Where’s the rock: Using convolutional neural networks to improve land cover classification

Remote Sensing
By: , and 



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 F1 score. Comparatively, the classical OBIA approach gives only a 0.84 F1 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.

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Publication type Article
Publication Subtype Journal Article
Title Where’s the rock: Using convolutional neural networks to improve land cover classification
Series title Remote Sensing
DOI 10.3390/rs11192211
Volume 11
Issue 19
Year Published 2019
Language English
Publisher MDPI
Contributing office(s) Geology, Minerals, Energy, and Geophysics Science Center
Description 2211, 20 p.
Country United States
State California
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