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Zachary L. Langford
Zachary L. Langford
Databricks
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Title
Cited by
Cited by
Year
Arctic vegetation mapping using unsupervised training datasets and convolutional neural networks
ZL Langford, J Kumar, FM Hoffman, AL Breen, CM Iversen
Remote Sensing 11 (1), 69, 2019
512019
Mapping Arctic plant functional type distributions in the Barrow Environmental Observatory using WorldView-2 and LiDAR datasets
Z Langford, J Kumar, FM Hoffman, RJ Norby, SD Wullschleger, VL Sloan, ...
Remote Sensing 8 (9), 733, 2016
432016
Wildfire mapping in Interior Alaska using deep neural networks on imbalanced datasets
Z Langford, J Kumar, F Hoffman
2018 IEEE International Conference on Data Mining Workshops (ICDMW), 770-778, 2018
372018
Convolutional neural network approach for mapping arctic vegetation using multi-sensor remote sensing fusion
ZL Langford, J Kumar, FM Hoffman
2017 IEEE International Conference on Data Mining Workshops (ICDMW), 322-331, 2017
212017
Robust signal classification using siamese networks
Z Langford, L Eisenbeiser, M Vondal
Proceedings of the ACM workshop on wireless security and machine learning, 1-5, 2019
202019
Spatiotemporal dynamics of wetted soils across a polar desert landscape
ZL Langford, MN Gooseff, DJ Lampkin
Antarctic Science 27 (2), 197-209, 2015
122015
Oak Ridge National Laboratory (DOE)
Z Langford
United States, 0
3
Wildfires identification: Semantic segmentation using support vector machine classifier
M Pecha, Z Langford, D Horák, R Tran Mills
Programs and Algorithms of Numerical Mathematics, 173-186, 2023
12023
Remote sensing-based characterization, 2-m, Plant Functional Type Distributions, Barrow Environmental Observatory, 2010
Z Langford, J Kumar, F Hoffman
Next Generation Ecosystems Experiment-Arctic, Oak Ridge National Laboratory …, 2016
12016
Distributed and GPU-Enabled Machine-Learning Approaches for Wildfire Identification from Remote Sensing Data
RT Mills, M Pecha, ZL Langford, J Kumar, D Horák
AGU23, 2023
2023
Scalable Wildfire Classification Using Distributed Memory Parallel, GPU-enabled Support Vector Machines
RT Mills, ZL Langford, M Pecha, J Kumar, FM Hoffman, D Horák
AGU Fall Meeting Abstracts 2022, NH43A-08, 2022
2022
Leveraging gradient weighted class activation mapping to improve classification effectiveness: Case study in transportation infrastructure characterization
TP Karnowski, D Aykac, RK Ferrell, C Gambrell, Z Langford, L Torkelson
Electronic Imaging 34, 1-6, 2022
2022
Acknowledgment to Reviewers of Journal of Imaging in 2021
A Ouahabi, A Barnes, A Pouliakis, A Pakula, A Bhowmik, A Förschler, ...
2022
Wildfire Classification using PETSc-based Support Vector Machines on Distributed-Memory GPU-based Parallel Computers
R Mills, Z Langford, J Kumar, F Hoffman
AGU Fall Meeting Abstracts 2021, IN11C-04, 2021
2021
Computationally Tractable High-Fidelity Representation of Global Hydrology in ESMs via Machine Learning Approaches to Scale-Bridging
RT Mills, FM Hoffman, J Kumar, R Jacob, Z Langford, S Sreepathi, ...
Artificial Intelligence for Earth System Predictability (AI4ESP …, 2021
2021
Exploiting Artificial Intelligence for Advancing Earth and Environmental System Science
FM Hoffman, J Kumar, ZL Langford, VS Konduri, N Collier
AGU Fall Meeting 2019, 2019
2019
Deep Transfer Learning With Field-Based Measurements for Large Area Classification
ZL Langford, J Kumar, FM Hoffman
2019 International Conference on Data Mining Workshops (ICDMW), 262-269, 2019
2019
Remote Sensing-Based, 5-m, Vegetation Distributions, Kougarok Study Site, Seward Peninsula, Alaska, ca. 2009-2016
Z Langford, J Kumar, F Hoffman, C Iversen, A Breen
Next Generation Ecosystems Experiment-Arctic, Oak Ridge National Laboratory …, 2019
2019
Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion
ZL Langford, J Kumar, FM Hoffman
Workshop on Innovating the Geosciences, 2018
2018
Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion
J Kumar, Z Langford, F Hoffman
ICEI 2018: 10th International Conference on Ecological Informatics …, 2018
2018
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