Jordan Milton Malof

Journal article ISPRS International Journal of Geo Information · June 1, 2026 Point data, such as population, disease incidence, and greenhouse gas emissions, are commonly aggregated to a uniform grid of raster data for storage and representation. In many remote sensing applications, polygons are instead used to describe regions of ... Full text Cite

Are deep learning models robust to partial object occlusion in visual recognition tasks?

Journal article Pattern Recognition · March 1, 2026 Image classification models, including convolutional neural networks (CNNs), perform well on a variety of classification tasks but struggle under conditions of partial occlusion of relevant objects. Methods to improve performance under occlusion, including ... Full text Cite

Physics-informed learning in artificial electromagnetic materials

Journal article Applied Physics Reviews · March 1, 2025 The advent of artificial intelligence—deep neural networks (DNNs) in particular—has transformed traditional research methods across many disciplines. DNNs are data driven systems that use large quantities of data to learn patterns that are fundamental to a ... Full text Cite

Learning Electromagnetic Metamaterial Physics With ChatGPT

Journal article IEEE Access · January 1, 2025 Large language models (LLMs) such as ChatGPT, Gemini, LlaMa, and Claude are trained on massive quantities of text parsed from the internet and have shown a remarkable ability to respond to complex prompts in a manner often indistinguishable from humans. Fo ... Full text Cite

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

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

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

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