On The Extraction of Training Imagery from Very Large Remote Sensing Datasets for Deep Convolutional Segmenatation Networks. - Bohao Huang; Daniel Reichman 002; Leslie M. Collins; Kyle Bradbury; Jordan M. Malof (IGARSS, 2018)

Deep Convolutional Segmentation of Remote Sensing Imagery: A Simple and Efficient Alternative to Stitching Output Labels. - Bohao Huang; Leslie M. Collins; Kyle Bradbury; Jordan M. Malof (IGARSS, 2018)

Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark. - Bohao Huang; Kangkang Lu 001; Nicolas Audebert; Andrew Khalel; Yuliya Tarabalka; Jordan M. Malof; Alexandre Boulch; Bertrand Le Saux; Leslie M. Collins; Kyle Bradbury; Sébastien Lefèvre; Motaz El-Saban (IGARSS, 2018)

Dense labeling of large remote sensing imagery with convolutional neural networks: a simple and faster alternative to stitching output label maps. - Bohao Huang; Daniel Reichman 002; Leslie M. Collins; Kyle Bradbury; Jordan M. Malof (CoRR, 2018)

Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: preliminary results. - Fanjie Kong; Cheng Chen; Bohao Huang; Leslie M. Collins; Kyle Bradbury; Jordan M. Malof (IGARSS, 2019)

A simple rotational equivariance loss for generic convolutional segmentation networks: preliminary results. - Kangcheng Lin; Bohao Huang; Leslie M. Collins; Kyle Bradbury; Jordan M. Malof (IGARSS, 2019)

Mapping solar array location, size, and capacity using deep learning and overhead imagery. - Jordan M. Malof; Boning Li; Bohao Huang; Kyle Bradbury; Artem Stretslov (CoRR, 2019)

Do Deep Learning Models Generalize to Overhead Imagery from Novel Geographic Domains? The xGD Benchmark Problem. - Bohao Huang; Kyle Bradbury; Leslie M. Collins; Jordan M. Malof (IGARSS, 2020)

Designing Synthetic Overhead Imagery to Match a Target Geographic Region: Preliminary Results Training Deep Learning Models. - Varun Nair; Paul Rhee; Jichen Yang; Bohao Huang; Kyle Bradbury; Jordan M. Malof (IGARSS, 2020)

The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation. - Fanjie Kong; Bohao Huang; Kyle Bradbury; Jordan M. Malof (WACV, 2020)

Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery. - Can Yaris; Bohao Huang; Kyle Bradbury; Jordan M. Malof (CoRR, 2021)

SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems. - Yang Xu 007; Bohao Huang; Xiong Luo; Kyle Bradbury; Jordan M. Malof (CoRR, 2021)

Using Synthetic Satellite Imagery from Virtual Worlds to Train Deep Learning Models for Object Recognition. - Bohao Huang (2021)

GridTracer: Automatic Mapping of Power Grids Using Deep Learning and Overhead Imagery. - Bohao Huang; Jichen Yang; Artem Streltsov; Kyle Bradbury; Leslie M. Collins; Jordan M. Malof (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022)

SIMPL: Generating Synthetic Overhead Imagery to Address Custom Zero-Shot and Few-Shot Detection Problems. - Yang Xu 007; Bohao Huang; Xiong Luo; Kyle Bradbury; Jordan M. Malof (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022)

Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery. - Can Yaras; Kaleb Kassaw; Bohao Huang; Kyle Bradbury; Jordan M. Malof (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024)