Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models

The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate. Recent algorithmic attempts to fill this gap have employed unsupervised tools to discover structure in data. Some of the most successful approaches have used probabilistic models to uncover latent thematic structure in discrete data. Despite the success of these models on textual data, they have not generalized as well to image data, in part because of the spatial and temporal structure that may exist in an image stream. We introduce a novel unsupervised machine learning framework that incorporates the ability of convolutional autoencoders to discover features from images that directly encode spatial information, within a Bayesian nonparametric topic model that discovers meaningful latent patterns within discrete data. By using this hybrid framework, we overcome the fundamental dependency of traditional topic models on rigidly hand-coded data representations, while simultaneously encoding spatial dependency in our topics without adding model complexity. We apply this model to the motivating application of high-level scene understanding and mission summarization for exploratory marine robots. Our experiments on a seafloor dataset collected by a marine robot show that the proposed hybrid framework outperforms current state-of-the-art approaches on the task of unsupervised seafloor terrain characterization.Comment: 8 page

Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchments. Multiple tests with both designed and real rainfall events were performed and the results show that the flood predictions by neural network uses only 0.5 % of time comparing with physically-based approaches, with promising accuracy and ability of generalizations. The proposed neural network can also potentially be applied to different but relevant problems including flood predictions for urban layout planning

Applications of Digital Terrain Modeling to Address Problems in Geomorphology and Engineering Geology

This dissertation uses digital terrain modeling and computational methods to yield insight into three topics: 1) evaluating the influence of glacial topography on fluvial sediment transport in the Teton Range, WY, 2) integrating regional airborne lidar, UAV lidar, and structure from motion photogrammetry to characterize decadal-scale movement of slow-moving landslides in northern Kentucky, and 3) applying machine learning methods to surficial geologic mapping. The role of topography as a boundary condition that controls the efficiency of fluvial erosion in the Teton Range, Wyoming, was investigated by using existing lidar data to delineate surficial geologic units, geometrically reconstruct the depth to bedrock, and estimate the sediment volume and sediment production rate in two catchments. This data was coupled with seismic reflection data in the bay into which these catchments drain. We found that while the sediment production rate of 0.17 ± 0.02 mm/yr is similar to the uplift rate of the Teton Range, only about 2.6% of the post-glacial sediment has been transported out of the catchments, and the denudation rate is just 0.004 ± 0.001 mm/yr. We conclude that once the topography has been altered by glaciers, which flatten the valley bottom and steepen the valley walls, rivers are incapable of evacuating the sediment effectively. Sediment will be trapped in the valleys until the next glacial advance, or until uplift steepens the system such that rivers can once again become efficient. Repeat digital terrain surveys can be used to quantify changes to the Earth’s surface. Challenges include determining the threshold of change that can be detected when combining topographic data acquired by different platforms and of varying quality. To quantify the threshold of detectible elevation change in a slow-moving colluvial landslide in northern Kentucky over 14 years using county-wide lidar, uncrewed aerial vehicles (UAV) structure from motion surveys (SfM) and a UAV lidar survey, we used the statistics of noise from elevation difference maps in areas outside of the landslide. We found that the threshold of detectable elevation change ranges from 0.05 to 0.20 m, depending on the survey combination, and that detectable change in the landslide was found between all surveys, including those separated by only 2 weeks. For most users, geologic maps may convey a level of certainty which obscures the decisions and interpretations made by the mapper. The combination of machine learning and digital terrain data provides a new method for producing geologic maps which can also convey and preserve the underlying uncertainty. We test the performance of machine learning methods to accurately map the surficial geology of two quadrangles in Kentucky using 31 variables derived from lidar data, including surface roughness, slope, topographic position, and residual topography. The performance of eight machine learning methods were compared, and the importance of each variable was measured. The classifier with the highest accuracy using just the most important variables was used to produce surficial geologic maps in 6 areas, with resulting accuracies ranging from 0.795 to 0.931. The uncertainty resulting from the machine learning process is conveyed using gradations of color, which can be modified depending on the needs of the map user