Explanipedia

Improving Label Error Detection and Elimination with Uncertainty Quantification Open

Johannes Jakubik, Michael Vössing, Manil Maskey, Christopher Wölfle, Gerhard Satzger · 2025

Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that a low self-confidence of models with respect to a certain l…

Finetuning AI Foundation Models to Develop Subgrid‐Scale Parameterizations: A Case Study on Atmospheric Gravity Waves Open

Aman Gupta, Aditi Sheshadri, Vishal Gaur, Wei Ji Leong, Manil Maskey , et al. · 2025

Global climate models parameterize a range of atmospheric‐oceanic processes, including gravity waves (GWs), clouds, moist convection, and turbulence, that cannot be sufficiently resolved. These subgrid‐scale closures for unresolved process…

Advancing Hurricane Forecasting With AI Models for Track and Intensity Prediction Open

Kumar Ankur, Sujit Roy, C. Phillips, U. S. Nair, Manil Maskey , et al. · 2025

Hurricane forecasting has traditionally relied on numerical weather prediction (NWP) models. However, advancements in artificial intelligence (AI) offer new opportunities to improve forecasting accuracy. This study presents a novel evaluat…

SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction Open

Sujit Roy, Dinesha V. Hegde, Johannes Schmude, Amy Lin, Vishal Gaur , et al. · 2025

This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weath…

Surya: Foundation Model for Heliophysics Open

Sujit Roy, Johannes Schmude, Rohit Lal, Vishal Gaur, Marcus Freitag , et al. · 2025

Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observatory (SDO), most models remain task-specific and constrained b…

Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications Open

Daniela Szwarcman, Sujit Roy, Paolo Fraccaro, Orsteinn Elí Gíslason, Benedikt Blumenstiel , et al. · 2025

WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks Open

Rajat C. Shinde, C. Phillips, Ankur Kumar, Aman Gupta, Simon Pfreundschuh , et al. · 2024

High-quality machine learning (ML)-ready datasets play a foundational role in developing new artificial intelligence (AI) models or fine-tuning existing models for scientific applications such as weather and climate analysis. Unfortunately…

Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications Open

Daniela Szwarcman, Sujit Roy, Paolo Fraccaro, Þorsteinn Elí Gíslason, Benedikt Blumenstiel , et al. · 2024

This technical report presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2M global time series samples from NASA's Harmonized Landsat and Senti…

Challenges in Guardrailing Large Language Models for Science Open

Nishan Pantha, Muthukumaran Ramasubramanian, Iksha Gurung, Manil Maskey, Rahul Ramachandran · 2024

The rapid development in large language models (LLMs) has transformed the landscape of natural language processing and understanding (NLP/NLU), offering significant benefits across various domains. However, when applied to scientific resea…

AI Foundation Model for Heliophysics: Applications, Design, and Implementation Open

Sujit Roy, Talwinder Singh, Marcus Freitag, Johannes Schmude, Rohit Lal , et al. · 2024

Deep learning-based methods have been widely researched in the areas of language and vision, demonstrating their capacity to understand long sequences of data and their usefulness in numerous helio-physics applications. Foundation models (…

Prithvi WxC: Foundation Model for Weather and Climate Open

Johannes Schmude, Sujit Roy, Will Trojak, Johannes Jakubik, Daniel Civitarese , et al. · 2024

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecas…

Croissant: A Metadata Format for ML-Ready Datasets Open

Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Pieter Gijsbers, Joan Giner-Miguelez , et al. · 2024

Presentation on Artificial Intelligence developments in the European Open Science Cloud (EOSC) took place at the EOSC Winter School 2025 in Seville, Spain. Recent advancements in semantic interoperability were highlighted, particularly in …

INDUS: Effective and Efficient Language Models for Scientific Applications Open

Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa , et al. · 2024

Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specia…

Improving Label Error Detection and Elimination with Uncertainty Quantification Open

Johannes Jakubik, Michael Vössing, Manil Maskey, Christopher Wölfle, Gerhard Satzger · 2024

Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that a low self-confidence of models with respect to a certain l…

NASA’s Open Science Platform VEDA (Visualization, Exploration and Data Analytics) Open

Zac Deziel, Aimee Barciauskas, Jonas Sølvsteen, Manil Maskey, Brian Freitag , et al. · 2024

VEDA is an open-source science cyberinfrastructure for data processing, visualization, exploration, and geographic information systems (GIS) capabilities (https://www.earthdata.nasa.gov/esds/veda, https://www.earthdata.nasa.gov/dashboard/)…

Foundation Models for Science: Potential, Challenges, and the Path Forward Open

Manil Maskey, Rahul Ramachandran, Tsengdar Lee, Kevin Murphy, Sujit Roy , et al. · 2024

Foundation models signify a significant shift in AI by creating large-scale machine learning models (FMs) pre-trained on wide-ranging datasets. These models act as flexible starting points, ready to be fine-tuned for various specialized ta…

