Yucong Wang - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Yucong Wang

Publications

9

Citations

117

Publications by Year

20212024

Co-Authors:

Panaetov AlexanderAditya AroraMustafa AyazoğluQixuan CaiMinjie Cai

Show All Co-Authors (303)

Yucong Wang

Affiliation

Department of Automation

Tsinghua University

Beijing, China

Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China

Publication Topics

Base Station,
Bit Error Rate,
Constant False Alarm Rate,
Uniform Linear Array,
Angle Estimation,
Attention Block,
Beam Direction,
Beamforming Design,
Channel Estimation,
Cluttered Environments,
Communication Performance,
Convolutional Layers

Biography

Yucong Wang (Graduate Student Member, IEEE) received the B.Eng. degree from Xidian University, Xi’an, China, in 2021. He is currently pursuing the Ph.D. degree with the Department of Automation, Tsinghua University, Beijing, China.,His research interests include ultra-wideband communications, THz communication systems, radar systems, and ISAC.(Based on document published on 25 March 2024).

Publications

9

Citations

117

Publications by Year

20212024

Co-Authors:

Panaetov Alexander
Aditya Arora
Mustafa Ayazoğlu
Qixuan Cai
Minjie Cai

Show All Co-Authors (303)

Author's Published Works

Search History

Showing 1-25 of 60 results

Conferences (49)

Journals (6)

Magazines (5)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Rahul Ramachandran(54)
Manil Maskey(25)
Iksha Gurung(15)
Muthukumaran Ramasubramanian(10)
Jia Zhang(9)
NASA Marshall Space Flight Center(13)
University of Alabama in Huntsville(10)
Information Technology and Systems Center, University of Alabama Huntsville, Huntsville, AL, USA(7)
Development Seed(4)
NASA/MSFC, USA(3)
IEEE Geoscience and Remote Sensing Magazine(5)
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium(4)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(3)
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium(3)
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium(3)
IEEE(60)
Waikoloa, HI, USA(4)
Kuala Lumpur, Malaysia(3)
Pasadena, CA, USA(3)
Valencia, Spain(3)
Yokohama, Japan(3)
Earth Science(22)
Convolutional Neural Network(11)
Machine Learning(10)
Neural Network(9)
Deep Learning(8)

Select All on Page

Sort By

Results

Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

Paridhi Parajuli;Rajat Shinde;Iksha Gurung;Manil Maskey;Rahul Ramachandran

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium

Year: 2024 | Conference Paper |

HTML

An equitable and environmentally just community is essential in order to avoid disproportionate burden borne by vulnerable communities. This need becomes pressing in the aftermath of an extreme event such as disaster or hazard when it is difficult for the governing bodies to implement resource allocation as per the need. Artificial Intelligence (AI) algorithms can help surface Equity and Environme...Show More

Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

Paridhi Parajuli;Rajat Shinde;Iksha Gurung;Manil Maskey;Rahul Ramachandran

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium

Year: 2024 | Conference Paper |

Enabling Dynamic Data Governance in Science: Design, Implementation, and Future Directions of the Modern Data Governance Framework

Kaylin Bugbee;Rahul Ramachandran;Aaron Kaulfus;Jeanne Le Roux;Ge Peng;Deborah Smith;Iksha Gurung;Ashish Acharya;Jerika Christman

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium

Year: 2024 | Conference Paper |

HTML

As scientific data volumes exponentially grow, dynamic, flexible and open approaches to data governance are needed. In this paper, we describe our efforts to build an open, scientific Modern Data Governance Framework (mDGF) that streamlines and makes actionable data governance requirements for projects and data providers. We present the goals and design of the mDGF. We also share our envisioned us...Show More

Enabling Dynamic Data Governance in Science: Design, Implementation, and Future Directions of the Modern Data Governance Framework

Kaylin Bugbee;Rahul Ramachandran;Aaron Kaulfus;Jeanne Le Roux;Ge Peng;Deborah Smith;Iksha Gurung;Ashish Acharya;Jerika Christman

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium

Year: 2024 | Conference Paper |

A Framework for Large Scale Semantic Similarity Search on Satellite Imagery

Muthukumaran Ramasubramanian;Iksha Gurung;Leo Thomas;Kathryn Berger;Soumya Ranjan;Heidi Mok;Sowmya Subramanian;Vitor George;Manil Maskey;Rahul Ramachandran

