Publications
Space-based monitoring of penguin diet links sea ice, food webs, and population change
Youngflesh, C., C. Che-Castaldo, M. Schwaller, S. Serbin, M. Polito, and H.J. Lynch. 2026. Space-based monitoring of links sea ice, food webs, and population change. Current Biology 36:1-8.
Detecting Antarctic breeding flying seabird colonies with satellite remote sensing: opportunities and limitations
Lynch, H.J., Schwaller, M., L. Emmerson, K. Blackwell, C. Southwell. 2026. Detecting Antarctic breeding flying seabird colonies with satellite remote sensing: opportunities and limitations. Polar Biology 49: 28.
Predicting pack-ice seal occupancy of ice floes along the Western Antarctic Peninsula
Wethington, M.J., B. Sen, and H.J. Lynch. 2024. Predicting pack-ice seal occupancy of ice floes along the Western Antarctic Peninsula. PLoS ONE 19(12): e0311747.
Satellite remote sensing for wildlife research in the polar regions
Lynch, H.J. Satellite remote sensing for wildlife research in the polar regions. 2023. Marine Technology Society Journal 57(3): 43-50.
Species classification of Antarctic pack-ice seals using very-high-resolution satellite imagery
Wethington, M.J., B.C. Goncalves, E. Talis, and H.J. Lynch. 2023. Species classification of Antarctic pack-ice seals using very-high-resolution satellite imagery. Marine Mammal Science 1-17.
SealNet 2.0: Human-level fully-automated pack-ice seal detection
Goncalves, B.C., M. Wethington, and H.J. Lynch. 2022. SealNet 2.0: Human-level fully-automated pack-ice seal detection in very-high resolution satellite imagery with CNN model ensembles. Remote Sensing 14(22): 5655.
A Convolutional Neural Network Architecture Designed for the Automated Survey of Seabird Colonies
Le, H., D. Samaras, and H.J. Lynch. 2022. A convolutional neural network architecture designed for the automated survey of seabird colonies. Remote Sensing of Ecology & Conservation 8(2): 251-262.
Fine-Scale Sea Ice Extraction for High-Resolution Satellite Imagery with Weakly-Supervised CNNs
Goncalves, B.G. and H.J. Lynch. 2021. Remote Sensing 13: 3562.
Comparing Workflow Application Designs for High Resolution Satellite Image Analysis
Al-Saadi, A., I. Paraskevakos, B.C. Goncalves, H.J. Lynch, S. Jha, and M. Turilli. 2021. Future Generation Computer Systems 124: 315-329.
SealNet: A Fully Automated Pack-Ice Seal Detection Pipeline for Sub-Meter Satellite Imagery
Goncalves, B., B. Spitzbart, and H.J. Lynch. 2020. Remote Sensing of Environment 239: 111617.
Weakly Labeling the Antarctic: The Penguin Colony Case
Le, H., B. Goncalves, D. Samaras, and H.J. Lynch. 2019. CVPR 2019 Workshop.
Aerial-Trained Deep Learning Networks for Surveying Cetaceans From Satellite Imagery
Borowicz, A., H. Le, G. Humphries, G. Nehls, C. Höschle, V. Kosarev, and H.J. Lynch. 2019. PLoS ONE 14(10): e0212532.
A Continent-Wide Search for Antarctic Petrel Breeding Sites With Satellite Remote Sensing
Schwaller, M.R., H.J. Lynch, A. Tarroux, and B. Prehn. 2018. Remote Sensing of Environment 210: 444-451.
Benchmarking of Data Fusion Algorithms in Support of Earth Observation Based Antarctic Wildlife Monitoring
Witharana, C., M.A. LaRue, and H.J. Lynch. 2016. ISPRS Journal of Photogrammetry and Remote Sensing 113: 124-143.
An Object-Based Image Analysis Approach for Detecting Penguin Guano From Very High Spatial Resolution Satellite Images
Witharana, C., and H.J. Lynch. 2016. Remote Sensing 8(5): 375.
Multi-Sensor Cross Validation of Adélie Penguin Detection and Abundance Estimation
Lynch, H. J., and M.R. Schwaller. 2014. PLoS ONE 9(11): e113301.
First Global Survey of Adélie Penguin Populations
Lynch, H.J., and M.A. LaRue. 2014. The Auk 131(4): 457-466.
Establishing a Method to Estimate Adélie Penguin Populations Using Remotely-Sensed Imagery
LaRue, M.A., H.J. Lynch, P. Lyver, K. Barton, D.G. Ainley, A.M. Pollard, W. Fraser, G. Ballard. 2014. Polar Biology 37: 507-517.
Detection, Differentiation, and Abundance Estimation of Penguin Species by High-Resolution Satellite Imagery
Lynch, H.J., R. White, A.D. Black, and R. Naveen. 2012. Polar Biology 35: 963-968.