Estimating millimeter-scale surface roughness of rock outcrops using drone-flyover structure-from-motion (SfM) photogrammetry by applying machine learning model

Abstract

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 could predict the millimeter-scale surface ruggedness of the given rock outcrop. In the prediction, one of the three studied outcrops was selected as training data, and various patterns of choices from the available georeferenced visual information (i.e., coordinates, H, S, V) and TRI data were used as explanatory and response variables, respectively. The results revealed that our model provided reasonable quantitative predictions of surface ruggedness. In addition, 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 quantitively predict the millimeter-scale surface ruggedness of outcrops, facilitating the acquisition of high-resolution surface morphology data without DEMs. This achievement can be a step forward in better acquiring surface geological information, the quality of which is often compromised by the person carrying out the survey.