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

HTML

Searching for Earth Science phenomena in large archives of Earth Observation Satellite Imagery data requires elaborate processing and spatio-temporal indexing of the images into categories of the said phenomena. Manual tagging is laborious as it needs constant monitoring through vast volumes of satellite data, the volume and velocity of which is ever-increasing. A complete re-indexing is also need...Show More

A Framework for Large Scale Semantic Similarity Search on Satellite Imagery

Muthukumaran Ramasubramanian;Iksha Gurung;Leo Thomas;Kathryn Berger;Soumya Ranjan;Heidi Mok;Sowmya Subramanian;Vitor George;Manil Maskey;Rahul Ramachandran

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

Generative Framework Approach to Match Landsat and Sentinel-2 Data

Sujit Roy;Madhu Sridhar;John Mandel;Brian Freitag;Junchang Ju;Rahul Ramachandran

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

HTML

Linear regression and histogram matching-based techniques have been widely used to minimize the surface reflectance difference between two similar satellite observations such as Landsat-8/9 and Sentinel-2A/B products. However, regionally or globally derived conversion factors may not be suitable for all land cover types and locations, resulting in noticeable residual differences between the sensor...Show More

Generative Framework Approach to Match Landsat and Sentinel-2 Data

Sujit Roy;Madhu Sridhar;John Mandel;Brian Freitag;Junchang Ju;Rahul Ramachandran

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

Observing Supraglacial Lakes Using Deep Learning and Planetscope Imagery

Lillianne Thomas;Slesa Adhikari;Iksha Gurung;Aaron Kaulfus;Muthukumaran Ramasubramanian;Manil Maskey;Rahul Ramachandran

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

HTML

Supraglacial lakes (SGL)s result from melt water accumulation in topographic depressions on the surface of glaciers. SGLs primarily affect glacial dynamics through a positive feedback loop in which the albedo-lowering effect of SGLs can escalate surface melt leading to increases in lake extent and depth, amplifying the aforementioned albedo-lowering effect. The implications of accelerated glacial ...Show More

Observing Supraglacial Lakes Using Deep Learning and Planetscope Imagery

Lillianne Thomas;Slesa Adhikari;Iksha Gurung;Aaron Kaulfus;Muthukumaran Ramasubramanian;Manil Maskey;Rahul Ramachandran

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

Blaze: A High-Performance, Scalable, and Efficient Data Transfer Framework with Configurable and Extensible Features : Principles, Implementation, and Evaluation of a Transatlantic Inter-Cloud Data Transfer Case Study

Suresh Marru;Brian Freitag;Dimuthu Wannipurage;Uday Kumar Bommala;Patrick Pradier;Christophe Demange;Nishan Pantha;Tathagata Mukherjee;Betlem Rosich;Eric Monjoux;Rahul Ramachandran

2023 IEEE 16th International Conference on Cloud Computing (CLOUD)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

HTML

Blaze is a high-speed data transfer framework that enables efficient and scalable data movement between distributed storage systems. In this paper, we describe the design, implementation, and evaluation of Blaze in the context of a case study involving the transfer of 5.6 petabytes of data from OVH cloud storage to an Amazon S3 bucket. We discuss the technical challenges and design choices that le...Show More

Blaze: A High-Performance, Scalable, and Efficient Data Transfer Framework with Configurable and Extensible Features : Principles, Implementation, and Evaluation of a Transatlantic Inter-Cloud Data Transfer Case Study

Suresh Marru;Brian Freitag;Dimuthu Wannipurage;Uday Kumar Bommala;Patrick Pradier;Christophe Demange;Nishan Pantha;Tathagata Mukherjee;Betlem Rosich;Eric Monjoux;Rahul Ramachandran

2023 IEEE 16th International Conference on Cloud Computing (CLOUD)

Year: 2023 | Conference Paper |

Goal-Driven Context-Aware Service Recommendation for Mashup Development

Xihao Xie;Jia Zhang;Rahul Ramachandran;Tsengdar J. Lee;Seungwon Lee

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

HTML

As service-oriented architecture becoming one prevalent technique to rapidly compose functionalities to customers, increasingly more reusable software components have been published online in the form of web services. To create a mashup, however, it gets not only time-consuming but also error-prone for developers to find suitable services components from such a sea of services. Service discovery a...Show More

