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Found 10 papers, 3 papers with code
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.
Deep Active Learning for Scientific Computing in the Wild
Deep learning (DL) is revolutionizing the scientific computing community.
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.
Towards Robust Deep Active Learning for Scientific Computing
Deep learning (DL) is revolutionizing the scientific computing community.
Utilizing geospatial data for assessing energy security: Mapping small solar home systems using unmanned aerial vehicles and deep learning
In this work, we explore the viability and cost-performance tradeoff of using automatic SHS detection on unmanned aerial vehicle (UAV) imagery as an alternative to satellite imagery.
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.
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
To this end, we propose a physics-infused deep neural network based on the Blaschke products for phase retrieval.
Benchmarking deep inverse models over time, and the neural-adjoint method
We consider the task of solving generic inverse problems, where one wishes to determine the hidden parameters of a natural system that will give rise to a particular set of measurements.
