198 citations · 458 across the 34 of their papers we have counts for
34 papers
Improved Automatic Target Recognition in Synthetic Aperture Sonar Imagery Using Large Deep Neural Networks
C. J. Moore, Alex Hurt, Jordan Malof
Automatic Target Recognition (ATR) in Synthetic Aperture Sonar (SAS) is a task largely dominated by deep neural networks (DNNs). Most SAS-ATR models use convolutional neural networ…
A Comparison of Data Augmentation Methods for Training Deep Neural Networks on Synthetic Aperture Sonar
C. J. Moore, Gregory D. Vetaw, Jordan Malof
In this work we study Automatic Target Recognition (ATR) for Synthetic Aperture Sonar (SAS) data with a focus on deep neural networks (DNNs). The main challenge in training DNNs fo…
Global Building Area Estimation Products: How Accurate Are They?
Saad Lahrichi, Doa'a Allabadi, Kyle Bradbury +1
Geo-spatial rasters of building footprint area are useful for a variety of tasks, such as monitoring urbanization, improving energy efficiency, and tracking greenhouse gas emission…
Closing Gaps in Emissions Monitoring with Climate TRACE
Brittany V. Lancellotti, Jordan M. Malof, Aaron Davitt +32
Global greenhouse gas emissions estimates are essential for monitoring and mitigation planning. Existing emissions datasets provide critical foundations for understanding emissions…
Improving and Evaluating Open Deep Research Agents
Doaa Allabadi, Kyle Bradbury, Jordan M. Malof
We focus here on Deep Research Agents (DRAs), which are systems that can take a natural language prompt from a user, and then autonomously search for, and utilize, internet-based c…
Is Self-Supervised Pre-training on Satellite Imagery Better than ImageNet? A Systematic Study with Sentinel-2
Saad Lahrichi, Zion Sheng, Shufan Xia +2
Self-supervised learning (SSL) has demonstrated significant potential in pre-training robust models with limited labeled data, making it particularly valuable for remote sensing (R…
Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?
Kaleb Kassaw, Francesco Luzi, Leslie M. Collins +1
Image classification models, including convolutional neural networks (CNNs), perform well on a variety of classification tasks but struggle under conditions of partial occlusion, i…
Deep Generalized Green's Functions
Rixi Peng, Juncheng Dong, Jordan Malof +2
In this study, we address the challenge of obtaining a Green's function operator for linear partial differential equations (PDEs). The Green's function is well-sought after due to…
Machine Learning for Mie-Tronics
Wenhao Li, Hooman Barati Sedeh, Willie J. Padilla +3
Electromagnetic multipole expansion theory underpins nanoscale light-matter interactions, particularly within subwavelength meta-atoms, paving the way for diverse and captivating o…
Segment anything, from space?
Simiao Ren, Francesco Luzi, Saad Lahrichi +4
Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM). SAM can segment objects in input…
Does Deep Active Learning Work in the Wild?
Simiao Ren, Saad Lahrichi, Yang Deng +3
Deep active learning (DAL) methods have shown significant improvements in sample efficiency compared to simple random sampling. While these studies are valuable, they nearly always…
Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer
Evelyn A. Stump, Francesco Luzi, Leslie M. Collins +1
Recent object detection models for infrared (IR) imagery are based upon deep neural networks (DNNs) and require large amounts of labeled training imagery. However, publicly availab…
Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling
Gregory P. Spell, Simiao Ren, Leslie M. Collins +1
We propose and show the efficacy of a new method to address generic inverse problems. Inverse modeling is the task whereby one seeks to determine the control parameters of a natura…
Transformers For Recognition In Overhead Imagery: A Reality Check
Francesco Luzi, Aneesh Gupta, Leslie Collins +2
There is evidence that transformers offer state-of-the-art recognition performance on tasks involving overhead imagery (e.g., satellite imagery). However, it is difficult to make u…
Meta-simulation for the Automated Design of Synthetic Overhead Imagery
Handi Yu, Simiao Ren, Leslie M. Collins +1
The use of synthetic (or simulated) data for training machine learning models has grown rapidly in recent years. Synthetic data can often be generated much faster and more cheaply…
Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis
Simiao Ren, Wei Hu, Kyle Bradbury +4
High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is…
Towards Robust Deep Active Learning for Scientific Computing
Simiao Ren, Yang Deng, Willie J. Padilla +1
Deep learning (DL) is revolutionizing the scientific computing community. To reduce the data gap, active learning has been identified as a promising solution for DL in the scientif…
Utilizing geospatial data for assessing energy security: Mapping small solar home systems using unmanned aerial vehicles and deep learning
Simiao Ren, Jordan Malof, T. Robert Fetter +3
Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides p…
Inverse deep learning methods and benchmarks for artificial electromagnetic material design
Simiao Ren, Ashwin Mahendra, Omar Khatib +3
Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices. Many DL inverse te…
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
Juncheng Dong, Simiao Ren, Yang Deng +5
Numerous physical systems are described by ordinary or partial differential equations whose solutions are given by holomorphic or meromorphic functions in the complex domain. In ma…
SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems
Yang Xu, Bohao Huang, Xiong Luo +2
Recently deep neural networks (DNNs) have achieved tremendous success for object detection in overhead (e.g., satellite) imagery. One ongoing challenge however is the acquisition o…
Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery
Can Yaras, Kaleb Kassaw, Bohao Huang +2
Modern deep neural networks (DNNs) are highly accurate on many recognition tasks for overhead (e.g., satellite) imagery. However, visual domain shifts (e.g., statistical changes du…
GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery
Bohao Huang, Jichen Yang, Artem Streltsov +3
Energy system information valuable for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers, termed the power grid…
Neural-adjoint method for the inverse design of all-dielectric metasurfaces
Yang Deng, Simiao Ren, Kebin Fan +2
All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently us…