Goal-Driven Context-Aware Service Recommendation for Mashup Development

Xihao Xie;Jia Zhang;Rahul Ramachandran;Tsengdar J. Lee;Seungwon Lee

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

Year: 2022 | Conference Paper |

Selecting Approaches for Enabling Enterprise Data Search: NASA's Science Mission Directorate (SMD) Catalog

Kaylin Bugbee;Rahul Ramachandran;Ashish Acharya;Dai-Hai Ton That;John Hedman;Ahmed Eleish;Charles Driessnack;Wesley Adams;Emily Foshee

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Year: 2022 | Conference Paper |

Cited by: Papers (2)

HTML

NASA's Science Mission Directorate (SMD) is working to build an open-source science infrastructure to accelerate open, collaborative and interdisciplinary science. One key component in the open-source science infrastructure is the SMD data catalog. In this paper, we present our process for selecting a technical approach to building a NASA SMD enterprise-wide integrated search capability for scienc...Show More

Selecting Approaches for Enabling Enterprise Data Search: NASA's Science Mission Directorate (SMD) Catalog

Kaylin Bugbee;Rahul Ramachandran;Ashish Acharya;Dai-Hai Ton That;John Hedman;Ahmed Eleish;Charles Driessnack;Wesley Adams;Emily Foshee

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Year: 2022 | Conference Paper |

Language Model for Earth Science: Exploring Potential Downstream Applications as well as Current Challenges

R. Ramachandran;M. Ramasubramanian;P. Koirala;I. Gurung;M. Maskey

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Year: 2022 | Conference Paper |

HTML

The use of deep learning techniques to build transformer language models such as SciBERT and GPT3 have transformed the natural language technology (NLT) landscape. These new NLTs are being used in speech to text and vice versa, automated text classification, sentiment analysis, topic modeling, text summarization, and cognitive assistants. While Earth science has no shortage of unstructured data su...Show More

Language Model for Earth Science: Exploring Potential Downstream Applications as well as Current Challenges

R. Ramachandran;M. Ramasubramanian;P. Koirala;I. Gurung;M. Maskey

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Year: 2022 | Conference Paper |

Artificial Intelligence Vis-à-Vis Data Systems

Manil Maskey;Rahul Ramachandran;Iksha Gurung;Muthukumaran Ramasubramanian;Anirudh Koul

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Year: 2022 | Conference Paper |

HTML

NASA Earth Science Data Systems (ESDS) program manages a full lifecycle of data collected by all Earth science missions. ESDS also develops capabilities optimized to support rigorous science investigations. As technology landscapes evolve, ESDS has also evolved to transform its internal services and enhance external user centric services. This paper describes how ESDS is (i) adopting artificial in...Show More

Artificial Intelligence Vis-à-Vis Data Systems

Manil Maskey;Rahul Ramachandran;Iksha Gurung;Muthukumaran Ramasubramanian;Anirudh Koul

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Year: 2022 | Conference Paper |

Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition

Xihao Xie;Jia Zhang;Rahul Ramachandran;Tsengdar J. Lee;Seungwon Lee

2022 IEEE/ACIS 22nd International Conference on Computer and Information Science (ICIS)

Year: 2022 | Conference Paper |

Cited by: Papers (3)

HTML

As increasingly more software services have been published onto the Internet, it becomes critical yet highly challenging to recommend suitable services to facilitate scientific workflow composition. This paper proposes a novel Natural Language Processing (NLP)-inspired approach to recommending services throughout a workflow development process, based on incrementally learning latent service repres...Show More

Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition

Xihao Xie;Jia Zhang;Rahul Ramachandran;Tsengdar J. Lee;Seungwon Lee

2022 IEEE/ACIS 22nd International Conference on Computer and Information Science (ICIS)

Year: 2022 | Conference Paper |

Graph Convolutional Network-Strengthened Topic Modeling for Scientific Papers

Jia Zhang;Junhao Shen;Beichen Hu;Rahul Ramachandran;Tsengdar J. Lee;Kwo-Sen Kuo;Manil Maskey;Seungwon Lee

2021 IEEE International Conference on Smart Data Services (SMDS)

Year: 2021 | Conference Paper |

HTML

Machine learning has been woven into statistics to modernize topic modeling over textual documents written in natural language, and scientific paper search and recommendation can consequently offer higher accuracy instead of counting on traditional keyword-based search. However, topic distribution of a paper resulted from existing topic modeling techniques only relies on the statistics of words co...Show More

