Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Spatio-Spectral Exploration Combining In Situ and Remote Measurements
Authors: David Thompson, David Wettergreen, Greydon Foil, Michael Furlong, Anatha Kiran
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We simulate exploration using alternative planning approaches, and show proof of concept results with the canonical spectroscopic map of a mining district in Cuprite, Nevada. |
| Researcher Affiliation | Collaboration | David R. Thompson Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. Pasadena, CA 91109 EMAIL David Wettergreen and Greydon Foil The Robotics Institute Carnegie Mellon University 5000 Forbes Ave. Pittsburgh, PA 15213 EMAIL Michael Furlong Intelligent Robotics Group NASA Ames Research Center Naval Air Station, Moffett Field Mountain View, CA 94035 EMAIL Anatha Ravi Kiran Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. Pasadena, CA 91109 EMAIL |
| Pseudocode | Yes | Algorithm 1: Greedy spectrum selection |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We used the Airborne Visible Near Infrared Spectrometer (AVIRIS) to simulate rover in situ spectra (Green et al. 1998). This instrument observes the spectral region from 0.38µm to 2.5µm at a spectral resolution of 0.01µm and high spatial resolution, making it comparable in spectral range and resolution to field instruments. Our case study centers on the mining district of Cuprite, Nevada. It contains a wide range of distinctive mineralogical signatures, and has been extensively studied through scientific expeditions and field campaigns (Kruse 2002). We also used orbital remote sensing data from the ASTER instrument (Fujisada 1995). |
| Dataset Splits | No | The paper describes a simulation setup and evaluation, but does not explicitly specify training, validation, or test dataset splits in terms of percentages or sample counts for model reproduction. |
| Hardware Specification | No | The paper only states that 'The entire planning process took just a few seconds to run on a modern laptop computer,' which does not provide specific hardware details (e.g., CPU, GPU model, memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library versions needed to replicate the experiment. |
| Experiment Setup | Yes | We simulated 256 random trials of hypothetical traverses. Each had a random start and finish location at the left and right of the image. We defined a path length budget of 125% of the Euclidean distance between start and end locations. This provided a challenging constraint with enough margin to visit a few targets of opportunity. ... Each algorithm selected waypoints from a grid of candidate locations spaced at 20 pixels. The ASTER spectra associated with these grid points became our target library for the unmxing objective. |