Abstract
A quantitative outcrop survey was conducted in three coastal areas in Japan to estimate the relationship between the surface morphology and visual information of well-exposed rocks using photogrammetry of drone flyovers. We generated three-dimensional digital outcrop models in the study areas to produce the hue, saturation, value (HSV) color space images and digital elevation model (DEM) data, together with terrain ruggedness index (TRI) computed from the DEM data. Using the data, we examined whether our machine learning model (MLM) could predict the millimeter-scale surface ruggedness of the given rock outcrop. In the prediction, one of the three studied outcrops was selected for the training data, and various patterns of choices from available georeferenced visual information (i.e., coordinates, H, S, V) and TRI data in the other study areas were used as explanatory variables and response variables, respectively. The MLM with H, S, and V as explanatory variables using the 3σ method for outlier removal showed the smallest Root Mean Square Error of 0.51 × 10−3. The results revealed that our MLM provided reasonable quantitative predictions of surface ruggedness. Additionally, our predictions worked well even in the presence of cast shadows on the studied outcrops, suggesting that the shadow effects were likely negligible. Our findings emphasize that the HSV color space data acquired by drone-flyover photogrammetry alone can quantitatively predict the millimeter-scale surface ruggedness of outcrops, facilitating the acquisition of high-resolution surface morphology data without DEMs.
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Data availability
The datasets generated and analysed during the current study are available from the corresponding author(s) on reasonable request.
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Acknowledgements
We thank Y. Okuma for fruitful discussions on geological interpretations. The manuscript benefited from constructive reviews by two anonymous reviewers. The computation was partly performed on the ITO supercomputer system (General Projects to A.K.) at the Research Institute for Information Technology of Kyushu University. This work was supported by Fukada Field Survey Grants of Fukada Geological Institute (to T.N.), Education and Research Program for Mathematical and Data Science from Kyushu University (to A.K.), Maeda Engineering Foundation Grant (to A.K.), and JSPS Bilateral Joint Research Project JPJSBP120214811 (to A.K.).
Funding
This work was supported by Fukada Field Survey Grants of Fukada Geological Institute (to T.N.), Education and Research Program for Mathematical and Data Science from Kyushu University (to A.K.), Maeda Engineering Foundation Grant (to A.K.), and JSPS Bilateral Joint Research Project JPJSBP120214811 (to A.K.).
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Takumu Nakamura: Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Software; Validation; Visualization; Writing - original draft; Writing - review & editing. Arata Kioka: Conceptualization; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Writing - original draft; Writing - review & editing. Kosuke Egawa: Supervision; Writing - review & editing. Takuma Ishii: Investigation; Writing - review & editing. Yasuhiro Yamada: Conceptualization; Supervision; Writing - review & editing.
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Nakamura, T., Kioka, A., Egawa, K. et al. Estimating millimeter-scale surface roughness of rock outcrops using drone-flyover structure-from-motion (SfM) photogrammetry by applying machine learning model. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01280-z
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DOI: https://doi.org/10.1007/s12145-024-01280-z