Graph Convolutional Network-Strengthened Topic Modeling for Scientific Papers

Jia Zhang;Junhao Shen;Beichen Hu;Rahul Ramachandran;Tsengdar J. Lee;Kwo-Sen Kuo;Manil Maskey;Seungwon Lee

2021 IEEE International Conference on Smart Data Services (SMDS)

Year: 2021 | Conference Paper |

Augmenting Data Systems with Prediction based Embeddings

Rahul Ramachandran;M. Ramasubramanian;Iksha Gurung;Carson Davis;Derek Koehl;Manil Maskey;Tsengdar Lee

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

Year: 2021 | Conference Paper |

Cited by: Papers (1)

HTML

One of the challenges of improving the search and use of complex Earth science data is designing and incorporating semantic components in existing Earth science data systems. Many projects have addressed this by using a knowledge engineering approach. However, using ontologies has inherent limitations as a practical and scalable approach. Data-driven strategies based on natural language processing...Show More

Augmenting Data Systems with Prediction based Embeddings

Rahul Ramachandran;M. Ramasubramanian;Iksha Gurung;Carson Davis;Derek Koehl;Manil Maskey;Tsengdar Lee

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

Year: 2021 | Conference Paper |

Day time and Nighttime Dust Event Segmentation using Deep Convolutional Neural Networks

Muthukumaran Ramasubramanian;Iksha Gurung;Talha Khan;Aaron Kaulfus;Manil Maskey;Rahul Ramachandran;Nicholas Elmer;Emily Berndt

SoutheastCon 2021

Year: 2021 | Conference Paper |

HTML

Airborne dust is known to have detrimental effects on human health, the environment, and aviation. Earth-observing satellites have been used to monitor dust events using visible and infrared bands, but the presence of clouds and smoke makes it a difficult phenomenon to identify. Moreover, nighttime dust has similar radioactive properties to that of cooler underlying surfaces. We propose a dust det...Show More

Day time and Nighttime Dust Event Segmentation using Deep Convolutional Neural Networks

Muthukumaran Ramasubramanian;Iksha Gurung;Talha Khan;Aaron Kaulfus;Manil Maskey;Rahul Ramachandran;Nicholas Elmer;Emily Berndt

SoutheastCon 2021

Year: 2021 | Conference Paper |

A Quantitative Analysis on the Use of Supervised Machine Learning in Earth Science

Katrina Virts;Ashlyn Shirey;George Priftis;Kumar Ankur;Muthukumaran Ramasubramanian;Hassan Muhammad;Ashish Acharya;Rahul Ramachandran

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

Cited by: Papers (7)

HTML

Recent review papers have discussed the opportunities and challenges of applying machine learning (ML) techniques to Earth science data. A common challenge cited in these papers is the lack of labeled training data. A literature review of Earth science papers over the last 10 years demonstrates that while there is rapid adoption of ML, particularly in biogeoscience and land surface research, the t...Show More

A Quantitative Analysis on the Use of Supervised Machine Learning in Earth Science

Katrina Virts;Ashlyn Shirey;George Priftis;Kumar Ankur;Muthukumaran Ramasubramanian;Hassan Muhammad;Ashish Acharya;Rahul Ramachandran

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

Standardized Algorithm Documentation for Improved Scientific Data Understanding: The Algorithm Publication Tool Prototype

Kaylin Bugbee;Aaron Kaulfus;Alyssa Harris;Sean Bailey;Rahul Ramachandran;Sean Harkins;Aimee Barciauskas;Deborah Smith

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

HTML

Algorithm Theoretical Basis Documents (ATBDs) are documents which accompany Earth observation data products generated from algorithms. While ATBDs are essential to scientific reproducibility, these key documents are not standardized and are often difficult to find. In this paper, we present the prototype Algorithm Publication Tool (APT), a cloud-based ATBD authoring and editing tool for NASA's Ear...Show More

Standardized Algorithm Documentation for Improved Scientific Data Understanding: The Algorithm Publication Tool Prototype

Kaylin Bugbee;Aaron Kaulfus;Alyssa Harris;Sean Bailey;Rahul Ramachandran;Sean Harkins;Aimee Barciauskas;Deborah Smith

