Geomorphological Analysis Using Unpiloted Aircraft Systems, Structure from Motion, and Deep Learning

by   Zhiang Chen, et al.

We present a pipeline for geomorphological analysis that uses structure from motion (SfM) and deep learning on close-range aerial imagery to estimate spatial distributions of rock traits (diameter and orientation), along a tectonic fault scarp. Unpiloted aircraft systems (UAS) have enabled acquisition of high-resolution imagery at close range, revolutionizing domains such as infrastructure inspection, precision agriculture, and disaster response. Our pipeline leverages UAS-based imagery to help scientists gain a better understanding of surface processes. Our pipeline uses SfM on aerial imagery to produce a georeferenced orthomosaic with 2 cm/pixel resolution. A human expert annotates rocks on a set of image tiles sampled from the orthomosaic, and these annotations are used to train a deep neural network to detect and segment individual rocks in the whole site. Our pipeline, in effect, automatically extracts semantic information (rock boundaries) on large volumes of unlabeled high-resolution aerial imagery, and subsequent structural analysis and shape descriptors result in estimates of rock diameter and orientation. We present results of our analysis on imagery collected along a fault scarp in the Volcanic Tablelands in eastern California. Although presented in the context of geology, our pipeline can be extended to a variety of morphological analysis tasks in other domains.


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