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Found 18 papers, 4 papers with code
Can Large Language Models Learn the Physics of Metamaterials? An Empirical Study with ChatGPT
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.
Segment anything, from space?
In this work, we examine whether SAM's performance extends to overhead imagery problems and help guide the community's response to its development.
Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer
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.
Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling
We propose and show the efficacy of a new method to address generic inverse problems.
Meta-simulation for the Automated Design of Synthetic Overhead Imagery
The use of synthetic (or simulated) data for training machine learning models has grown rapidly in recent years.
Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis
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.
Inverse deep learning methods and benchmarks for artificial electromagnetic material design
Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices.
SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems
Recently deep neural networks (DNNs) have achieved tremendous success for object detection in overhead (e. g., satellite) imagery.
Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery
Modern deep neural networks (DNNs) are highly accurate on many recognition tasks for overhead (e. g., satellite) imagery.
The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation
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.
What you get is not always what you see: pitfalls in solar array assessment using overhead imagery
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.
gprHOG and the popularity of Histogram of Oriented Gradients (HOG) for Buried Threat Detection in Ground-Penetrating Radar
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.
Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations
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
A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination Algorithms for Buried Threat Detection in Ground Penetrating Radar
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.
Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery
In this work, we employ a state-of-the-art convolutional neural network architecture, called SegNet (Badrinarayanan et.
A large comparison of feature-based approaches for buried target classification in forward-looking ground-penetrating radar
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.
On Choosing Training and Testing Data for Supervised Algorithms in Ground Penetrating Radar Data for Buried Threat Detection
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.
Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagery
Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution.
