Real Time Lidar Terrain Mapping and Analysis

Mapping terrain is difficult. Capturing river morphology or the shape of faults, cliffs or landslides can require a lot of time and manpower. An Unmanned Aerial Vehicle (UAV) equipped with a lidar sensor can examine entire landscapes at a time, capturing a detailed 3D view of nearly any geologic structure with very little error. Existing approaches allow for a UAV to follow a flight plan and capture data for later analysis. During analysis, though, the user will often find that the data gathered was unsatisfactory, necessitating another flight. This is a tedious workflow. This project aims to develop software for the real-time reception and analysis of data gathered by a lidar-equipped UAV. The user will view the incoming data and its analyses as the UAV is in flight and be able to correct the flight path accordingly. The user of the software will see an interactive model of the landscape with selected analyses shown in colors. The capability to easily map the environment will facilitate the improvement of many ecosystem services

Lightweight Knowledge Representations for Automating Data Analysis

The principal goal of data science is to derive meaningful information from data. To do this, data scientists develop a space of analytic possibilities and from it reach their information goals by using their knowledge of the domain, the available data, the operations that can be performed on those data, the algorithms/models that are fed the data, and how all of these facets interweave. In this work, we take the first steps towards automating a key aspect of the data science pipeline: data analysis. We present an extensible taxonomy of data analytic operations that scopes across domains and data, as well as a method for codifying domain-specific knowledge that links this analytics taxonomy to actual data. We validate the functionality of our analytics taxonomy by implementing a system that leverages it, alongside domain labelings for 8 distinct domains, to automatically generate a space of answerable questions and associated analytic plans. In this way, we produce information spaces over data that enable complex analyses and search over this data and pave the way for fully automated data analysis