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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 |
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
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 |
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
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 |
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
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 |
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
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 |
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
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)
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
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)
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
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)
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
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 |
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
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 |
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
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)
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
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 |
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
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)
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
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 |
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
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)
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
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 |
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
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)
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
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)
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
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
ES2Vec: Earth Science Metadata Keyword Assignment using Domain-Specific Word Embeddings
Muthukumaran Ramasubramanian;Hassan Muhammad;Iksha Gurung;Manil Maskey;Rahul Ramachandran
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
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
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
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 |
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
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)
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
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)
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
Year: 2019 | Conference Paper |
Applying Deep Learning to Hail Detection: A Case Study
Melinda Pullman;Iksha Gurung;Manil Maskey;Rahul Ramachandran;Sundar A. Christopher
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
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