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The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation
Recently deep learning - namely convolutional neural networks (CNNs) - h...
01/15/2020 ∙ by Fanjie Kong, et al. ∙
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Mapping solar array location, size, and capacity using deep learning and overhead imagery
The effective integration of distributed solar photovoltaic (PV) arrays ...
02/28/2019 ∙ by Jordan M. Malof, et al. ∙
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gprHOG: Several Simple Improvements to the Histogram of Oriented Gradients Feature for Threat Detection in Ground-Penetrating Radar
The ground penetrating radar (GPR) is one of the most successful remote ...
06/04/2018 ∙ by Daniël Reichman, et al. ∙
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Dense labeling of large remote sensing imagery with convolutional neural networks: a simple and faster alternative to stitching output label maps
In this work we consider the application of convolutional neural network...
05/30/2018 ∙ by Bohao Huang, et al. ∙
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A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination Algorithms for Buried Threat Detection in Ground Penetrating Radar
In this paper we consider the development of algorithms for the automati...
03/10/2018 ∙ by Jordan M. Malof, et al. ∙
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Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery
We consider the problem of automatically detecting small-scale solar pho...
01/11/2018 ∙ by Joseph Camilo, et al. ∙
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A large comparison of feature-based approaches for buried target classification in forward-looking ground-penetrating radar
Forward-looking ground-penetrating radar (FLGPR) has recently been inves...
02/09/2017 ∙ by Joseph A. Camilo, et al. ∙
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On Choosing Training and Testing Data for Supervised Algorithms in Ground Penetrating Radar Data for Buried Threat Detection
Ground penetrating radar (GPR) is one of the most popular and successful...
12/11/2016 ∙ by Daniël Reichman, et al. ∙
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