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Open AccessArticle

Where’s the Rock: Using Convolutional Neural Networks to Improve Land Cover Classification

1
Digamma.ai, 14500 Big Basin Way, Suite G, Saratoga, CA 95070, USA
2
U.S. Geological Survey, Menlo Park, CA 94025, USA
3
Carnegie Mellon University, NASA Research Park, Moffett Field, CA 94035, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2211; https://doi.org/10.3390/rs11192211
Received: 9 July 2019 / Revised: 12 September 2019 / Accepted: 15 September 2019 / Published: 21 September 2019
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’s 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 F 1 score. Comparatively, the classical OBIA approach gives only a 0.84 F 1 score. 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. View Full-Text
Keywords: remote sensing; environment; geology; land cover; land use; classification remote sensing; environment; geology; land cover; land use; classification
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MDPI and ACS Style

Petliak, H.; Cerovski-Darriau, C.; Zaliva, V.; Stock, J. Where’s the Rock: Using Convolutional Neural Networks to Improve Land Cover Classification. Remote Sens. 2019, 11, 2211.

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