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Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis



Simiao Ren , Wei Hu , Kyle Bradbury , Dylan Harrison-Atlas , Laura Malaguzzi Valeri , Brian Murray , Jordan M. Malof


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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 , Robert Beach , Jay Rineer , Kyle Bradbury


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SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems



Yang Xu , Bohao Huang , Xiong Luo , Kyle Bradbury , Jordan M. Malof


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Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery



Can Yaras , Bohao Huang , Kyle Bradbury , Jordan M. Malof

* Includes a main paper (10 pages) and supplementary material (4 additional pages). This paper is currently undergoing peer review 

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GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery



Bohao Huang , Jichen Yang , Artem Streltsov , Kyle Bradbury , Leslie M. Collins , Jordan Malof


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The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation



Fanjie Kong , Bohao Huang , Kyle Bradbury , Jordan M. Malof

* Preprint of paper accepted for publication at Winter Conference on Applications of Computer Vision (WACV) 2020 

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Mapping solar array location, size, and capacity using deep learning and overhead imagery



Jordan M. Malof , Boning Li , Bohao Huang , Kyle Bradbury , Artem Stretslov


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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



Bohao Huang , Daniel Reichman , Leslie M. Collins , Kyle Bradbury , Jordan M. Malof


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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



Joseph Camilo , Rui Wang , Leslie M. Collins , Kyle Bradbury , Jordan M. Malof

* Accepted for publication at the 2017 IEEE Applied Imagery Pattern Recognition (AIPR) Workshop. Presented at the conference in Washington D.C., October 10-12 

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