| A comparison of remotely sensed environmental predictors for avian distributions LM Hopkins, TA Hallman, J Kilbride, WD Robinson, RA Hutchinson Landscape Ecology 37 (4), 997-1016, 2022 | 10 | 2022 |
| Cross-validation for geospatial data: Estimating generalization performance in geostatistical problems J Wang, L Hopkins, T Hallman, WD Robinson, R Hutchinson Transactions on Machine Learning Research, 2023 | 2 | 2023 |
| Electric-field-induced band bending on GaN: in situ effects of electron beam irradiation on time-dependent cathodoluminescence EM Campo, M Pophristic, L Hopkins, IT Ferguson Applied Optics 54 (12), 3613-3623, 2015 | 2 | 2015 |
| Scalable Hyperspectral Inversion with Uncertainty Quantification L Hopkins, N LaHaye, J Kravitz, S Mauceri AGU Fall Meeting Abstracts 2022, GC42D-0742, 2022 | 1 | 2022 |
| Validation of Machine Learning Algorithms for Hyperspectral Inversion of Common Water Quality Indicators J Kravitz, L Hopkins, N LaHaye, S Mauceri AGU Fall Meeting Abstracts 2022, GC33C-02, 2022 | 1 | 2022 |
| NASA's Prototype Spectral Water Inversion Processor and Emulator (SWIPE): Towards Global Coastal and Inland Water Quality and Algal Biodiversity Monitoring J Kravitz, L Robertson-Lain, S Mauceri, L Hopkins, N LaHaye, T Norman, ... AGU Fall Meeting Abstracts 2022, B22D-1469, 2022 | 1 | 2022 |
| Pushing the limits of aquatic remote sensing: Synthetic data and deep learning for fast inverse emulation of a coupled water-atmosphere radiative transfer model J Kravitz, LS Guild, L Lain, S Mauceri, N LaHaye, L Hopkins, IG Brosnan 2024 Ocean Sciences Meeting, 2024 | | 2024 |
| Model Evaluation for Geospatial Problems J Wang, T Hallman, L Hopkins, JB Kilbride, WD Robinson, R Hutchinson NeurIPS 2023 Computational Sustainability: Promises and Pitfalls from Theory …, 2023 | | 2023 |
| Opportunities and Challenges using Observations and Simulated Data for Machine-Learning-Based Retrievals of Water Quality N LaHaye, L Hopkins, J Kravitz, S Mauceri Fall Meeting 2022, 2022 | | 2022 |
| What are deep networks learning? A Comparison of Deep Learning and Traditional Summaries of Remotely Sensed Imagery L Hopkins, TA Hallman, JB Kilbride, WD Robinson, WK Wong, ... AGU Fall Meeting 2021, 2021 | | 2021 |
| Deep Learning Methods for Extracting Habitat Summaries from Remotely Sensed Data for Species Distribution Modeling L Hopkins, U Zaragoza, WK Wong, R Hutchinson AGU Fall Meeting Abstracts 2020, B071-05, 2020 | | 2020 |
| Improving species distribution models with habitat summaries extracted from remote sensing imagery with deep learning methods L Hopkins, U Zaragoza, R Hutchinson 2020 ESA Annual Meeting (August 3-6), 0 | | |
