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

Zhiang Chen, Tyler R. Scott, Sarah Bearman, Harish Anand, Devin Keating, Chelsea P. Scott, Ramon Arrowsmith, & Jnaneshwar Das

Accepted August 1, 2020, SCEC Contribution #10775

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 (size, roundness, and orientation) along a tectonic fault scarp. The properties of the rocks on the fault scarp derive from the combination of initial volcanic fracturing and subsequent tectonic and geomorphic fracturing, and our pipeline allows scientists to leverage UAS-based imagery to gain a better understanding of such surface processes. We start by using SfM on aerial imagery to produce georeferenced orthomosaics and digital elevation models (DEM). A human expert then annotates rocks on a set of image tiles sampled from the orthomosaics, and these annotations are used to train a deep neural network to detect and segment individual rocks in the entire site. The extracted semantic information (rock masks) on large volumes of unlabeled, high-resolution SfM products allows subsequent structural analysis and shape descriptors to estimate rock size, roundness, and orientation. We present results of two experiments conducted along a fault scarp in the Volcanic Tablelands near Bishop, California. We conducted the first, proof-of-concept experiment with a DJI Phantom 4 Pro equipped with an RGB camera and inspected if elevation information assisted instance segmentation from RGB channels. Rock-trait histograms along and across the fault scarp were obtained with the neural network inference. In the second experiment, we deployed a hexrotor and a multispectral camera to produce a DEM and five spectral orthomosaics in red, green, blue, red edge, and near infrared. We focused on examining the effectiveness of different combinations of input channels in instance segmentation.

Key Words
machine learning, precariously balanced rocks, fault scarps

Citation
Chen, Z., Scott, T. R., Bearman, S., Anand, H., Keating, D., Scott, C. P., Arrowsmith, R., & Das, J. (2020, 08). Geomorphological Analysis Using Unpiloted Aircraft Systems, Structure from Motion, and Deep Learning. Poster Presentation at IEEE/RSJ International Conference on Intelligent Robots and Systems. https://arxiv.org/abs/1909.12874

Related Projects & Working Groups
Earthquake geology