Prati
Nicholas LaHaye
Nicholas LaHaye
Research Data Scientist / GeoAI Lead, Spatial Informatics Group, LLC.
Potvrđena adresa e-pošte na sig-gis.com
Naslov
Citirano
Citirano
Godina
Applications of supervised machine learning in autism spectrum disorder research: a review
KK Hyde, MN Novack, N LaHaye, C Parlett-Pelleriti, R Anden, DR Dixon, ...
Review Journal of Autism and Developmental Disorders 6, 128-146, 2019
2312019
Learning in the machine: To share or not to share?
J Ott, E Linstead, N LaHaye, P Baldi
Neural Networks 126, 235-249, 2020
252020
Multi-modal object tracking and image fusion with unsupervised deep learning
N LaHaye, J Ott, MJ Garay, HM El-Askary, E Linstead
IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2019
192019
Assessing the vertical displacement of the grand Ethiopian renaissance dam during its filling using DInSAR technology and its potential acute consequences on the downstream …
H El-Askary, A Fawzy, R Thomas, W Li, N LaHaye, E Linstead, T Piechota, ...
Remote Sensing 13 (21), 4287, 2021
142021
Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review. Rev J Autism Dev Disord 6, 128–146 (2019)
K Hyde, MN Novack, N LaHaye
5
A quantitative validation of multi-modal image fusion and segmentation for object detection and tracking
N LaHaye, MJ Garay, BD Bue, H El-Askary, E Linstead
Remote Sensing 13 (12), 2364, 2021
42021
Self-supervised contrastive learning for wildfire detection: utility and limitations
JW Choi, N LaHaye, Y Chen, H Lee, Y Gel
Advances in Machine Learning and Image Analysis for GeoAI 1, 153-163, 2024
22024
Scalable Hyperspectral Inversion with Uncertainty Quantification
L Hopkins, N LaHaye, J Kravitz, S Mauceri
AGU Fall Meeting Abstracts 2022, GC42D-0742, 2022
12022
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
12022
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
12022
Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets
N LaHaye, A Easley, K Yun, H Lee, E Linstead, MJ Garay, ...
arXiv preprint arXiv:2501.15343, 2025
2025
Multi-Platform / Multi-Sensor Fire and Smoke Segmentation from the FIREX-AQ 2019 Campaign Creators (dataset)
N LaHaye, A Easley, H Lee, K Yun, E Linstead, MJ Garay, ...
https://doi.org/10.5281/zenodo.14731718, 2025
2025
FIREX_AQ_2019_Fire_Smoke (model weights)
N LaHaye
https://huggingface.co/njlahaye/FIREX_AQ_2019_Fire_Smoke, 2025
2025
Precise Evaluation and Validation of Self-Supervised Segmentation of Wildland Fires and Smoke Plumes During FIREX-AQ 2019
A Easley, N LaHaye, H Lee, MJ Garay, K Yun, OV Kalashnikova
AGU24, 2024
2024
A Comparison of Model Complexity, Representative Capabilities, and Performance for Self-Supervised Multi-Sensor Wildfire and Smoke Segmentation and Tracking
N LaHaye, A Easley, H Lee, MJ Garay, K Yun, A Goodman, ...
AGU Fall Meeting 2024, 2024
2024
Precise Evaluation and Validation of Self-Supervised Segmentation of Wildland Fires During FIREX-AQ 2019
A Easley, N LaHaye, H Lee, MJ Garay, K Yun, OV Kalashnikova
AGU Fall Meeting 2024, 2024
2024
Toward Cloud Tomography from Passive Multi-anlge Imaging at a Global Scale by Combining Physics and Machine Learning
A Davis, L Forster, N LaHaye, S Mauceri, M Kurowski
AGU Fall Meeting 2024, 2024
2024
Towards practical Cloud Tomography: A Deep Learning Framework for Retrieval and Uncertainty Quantification of Cloud Optical Thickness from Multi-Angle Imaging
N LaHaye, L Forster, S Mauceri, M Kurowski, A Davis
AGU Fall Meeting, 2024
2024
Improved Segmentation of Palm Oil Farms and Deforestation Prevention using Self-Supervised Deep Learning
N Pinto, N LaHaye
1st Science Understanding through Data Science Conference, 2024
2024
FIRE-D: NASA-Centric Remote Sensing of Wildfire
Y Chen, N LaHaye, JW Choi, Z Zhen, P Davis, H Lee, M Parashar, Y Gel
1st Science Understanding through Data Science Conference, 2024
2024
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