Scholars@Duke
Jordan Milton Malof

Jordan Milton Malof

Adjunct Assistant Professor in the Department of Electrical and Computer Engineering
Electrical and Computer Engineering

Selected Publications

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

Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · January 1, 2024 Modern deep neural networks (DNNs) are highly accurate on many recognition tasks for overhead (e.g., satellite) imagery. However, visual domain shifts (e.g., statistical changes due to geography, sensor, or atmospheric conditions) remain a challenge, causi ... Full text Cite

Self-Supervised Encoders Are Better Transfer Learners in Remote Sensing Applications

Journal Article Remote Sensing · November 1, 2022 Transfer learning has been shown to be an effective method for achieving high-performance models when applying deep learning to remote sensing data. Recent research has demonstrated that representations learned through self-supervision transfer better than ... Full text Cite

Learning the Physics of All-Dielectric Metamaterials with Deep Lorentz Neural Networks

Journal Article Advanced Optical Materials · July 1, 2022 Deep neural networks (DNNs) have shown marked achievements across numerous research and commercial settings. Part of their success is due to their ability to “learn” internal representations of the input (x) that are ideal to attain an accurate approximati ... Full text Cite

Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning

Journal Article ISPRS International Journal of Geo-Information · April 1, 2022 Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides power to a single household. A crucial resource for targeting further investm ... Full text Cite

Inverse deep learning methods and benchmarks for artificial electromagnetic material design.

Journal Article Nanoscale · March 2022 In this work we investigate the use of deep inverse models (DIMs) for designing artificial electromagnetic materials (AEMs) - such as metamaterials, photonic crystals, and plasmonics - to achieve some desired scattering properties (e.g., transmissio ... Full text Cite

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

Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · January 1, 2022 Energy system information for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers-termed the power grid-is often incomplete, outdated, or altogether unavailable. Furthermore, conventional m ... Full text Cite

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

Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · January 1, 2022 Recently deep neural networks (DNNs) have achieved tremendous success for object detection in overhead (e.g., satellite) imagery. One ongoing challenge however is the acquisition of training data, due to high costs of obtaining satellite imagery and annota ... Full text Cite

Data from: Benchmarking deep learning architectures for artificial electromagnetic material problems

Dataset · August 25, 2021 Artificial electromagnetic materials (AEMs), including metamaterials, derive their electromagnetic properties from geometry rather than chemistry. With the appropriate geometric design, AEMs have achieved exotic properties not realizable with conventional ... Full text Cite

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Journal Article Advanced Functional Materials · August 1, 2021 Deep neural networks (DNNs) are empirically derived systems that have transformed traditional research methods, and are driving scientific discovery. Artificial electromagnetic materials (AEMs)—including electromagnetic metamaterials, photonic crystals, an ... Full text Cite

Neural-adjoint method for the inverse design of all-dielectric metasurfaces.

Journal Article Optics express · March 2021 All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently uses relatively simple unit-cell designs, but increased geometrical complexity ... Full text Cite

Estimating residential building energy consumption using overhead imagery

Journal Article Applied Energy · December 15, 2020 Residential buildings account for a large proportion of global energy consumption in both low- and high- income countries. Efficient planning to meet building energy needs while increasing operational, economic, and environmental efficiency requires accura ... Full text Cite

The synthinel-1 dataset: A collection of high resolution synthetic overhead imagery for building segmentation

Conference Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 · March 1, 2020 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. However, these benchmark datasets only capture a small ... Full text Cite

Benchmarking deep inverse models over time, and the neural-adjoint method

Conference Advances in Neural Information Processing Systems · January 1, 2020 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. Recently many new approaches based upon deep learning have arisen, ge ... Cite

A large-scale multi-institutional evaluation of advanced discrimination algorithms for buried threat detection in ground penetrating radar

Journal Article IEEE Transactions on Geoscience and Remote Sensing · September 1, 2019 In this paper, we consider the development of algorithms for the automatic detection of buried threats using ground penetrating radar (GPR) measurements. GPR is one of the most studied and successful modalities for automatic buried threat detection (BTD), ... Full text Cite

Deep learning for accelerated all-dielectric metasurface design.

Journal Article Optics express · September 2019 Deep learning has risen to the forefront of many fields in recent years, overcoming challenges previously considered intractable with conventional means. Materials discovery and optimization is one such field, but significant challenges remain, including t ... Full text Cite

Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: Preliminary results

Conference International Geoscience and Remote Sensing Symposium (IGARSS) · July 1, 2019 Segmentation convolutional neural networks (CNNs) are now popular for the semantic segmentation (i.e., dense pixel-wise labeling) of remote sensing imagery, such as color or hyperspectral satellite imagery. In recent years a large number of hand-labeled da ... Full text Cite

A simple rotational equivariance loss for generic convolutional segmentation networks: Preliminary results

Conference International Geoscience and Remote Sensing Symposium (IGARSS) · July 1, 2019 Segmentation convolutional neural networks (SCNNs) are now popular for the semantic segmentation (i.e., dense pixel-wise labeling) of remote sensing imagery, such as color or hyperspectral satellite imagery. One desirable property of SCNNs when applied to ... Full text Cite

Large-scale semantic classification: Outcome of the first year of inria aerial image labeling benchmark

Conference International Geoscience and Remote Sensing Symposium (IGARSS) · October 31, 2018 Over the recent years, there has been an increasing interest in large-scale classification of remote sensing images. In this context, the Inria Aerial Image Labeling Benchmark has been released online in December 2016. In this paper, we discuss the outcome ... Full text Cite

Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar

Conference 2017 9th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2017 - Proceedings · July 28, 2017 Ground Penetrating Radar (GPR) is a remote sensing modality that has been researched extensively for buried threat detection. For this purpose, algorithms can be developed to automatically determine the presence of such threats. To train such algorithms, s ... Full text Cite

Automatic detection of solar photovoltaic arrays in high resolution aerial imagery

Journal Article Applied Energy · December 1, 2016 The quantity of small scale solar photovoltaic (PV) arrays in the United States has grown rapidly in recent years. As a result, there is substantial interest in high quality information about the quantity, power capacity, and energy generated by such array ... Full text Open Access Cite

A Probabilistic Model for Designing Multimodality Landmine Detection Systems to Improve Rates of Advance

Journal Article IEEE Transactions on Geoscience and Remote Sensing · September 1, 2016 The ground penetrating radar (GPR) is a popular and successful remote sensing modality that has been investigated for landmine detection. GPR offers excellent detection performance, but it is limited by a low rate of advance (ROA) due to its short sensing ... Full text Cite

A queuing model for designing multi-modality buried target detection systems: Preliminary results

Conference Proceedings of SPIE - The International Society for Optical Engineering · January 1, 2015 Many remote sensing modalities have been developed for buried target detection, each one offering its own relative advantages over the others. As a result there has been interest in combining several modalities into a single detection platform that benefit ... Full text Cite

Automatic solar photovoltaic panel detection in satellite imagery

Conference 2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015 · January 1, 2015 The quantity of rooftop solar photovoltaic (PV) installations has grown rapidly in the US in recent years. There is a strong interest among decision makers in obtaining high quality information about rooftop PV, such as the locations, power capacity, and e ... Full text Cite