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

  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)},
  • 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
OpenUAV Cloud Testbed: a Collaborative Design Studio for Field Robotics
The OpenUAV testbed is presented, an open-source, easy-to-use, web-based, and reproducible software system that enables students and researchers to run robotic simulations on the cloud and provides a mechanism to support photorealistic robotics simulations by combining Unity game engine-based camera rendering and Gazebo physics. Expand
Autonomous Robotic Mapping of Fragile Geologic Features
This paper presents a target-oriented mapping system for sparsely distributed geologic surface features, such as precariously balanced rocks (PBRs), whose geometric fragility parameters can provide valuable information on earthquake shaking history and landscape development for a region. Expand
Localization and Mapping of Sparse Geologic Features with Unpiloted Aircraft Systems
This paper presents a system for localization and mapping of sparsely distributed surface features such as precariously balanced rocks (PBRs), whose geometric fragility (stability) parameters provide valuable information on earthquake processes. Expand
The OpenUAV Swarm Simulation Testbed: a Collaborative DesignStudio for Field Robotics
The authors' OpenUAV multi-robot design studio that enables simulations to run as browser accessible Lubuntu desktop containers, and built upon the previous work that leveraged concurrent multi-UAS simulations, to be useful for underwater, aerial and ground vehicles. Expand


Rapid mapping of ultrafine fault zone topography with structure from motion
Structure from Motion (SfM) generates high-resolution topography and coregistered texture (color) from an unstructured set of overlapping photographs taken from varying viewpoints, overcoming many ofExpand
Structure from motion photogrammetry in physical geography
The typical workflow applied by SfM-MVS software packages is detailed, practical details of implementing S fM- MVS are reviewed, existing validation studies to assess practically achievable data quality are combined, and the range of applications in physical geography are reviewed. Expand
Validation of meter-scale surface faulting offset measurements from high-resolution topographic data
Studies of active fault zones have flourished with the availability of high-resolution topographic data, particularly where airborne light detection and ranging (lidar) and structure from motionExpand
‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications
Abstract High-resolution topographic surveying is traditionally associated with high capital and logistical costs, so that data acquisition is often passed on to specialist third party organisations.Expand
Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments
The UAV-based approach to Structure from Motion approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology. Expand
Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery
A new saliency detection algorithm for fast location and segmentation of core fire area in aerial images using a 15-layered DCNN architecture named ‘Fire_Net’, which outperformed previous methods by achieving an overall accuracy of 98%. Expand
Observations on normal-fault scarp morphology and fault system evolution of the Bishop Tuff in the Volcanic Tableland, Owens Valley, California, U.S.A.
Mapping of normal faults cutting the Bishop Tuff in the Volcanic Tableland, northern Owens Valley, California, using side-looking airborne radar data, low-altitude aerial photographs, airborne lightExpand
An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection
Abstract The measurement of topography and of topographic change is essential for the study of many geomorphic processes. In recent years, structure from motion (SfM) techniques applied toExpand
Deep Learning Approach for Car Detection in UAV Imagery
An automatic solution to the problem of detecting and counting cars in unmanned aerial vehicle (UAV) images that outperforms the state-of-the-art methods, both in terms of accuracy and computational time. Expand
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Expand