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Books and Theses
- 2021
[b1]Bohao Huang:
Using Synthetic Satellite Imagery from Virtual Worlds to Train Deep Learning Models for Object Recognition. Duke University, Durham, NC, USA, 2021
Journal Articles
- 2024
- [j3]Can Yaras
, Kaleb Kassaw, Bohao Huang, Kyle Bradbury, Jordan M. Malof:
Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 17: 1988-1998 (2024) - 2022
- [j2]Yang Xu, Bohao Huang, Xiong Luo, Kyle Bradbury, Jordan M. Malof:
SIMPL: Generating Synthetic Overhead Imagery to Address Custom Zero-Shot and Few-Shot Detection Problems. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 15: 4386-4396 (2022) - [j1]Bohao Huang, Jichen Yang, Artem Streltsov, Kyle Bradbury, Leslie M. Collins, Jordan M. Malof:
GridTracer: Automatic Mapping of Power Grids Using Deep Learning and Overhead Imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 15: 4956-4970 (2022)
Conference and Workshop Papers
- 2020
- [c8]Varun Nair, Paul Rhee, Jichen Yang, Bohao Huang, Kyle Bradbury, Jordan M. Malof:
Designing Synthetic Overhead Imagery to Match a Target Geographic Region: Preliminary Results Training Deep Learning Models. IGARSS 2020: 948-951 - [c7]Bohao Huang, Kyle Bradbury, Leslie M. Collins, Jordan M. Malof:
Do Deep Learning Models Generalize to Overhead Imagery from Novel Geographic Domains? The xGD Benchmark Problem. IGARSS 2020: 1476-1479 - [c6]Fanjie Kong, Bohao Huang, Kyle Bradbury, Jordan M. Malof:
The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation. WACV 2020: 1803-1812 - 2019
- [c5]Kangcheng Lin, Bohao Huang, Leslie M. Collins, Kyle Bradbury, Jordan M. Malof:
A simple rotational equivariance loss for generic convolutional segmentation networks: preliminary results. IGARSS 2019: 3876-3879 - [c4]Fanjie Kong, Cheng Chen, Bohao Huang, Leslie M. Collins, Kyle Bradbury, Jordan M. Malof:
Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: preliminary results. IGARSS 2019: 3903-3906 - 2018
- [c3]Bohao Huang, Daniel Reichman, Leslie M. Collins, Kyle Bradbury, Jordan M. Malof:
On The Extraction of Training Imagery from Very Large Remote Sensing Datasets for Deep Convolutional Segmenatation Networks. IGARSS 2018: 6895-6898 - [c2]Bohao Huang, Leslie M. Collins, Kyle Bradbury, Jordan M. Malof:
Deep Convolutional Segmentation of Remote Sensing Imagery: A Simple and Efficient Alternative to Stitching Output Labels. IGARSS 2018: 6899-6902 - [c1]Bohao Huang, Kangkang Lu, Nicolas Audebert, Andrew Khalel, Yuliya Tarabalka, Jordan M. Malof, Alexandre Boulch, Bertrand Le Saux, Leslie M. Collins, Kyle Bradbury, Sébastien Lefèvre, Motaz El-Saban:
Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark. IGARSS 2018: 6947-6950
Informal and Other Publications
- 2021
- [i6]Bohao Huang, Jichen Yang, Artem Streltsov, Kyle Bradbury, Leslie M. Collins, Jordan M. Malof:
GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery. CoRR abs/2101.06390 (2021) - [i5]Can Yaris, Bohao Huang, Kyle Bradbury, Jordan M. Malof:
Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery. CoRR abs/2104.14032 (2021) - [i4]Yang Xu, Bohao Huang, Xiong Luo, Kyle Bradbury, Jordan M. Malof:
SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems. CoRR abs/2106.15681 (2021) - 2020
- [i3]Fanjie Kong, Bohao Huang, Kyle Bradbury, Jordan M. Malof:
The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation. CoRR abs/2001.05130 (2020) - 2019
- [i2]Jordan M. Malof, Boning Li, Bohao Huang, Kyle Bradbury, Artem Stretslov:
Mapping solar array location, size, and capacity using deep learning and overhead imagery. CoRR abs/1902.10895 (2019) - 2018
- [i1]Bohao Huang, Daniel Reichman, Leslie M. Collins, Kyle Bradbury, Jordan M. Malof:
Dense labeling of large remote sensing imagery with convolutional neural networks: a simple and faster alternative to stitching output label maps. CoRR abs/1805.12219 (2018)
Coauthor Index
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