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

@article{Chen2020GeomorphologicalAU,
  title={Geomorphological Analysis Using Unpiloted Aircraft Systems, Structure from Motion, and Deep Learning},
  author={Zhiang Chen and Tyler Scott and Sarah Bearman and H. Anand and C. Scott and J. Arrowsmith and J. Das},
  journal={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020},
  pages={1276-1283}
}
  • Zhiang Chen, Tyler Scott, +4 authors J. Das
  • Published 2020
  • Computer Science, Physics
  • 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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… Expand
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