| Deep learning for accelerated all-dielectric metasurface design CC Nadell, B Huang, JM Malof, WJ Padilla Optics express 27 (20), 27523-27535, 2019 | 356 | 2019 |
| Large-scale semantic classification: outcome of the first year of inria aerial image labeling benchmark B Huang, K Lu, N Audeberr, A Khalel, Y Tarabalka, J Malof, A Boulch, ... IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018 | 99 | 2018 |
| Estimating residential building energy consumption using overhead imagery A Streltsov, JM Malof, B Huang, K Bradbury Applied Energy 280, 116018, 2020 | 54 | 2020 |
| The Synthinel-1 dataset: A collection of high resolution synthetic overhead imagery for building segmentation F Kong, B Huang, K Bradbury, J Malof Proceedings of the IEEE/CVF winter conference on applications of computer …, 2020 | 46 | 2020 |
| Tiling and stitching segmentation output for remote sensing: Basic challenges and recommendations B Huang, D Reichman, LM Collins, K Bradbury, JM Malof arXiv preprint arXiv:1805.12219, 2018 | 39 | 2018 |
| Mapping solar array location, size, and capacity using deep learning and overhead imagery JM Malof, B Li, B Huang, K Bradbury, A Stretslov Preprint at https://arxiv. org/abs/1902.10895, 2019 | 34* | 2019 |
| Non-intrusive load monitoring system performance over a range of low frequency sampling rates B Huang, M Knox, K Bradbury, LM Collins, RG Newell 2017 IEEE 6th International Conference on Renewable Energy Research and …, 2017 | 18 | 2017 |
| Randomized histogram matching: A simple augmentation for unsupervised domain adaptation in overhead imagery C Yaras, K Kassaw, B Huang, K Bradbury, JM Malof IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 | 13 | 2023 |
| Deep convolutional segmentation of remote sensing imagery: A simple and efficient alternative to stitching output labels B Huang, LM Collins, K Bradbury, JM Malof IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018 | 13 | 2018 |
| GridTracer: Automatic mapping of power grids using deep learning and overhead imagery B Huang, J Yang, A Streltsov, K Bradbury, LM Collins, JM Malof IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2021 | 9 | 2021 |
| Tiling and stitching segmentation output for remote sensing: basic challenges and recommendations (2018) B Huang, D Reichman, LM Collins, K Bradbury, JM Malof arXiv preprint arXiv:1805.12219 3, 1805 | 9 | 1805 |
| SIMPL: Generating synthetic overhead imagery to address custom zero-shot and few-shot detection problems Y Xu, B Huang, X Luo, K Bradbury, JM Malof IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2022 | 8 | 2022 |
| Dense labeling of large remote sensing imagery with convolutional neural networks: a simple and faster alternative to stitching output label maps B Huang, D Reichman, LM Collins, K Bradbury, JM Malof arXiv preprint arXiv:1805.12219, 2018 | 8 | 2018 |
| Bertr Le Saux, Leslie Collins, Kyle Bradbury, et al. Largescale semantic classification: outcome of the first year of inria aerial image labeling benchmark B Huang, K Lu, N Audeberr, A Khalel, Y Tarabalka, J Malof, A Boulch IGARSS 2018, 6947-6950, 2018 | 8 | 2018 |
| Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: preliminary results F Kong, C Chen, B Huang, LM Collins, K Bradbury, JM Malof IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium …, 2019 | 7 | 2019 |
| A simple rotational equivariance loss for generic convolutional segmentation networks: Preliminary results K Lin, B Huang, LM Collins, K Bradbury, JM Malof IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium …, 2019 | 7 | 2019 |
| Do deep learning models generalize to overhead imagery from novel geographic domains? the xgd benchmark problem B Huang, K Bradbury, LM Collins, JM Malof IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium …, 2020 | 5 | 2020 |
| Sampling training images from a uniform grid improves the performance and learning speed of deep convolutional segmentation networks on large aerial imagery B Huang, D Reichman, LM Collins, K Bradbury, JM Malof IGARSS, 2018 | 5 | 2018 |
| Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations. arXiv 2019 B Huang, D Reichman, LM Collins, K Bradbury, JM Malof arXiv preprint arXiv:1805.12219, 0 | 5 | |
| Mapping solar array location, size, and capacity using deep learning and overhead imagery. arXiv 2019 JM Malof, B Li, B Huang, K Bradbury, A Stretslov arXiv preprint arXiv:1902.10895, 0 | 5 | |
Jordan M MalofAssistant Professor, University of MontanaVerified email at duke.edu
