journal": { "@type": "Periodical", "name": "Remote Sensing", "volumeNumber": "11", "issueNumber": "19" }, "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "Remote Sensing" } ], "datePublished": "2019", "dateModified": "2021-02-04", "abstract": "While machine learning techniques have been increasingly applied to land cover classification problems, these techniques have not focused on separating exposed bare rock from soil covered areas. Therefore, we built a convolutional neural network (CNN) to differentiate exposed bare rock (rock) from soil cover (other). We made a training dataset by mapping exposed rock at eight test sites across the Sierra Nevada Mountains (California, USA) using USDA\u2019s 0.6 m National Aerial Inventory Program (NAIP) orthoimagery. These areas were then used to train and test the CNN. The resulting machine learning approach classifies bare rock in NAIP orthoimagery with a 0.95\u00a0F1\u00a0score. Comparatively, the classical OBIA approach gives only a 0.84\u00a0F1\u00a0score. This is an improvement over existing land cover maps, which underestimate rock by almost 90%. The resulting CNN approach is likely scalable but dependent on high-quality imagery and high-performance algorithms using representative training sets informed by expert mapping. As image quality and quantity continue to increase globally, machine learning models that incorporate high-quality training data informed by geologic, topographic, or other topical maps may be applied to more effectively identify exposed rock in large image collections.", "description": "2211, 20 p.", "publisher": { "@type": "Organization", "name": "MDPI" }, "author": [ { "@type": "Person", "name": "Petlyak, Helen", "givenName": "Helen", "familyName": "Petlyak", "affiliation": [ { "@type": "Organization", "name": "Digamma.ai" } ] }, { "@type": "Person", "name": "Cerovski-Darriau, Corina", "givenName": "Corina", "familyName": "Cerovski-Darriau", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-0543-0902", "url": "https://orcid.org/0000-0002-0543-0902" }, "affiliation": [ { "@type": "Organization", "name": "Earthquake Science Center", "url": "https://www.usgs.gov/centers/earthquake-science-center" } ] }, { "@type": "Person", "name": "Zaliva, Vadim", 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