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

Advancing Open Science Through Innovative Data System Solutions: The Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)'s Data Ecosystem

Kaylin Bugbee;Rahul Ramachandran;Manil Maskey;Aimee Barciauskas;Aaron Kaulfus;Dai-Hai Ton That;Katrina Virts;Kel Markert;Chris Lynnes

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

Cited by: Papers (5)

HTML

Collaborative open science practices are changing the way research is conducted. These changes affect how scientists work together on data, code and information. Data systems enhance open science by offering forward thinking technological solutions, such as providing data and computation on the cloud, to enable collaboration, sharing and analysis. In this paper, we present our vision for a concept...Show More

Advancing Open Science Through Innovative Data System Solutions: The Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)'s Data Ecosystem

Kaylin Bugbee;Rahul Ramachandran;Manil Maskey;Aimee Barciauskas;Aaron Kaulfus;Dai-Hai Ton That;Katrina Virts;Kel Markert;Chris Lynnes

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

EMPLOYING DEEP LEARNING TO ENABLE VISUAL EXPLORATION OF EARTH SCIENCE EVENTS

Manil Maskey;Rahul Ramachandran;Iksha Gurung;Muthukumaran Ramasubramanian;Brian Freitag;Aaron Kaulfus;Georgios Priftis;Drew Bollinger;Ricardo Mestre;Daniel da Silva

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

Cited by: Papers (1)

HTML

Earth science data archives have significantly increased in size due to the number of advanced sensors and science missions. In the meantime, Earth science data systems have not taken advantage of data driven technologies to provide advanced search capabilities. This paper discusses a machine learning-based approach, an enabling data driven technology, to detect Earth science events from image arc...Show More

EMPLOYING DEEP LEARNING TO ENABLE VISUAL EXPLORATION OF EARTH SCIENCE EVENTS

Manil Maskey;Rahul Ramachandran;Iksha Gurung;Muthukumaran Ramasubramanian;Brian Freitag;Aaron Kaulfus;Georgios Priftis;Drew Bollinger;Ricardo Mestre;Daniel da Silva

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

Analogy-based Assessment of Domain-specific Word Embeddings

Derek Koehl;Carson Davis;Udaysankar Nair;Rahul Ramachandran

2020 SoutheastCon

Year: 2020 | Conference Paper |

Cited by: Papers (2)

HTML

The ability of word embeddings to identify shared semantic regularities between word pair categories such as capital-country has led to the use of analogies as a method of validating word embedding models. Further research has shown that relative to the complete breadth of possible analogy categories, there exists a limit to the particular categories accessible, in terms of accuracy, to current an...Show More

Analogy-based Assessment of Domain-specific Word Embeddings

Derek Koehl;Carson Davis;Udaysankar Nair;Rahul Ramachandran

2020 SoutheastCon

Year: 2020 | Conference Paper |

ES2Vec: Earth Science Metadata Keyword Assignment using Domain-Specific Word Embeddings

Muthukumaran Ramasubramanian;Hassan Muhammad;Iksha Gurung;Manil Maskey;Rahul Ramachandran

2020 SoutheastCon

Year: 2020 | Conference Paper |

Cited by: Papers (3)

HTML

Earth science metadata keyword assignment is a challenging problem. Dataset curators select appropriate keywords from the Global Change Master Directory (GCMD) set of keywords. The keywords are integral part of search and discovery of these datasets. Hence, selection of keywords are crucial in increasing the discoverability of datasets. Utilizing machine learning techniques, we provide users with ...Show More

ES2Vec: Earth Science Metadata Keyword Assignment using Domain-Specific Word Embeddings

Muthukumaran Ramasubramanian;Hassan Muhammad;Iksha Gurung;Manil Maskey;Rahul Ramachandran

2020 SoutheastCon

Year: 2020 | Conference Paper |

Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System

Manil Maskey;Rahul Ramachandran;Muthukumaran Ramasubramanian;Iksha Gurung;Brian Freitag;Aaron Kaulfus;Drew Bollinger;Daniel J. Cecil;Jeffrey Miller

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2020 | Volume: 13 | Journal Article |

Cited by: Papers (48)

HTML

Tropical cyclones are one of the costliest natural disasters globally because of the wide range of associated hazards. Thus, an accurate diagnostic model for tropical cyclone intensity can save lives and property. There are a number of existing techniques and approaches that diagnose tropical cyclone wind speed using satellite data at a given time with varying success. This article presents a deep...Show More

Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System

Manil Maskey;Rahul Ramachandran;Muthukumaran Ramasubramanian;Iksha Gurung;Brian Freitag;Aaron Kaulfus;Drew Bollinger;Daniel J. Cecil;Jeffrey Miller

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2020 | Volume: 13 | Journal Article |

Alternative Datasets for Identification of Earth Science Events and Data

Kaylin Bugbee;Robert Griffin;Brian Freitag;Jeffrey Miller;Rahul Ramachandran;Jia Zhang

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Year: 2019 | Conference Paper |

HTML

Alternative, or non-traditional, data sources can be used to generate datasets which can in turn be analyzed for temporal, spatial and climatological patterns. Events and case studies inferred from the analysis of these patterns can be used by the remote sensing community to more effectively search for Earth observation data. In this paper, we present a new alternative Earth science dataset create...Show More

Alternative Datasets for Identification of Earth Science Events and Data

Kaylin Bugbee;Robert Griffin;Brian Freitag;Jeffrey Miller;Rahul Ramachandran;Jia Zhang

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Year: 2019 | Conference Paper |

Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment

Manil Maskey;Rahul Ramachandran;Iksha Gurung;Brian Freitag;J. J. Miller;Muthukumaran Ramasubramanian;Drew Bollinger;Ricardo Mestre;Daniel Cecil;Andrew Molthan;Chris Hain

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Year: 2019 | Conference Paper |

Cited by: Papers (6)

HTML

Enterprises are making machine learning for production as an integral part of their future roadmaps and Earth science domain is no exception. However, there is common problem in transitioning machine learning from science to production due to a major difference in constructing a model versus deploying it for people to use to make decisions. Phases of machine learning lifecycle that includes model ...Show More

Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment

Manil Maskey;Rahul Ramachandran;Iksha Gurung;Brian Freitag;J. J. Miller;Muthukumaran Ramasubramanian;Drew Bollinger;Ricardo Mestre;Daniel Cecil;Andrew Molthan;Chris Hain

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Year: 2019 | Conference Paper |

Building a Data Ecosystem: A New Data Stewardship Paradigm for the Multi-Mission Algorithm and Analysis Platform (MAAP)

Kaylin Bugbee;Manil Maskey;Aimee Barciauskas;Rahul Ramachandran;Aaron Kaulfus;Jeanné le Roux;Jeffrey Miller;Iksha Gurung;Amanda Whitehurst;Chris Lynnes

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Year: 2019 | Conference Paper |

Cited by: Papers (1)

HTML

New adaptive approaches to Earth observation data stewardship need to be adopted in order to allow for higher data volumes, heterogeneous data and constantly evolving technologies. The data ecosystem approach to stewardship offers a viable solution to this need by placing an emphasis on the relationships between data, technologies and people. In this paper, we present the Joint ESA-NASA Multi-Miss...Show More

Building a Data Ecosystem: A New Data Stewardship Paradigm for the Multi-Mission Algorithm and Analysis Platform (MAAP)

Kaylin Bugbee;Manil Maskey;Aimee Barciauskas;Rahul Ramachandran;Aaron Kaulfus;Jeanné le Roux;Jeffrey Miller;Iksha Gurung;Amanda Whitehurst;Chris Lynnes

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Year: 2019 | Conference Paper |

Applying Deep Learning to Hail Detection: A Case Study

Melinda Pullman;Iksha Gurung;Manil Maskey;Rahul Ramachandran;Sundar A. Christopher

IEEE Transactions on Geoscience and Remote Sensing

Year: 2019 | Volume: 57, Issue: 12 | Journal Article |

Cited by: Papers (9)

HTML

Deep learning is a subset of machine learning that uses deep neural networks (DNNs) capable of learning representations and extracting valuable information from vast data sets. Similarly, weather phenomena are often identified by patterns in data that serve as precursor signatures. Therefore, deep learning networks can be used to identify signatures of the weather phenomena, or possibly signatures...Show More

Applying Deep Learning to Hail Detection: A Case Study

Melinda Pullman;Iksha Gurung;Manil Maskey;Rahul Ramachandran;Sundar A. Christopher

IEEE Transactions on Geoscience and Remote Sensing

Year: 2019 | Volume: 57, Issue: 12 | Journal Article |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.