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Remote Sens. 2018, 10(2), 307; https://doi.org/10.3390/rs10020307

Assessment of Convolution Neural Networks for Surficial Geology Mapping in the South Rae Geological Region, Northwest Territories, Canada

1
Natural Resources Canada, Canadian Centre for Remote Sensing, 560 Rochester, Ottawa, ON K1A 0E4, Canada
2
Environment and Climate Change Canada, Landscape Science and Technology, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
3
Natural Resources Canada, Geological Survey of Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
*
Author to whom correspondence should be addressed.
Received: 24 January 2018 / Revised: 9 February 2018 / Accepted: 13 February 2018 / Published: 16 February 2018
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Abstract

Mapping of surficial geology is an important requirement for broadening the geoscience database of northern Canada. Surficial geology maps are an integral data source for mineral and energy exploration. Moreover, they provide information such as the location of gravels and sands, which are important for infrastructure development. Currently, surficial geology maps are produced through expert interpretation of aerial photography and field data. However, interpretation is known to be subjective, labour-intensive and difficult to repeat. The expert knowledge required for interpretation can be challenging to maintain and transfer. In this research, we seek to assess the potential of deep neural networks to aid surficial geology mapping by providing an objective surficial materials initial layer that experts can modify to speed map development and improve consistency between mapped areas. Such an approach may also harness expert knowledge in a way that is transferable to unmapped areas. For this purpose, we assess the ability of convolution neural networks (CNN) to predict surficial geology classes under two sampling scenarios. In the first scenario, a CNN uses samples collected over the area to be mapped. In the second, a CNN trained over one area is then applied to locations where the available samples were not used in training the network. The latter case is important, as a collection of in situ training data can be costly. The evaluation of the CNN was carried out using aerial photos, Landsat reflectance, and high-resolution digital elevation data over five areas within the South Rae geological region of Northwest Territories, Canada. The results are encouraging, with the CNN generating average accuracy of 76% when locally trained. For independent test areas (i.e., trained over one area and applied over other), accuracy dropped to 59–70% depending on the classes selected for mapping. In the South Rae region, significant confusion was found between till veneer and till blanket as well as glaciofluvial subclasses (esker, terraced, and hummocky ice-contact). Merging these classes respectively increased accuracy for independent test area to 68% on average. Relative to the more widely used Random Forest machine learning algorithm, this represents an improvement in accuracy of 4%. Furthermore, the CNN produced better results for less frequent classes with distinct spatial structure. View Full-Text
Keywords: surficial materials mapping; surficial geology; deep learning; remote sensing surficial materials mapping; surficial geology; deep learning; remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Latifovic, R.; Pouliot, D.; Campbell, J. Assessment of Convolution Neural Networks for Surficial Geology Mapping in the South Rae Geological Region, Northwest Territories, Canada. Remote Sens. 2018, 10, 307.

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