paper_2017.bib
@inproceedings{Langford_ICDM_2017,
author = {Z. L. Langford and J. Kumar and F. M. Hoffman},
booktitle = {2017 IEEE International Conference on Data Mining Workshops (ICDMW)},
title = {Convolutional Neural Network Approach for Mapping Arctic Vegetation Using Multi-Sensor Remote Sensing Fusion},
year = {2017},
volume = {},
number = {},
pages = {322-331},
keywords = {geophysical image processing;image classification;image fusion;image segmentation;neural nets;pattern clustering;sensor fusion;terrain mapping;vegetation;vegetation mapping;Alaska;Arctic ecosystems;Arctic vegetation mapping;CNN approaches;Mapcurves labeling;Next Generation Ecosystem Experiment;US Department of Energy;central Seward Peninsula;convolutional neural network approach;developed data products;existing Arctic vegetation maps;frequent cloud cover;high latitude environments;high resolution vegetation maps;high-resolution remote sensing datasets;hyperspectral-optical dataset fusion;k values;land-atmosphere interactions;multisensor data fusion approach;multisensor remote sensing fusion;public coarse resolution maps;size 343.72 km;terrestrial ecosystem processes;training map;unsupervised classification techniques;unsupervised clustering;validation study;vegetation class;vegetation classifications;Arctic;Artificial satellites;Biological system modeling;Earth;Meteorology;Remote sensing;Vegetation mapping;Fusion;Multi-Sensor;Remote Sensing;Vegetation Classificaiton},
doi = {10.1109/ICDMW.2017.48},
note = {\url{https://doi.org/10.1109/ICDMW.2017.48}},
issn = {},
month = {Nov}
}
@article{Song_JGR_2017,
author = {Song, Xia and Hoffman, Forrest M. and Iversen, Colleen M. and Yin, Yunhe and Kumar, Jitendra and Ma, Chun and Xu, Xiaofeng},
title = {Significant inconsistency of vegetation carbon density in CMIP5 Earth system models against observational data},
journal = {Journal of Geophysical Research: Biogeosciences},
volume = {122},
number = {9},
pages = {2282-2297},
keywords = {carbon density, root, vegetation, root/vegetation ratio, Earth system models},
doi = {10.1002/2017JG003914},
note = {\url{https://doi.org/10.1002/2017JG003914}},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017JG003914},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017JG003914},
year = {2017}
}
@inproceedings{Sreepathi_IEEECluster_2017,
title = {Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers},
author = {Sarat Sreepathi and Jitendra Kumar and Forrest Hoffman and Richard Mills and Vamsi Sripathi and William Hargrove},
booktitle = {2017 IEEE Cluster Conference},
year = {2017},
month = {Sep},
doi = {10.1109/CLUSTER.2017.88},
note = {\url{https://doi.org/10.1109/CLUSTER.2017.88}},
file = {pubs/Sreepathi_IEEECluster_2017.pdf}
}
@inproceedings{Devarakonda_IEEEBigData_2017,
author = {R. Devarakonda and M. Giansiracusa and J. Kumar and H. Shanafield},
booktitle = {2017 IEEE International Conference on Big Data (Big Data)},
title = {{Social media based NPL system to find and retrieve ARM data: Concept paper}},
year = {2017},
volume = {},
number = {},
pages = {4736-4737},
keywords = {SQL;application program interfaces;natural language interfaces;natural language processing;query processing;relational databases;social networking (online);ARM data retrieval;Apache Lucene Solr;Atmospheric Radiation Measurement Data Center;Cassandra DB;Kafka;NPL system;Natural Language Processing;OpenML;SQL language;SQL queries;Structured Query Language;Twitter API;automated query response system;database languages;database queries;information connectivity;information retrieval;informational databases;natural language interfaces;online information;pervasive source;public access;relational databases;social media;structured databases;Indexes;Monitoring;Natural language processing;Social network services;Structured Query Language;machine learning;natural language processing;social media interaction;stream pipelining},
doi = {10.1109/BigData.2017.8258525},
note = {\url{https://doi.org/10.1109/BigData.2017.8258525}},
issn = {},
month = {Dec}
}