The U.S. Greenhouse Gas Center: Extending Accessible and Integrated GHG Information from U.S. Government and Non-Public Sources to meet user needs Open

Shanna Combley, Argyro Kavvada, Lesley Ott, K. W. Bowman, Manil Maskey , et al. · 2024

The newly established United States Greenhouse Gas Center (U.S. GHG Center) is a multi-agency partnership between the National Aeronautics and Space Administration (NASA), the Environmental Protection Agency (EPA), the National Oceanic and…

High Performance and Disruptive Computing in Remote Sensing: The Third Edition of the School Organized by the HDCRS Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees] Open

Gabriele Cavallaro, Dora B. Heras, Manil Maskey · 2024

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Foundation Models for Generalist Geospatial Artificial Intelligence Open

Johannes Jakubik, Sujit Roy, Chris Phillips, Paolo Fraccaro, Bianca Zadrozny , et al. · 2024

Rethinking AI for Science: An Evolution From Data‐Driven to Data‐Centric Framework Open

Manil Maskey · 2023

The rapid advancements in Artificial Intelligence (AI) have found applications across a multitude of disciplines, including the scientific domain. Yet, the dominant focus on model‐centric AI often overlooks the critical role that data play…

DMLR: Data-centric Machine Learning Research -- Past, Present and Future Open

Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel , et al. · 2023

Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets tha…

Leveraging Citizen Science for Flood Extent Detection using Machine Learning Benchmark Dataset Open

Muthukumaran Ramasubramanian, Iksha Gurung, Shubhankar Gahlot, Ronny Hänsch, Andrew Molthan , et al. · 2023

Accurate detection of inundated water extents during flooding events is crucial in emergency response decisions and aids in recovery efforts. Satellite Remote Sensing data provides a global framework for detecting flooding extents. Specifi…

Foundation Models for Generalist Geospatial Artificial Intelligence Open

Johannes Jakubik, Sujit Roy, Chris Phillips, Paolo Fraccaro, Denys Godwin , et al. · 2023

Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote sensing. Foundation models are pre-trained on large unlabeled…

A Summer School Session on Mastering Geospatial Artificial Intelligence: From Data Production to Artificial Intelligence Foundation Model Development and Downstream Applications [Technical Committees] Open

Manil Maskey, Gabriele Cavallaro, Dora B. Heras, Paolo Fraccaro, Blair Edwards , et al. · 2023

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Tropical Cyclone Wind Speed Estimation: A Large Scale Training Data Set and Community Benchmarking Open

Iksha Gurung, Muthukumaran Ramasubhramanian, Brian Freitag, Aaron Kaulfus, Manil Maskey , et al. · 2023

Tropical cyclones (TCs) cause significant disruptions to infrastructure and livelihood. The scale of loss due to TCs may be mitigated by prompt and accurate advisories about TC wind speed. Current advisories are consensus based and have a …

Urban Growth of Houston, Texas over the Last Two Decades: Implications for Heat Stress Open

U. S. Nair, Andrew Blackford, Joey Bonucchi, Brian Freitag, Manil Maskey , et al. · 2023

<p>Houston is also one of the coastal cities in the US that has shown considerable growth in urban land cover over the last two decades. We utilized the National Land Cover Database derived from Landsat satellite imagery and coherent…

The Origins of NASA’s Visualization, Exploration and Data Analytics (VEDA) Platform: A platform for biomass research, NASA’s Earth Information System and the COVID-19 Dashboard Open

Aimee Barciauskas, Manil Maskey, D. J. Newman, Slesa Adhikari, Olaf Veerman , et al. · 2023

NASA's Visualization, Exploration, and Data Analysis (VEDA) project is an open-source science cyberinfrastructure for data processing, visualization, exploration, and geographic information systems (GIS) capabilities (https://www.earthdata…

Programmatic Update for NASA’s Commercial Smallsat Data Acquisition (CSDA) Program Open

Aaron Kaulfus, Alfreda Hall, Manil Maskey, Will McCarty, F. Policelli · 2023

Established in 2017 as a pilot project, the NASA Commercial Smallsat Data Acquisition (CSDA) Program evaluates and acquires commercial datasets that compliment NASA Earth Science research and application goals. The success of the pilot and…

Foundation AI Models for Science Open

Manil Maskey, Rahul Ramachandran, Tsengdar Lee, Raghu Ganti · 2023

Foundation Models (FM) are AI models that are designed to replace a task or an application specific model. These FM can be applied to many different downstream applications. These FM are trained using self supervised techniques and can be …

The future of NASA Earth Science in the commercial cloud: Challenges and opportunities Open

Alexey Shiklomanov, Manil Maskey, Yoseline Angel, Aimee Barciauskas, Philip G. Brodrick , et al. · 2023

NASA produces a large volume and variety of data products that are used every day to support research, decision making, and education. The widespread use of NASA’s Earth Science data is enabled by NASA’s Earth Science Data Syst…

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