no code implementations • 23 Apr 2024 • Darui Lu, Yang Deng, Jordan M. Malof, Willie J. Padilla
LLMs possess some advantages over humans that may give them benefits for research, including the ability to process enormous amounts of data, find hidden patterns in data, and operate in higher-dimensional spaces.
no code implementations • 25 Apr 2023 • Simiao Ren, Francesco Luzi, Saad Lahrichi, Kaleb Kassaw, Leslie M. Collins, Kyle Bradbury, Jordan M. Malof
In this work, we examine whether SAM's performance extends to overhead imagery problems and help guide the community's response to its development.
no code implementations • 24 Dec 2022 • Evelyn A. Stump, Francesco Luzi, Leslie M. Collins, Jordan M. Malof
To address this problem, we explore cross-modal style transfer (CMST) to leverage large and diverse color imagery datasets so that they can be used to train DNN-based IR image based object detectors.
no code implementations • 25 Nov 2022 • Gregory P. Spell, Simiao Ren, Leslie M. Collins, Jordan M. Malof
We propose and show the efficacy of a new method to address generic inverse problems.
no code implementations • 19 Sep 2022 • Handi Yu, Simiao Ren, Leslie M. Collins, Jordan M. Malof
The use of synthetic (or simulated) data for training machine learning models has grown rapidly in recent years.
no code implementations • 18 Feb 2022 • Simiao Ren, Wei Hu, Kyle Bradbury, Dylan Harrison-Atlas, Laura Malaguzzi Valeri, Brian Murray, Jordan M. Malof
These include the opportunity to extend the methods beyond electricity to broader energy systems and wider geographic areas; and the ability to expand the use of these methods in research and decision making as satellite data become cheaper and easier to access.
2 code implementations • 19 Dec 2021 • Simiao Ren, Ashwin Mahendra, Omar Khatib, Yang Deng, Willie J. Padilla, Jordan M. Malof
Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices.
1 code implementation • 29 Jun 2021 • Yang Xu, Bohao Huang, Xiong Luo, Kyle Bradbury, Jordan M. Malof
Recently deep neural networks (DNNs) have achieved tremendous success for object detection in overhead (e. g., satellite) imagery.
no code implementations • 28 Apr 2021 • Can Yaras, Kaleb Kassaw, Bohao Huang, Kyle Bradbury, Jordan M. Malof
Modern deep neural networks (DNNs) are highly accurate on many recognition tasks for overhead (e. g., satellite) imagery.
1 code implementation • 15 Jan 2020 • Fanjie Kong, Bohao Huang, Kyle Bradbury, Jordan M. Malof
Recently deep learning - namely convolutional neural networks (CNNs) - have yielded impressive performance for the task of building segmentation on large overhead (e. g., satellite) imagery benchmarks.
2 code implementations • 28 Feb 2019 • Wei Hu, Kyle Bradbury, Jordan M. Malof, Boning Li, Bohao Huang, Artem Streltsov, K. Sydny Fujita, Ben Hoen
Our findings suggest that traditional performance evaluation of the automated identification of solar PV from satellite imagery may be optimistic due to common limitations in the validation process.
no code implementations • 4 Jun 2018 • Daniel Reichman, Leslie M. Collins, Jordan M. Malof
Substantial research has been devoted to the development of algorithms that automate buried threat detection (BTD) with ground penetrating radar (GPR) data, resulting in a large number of proposed algorithms.
no code implementations • 30 May 2018 • Bohao Huang, Daniel Reichman, Leslie M. Collins, Kyle Bradbury, Jordan M. Malof
In this work we consider the application of convolutional neural networks (CNNs) for pixel-wise labeling (a. k. a., semantic segmentation) of remote sensing imagery (e. g., aerial color or hyperspectral imagery).
Segmentation Of Remote Sensing Imagery Semantic Segmentation
no code implementations • 10 Mar 2018 • Jordan M. Malof, Daniel Reichman, Andrew Karem, Hichem Frigui, Dominic K. C. Ho, Joseph N. Wilson, Wen-Hsiung Lee, William Cummings, Leslie M. Collins
In this work we report the results of a multi-institutional effort to develop advanced buried threat detection algorithms for a real-world GPR BTD system.
no code implementations • 11 Jan 2018 • Joseph Camilo, Rui Wang, Leslie M. Collins, Kyle Bradbury, Jordan M. Malof
In this work, we employ a state-of-the-art convolutional neural network architecture, called SegNet (Badrinarayanan et.
no code implementations • 9 Feb 2017 • Joseph A. Camilo, Leslie M. Collins, Jordan M. Malof
The first goal of this work is to provide a comprehensive comparison of detection performance using existing features on a large collection of FLGPR data.
no code implementations • 11 Dec 2016 • Daniël Reichman, Leslie M. Collins, Jordan M. Malof
Training data most often consists of 2-dimensional images (or patches) of GPR data, from which features are extracted, and provided to the classifier during training and testing.
no code implementations • 20 Jul 2016 • Jordan M. Malof, Kyle Bradbury, Leslie M. Collins, Richard G. Newell
Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution.