J
Jordan M. Malof
Researcher at Duke University
Publications - 101
Citations - 2180
Jordan M. Malof is an academic researcher from Duke University. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 14, co-authored 82 publications receiving 1504 citations. Previous affiliations of Jordan M. Malof include Durham University & University of Louisville.
Papers
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Journal ArticleDOI
Distributed solar photovoltaic array location and extent dataset for remote sensing object identification.
Kyle Bradbury,Raghav Saboo,Timothy L. Johnson,Jordan M. Malof,Arjun Devarajan,Wuming Zhang,Leslie M. Collins,Richard G. Newell +7 more
TL;DR: This work created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery, and contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California.
Journal ArticleDOI
Deep learning for accelerated all-dielectric metasurface design
TL;DR: A novel method to solve the inverse modeling problem, termed fast forward dictionary search (FFDS), is developed, which offers tremendous controls to the designer and only requires an accurate forward neural network model.
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Automatic detection of solar photovoltaic arrays in high resolution aerial imagery
TL;DR: The results are the first of their kind for the detection of solar PV in aerial imagery, demonstrating the feasibility of the approach and establishing a baseline performance for future investigations.
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Imaging descriptors improve the predictive power of survival models for glioblastoma patients
TL;DR: Investigating whether the predictive power of a survival model based on clinical patient features improves when MRI features are also included in the model finds imaging features assessed using a controlled lexicon have additional predictive value compared with clinical features when predicting survival time in glioblastoma patients.
Proceedings ArticleDOI
Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark
Bohao Huang,Kangkang Lu,Nicolas Audeberr,Andrew Khalel,Yuliya Tarabalka,Jordan M. Malof,Alexandre Boulch,Bertr Le Saux,Leslie M. Collins,Kyle Bradbury,Sébastien Lefèvre,Motaz El-Saban +11 more
TL;DR: The outcomes of the first year of the Inria Aerial Image Labeling Benchmark, which consisted in dense labeling of aerial images into building / not building classes, are discussed, with four methods with the highest numerical accuracies being convolutional neural network approaches.