Rahul Ramachandran
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View article: Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability Open
Landslides cause severe damage to lives, infrastructure, and the environment, making accurate and timely mapping essential for disaster preparedness and response. However, conventional deep learning models often struggle when applied acros…
View article: Finetuning AI Foundation Models to Develop Subgrid‐Scale Parameterizations: A Case Study on Atmospheric Gravity Waves
Finetuning AI Foundation Models to Develop Subgrid‐Scale Parameterizations: A Case Study on Atmospheric Gravity Waves Open
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…
View article: Earth Action in Transition: Highlights from the 2025 ESA-NASA International Workshop on AI Foundation Models for EO
Earth Action in Transition: Highlights from the 2025 ESA-NASA International Workshop on AI Foundation Models for EO Open
View article: Advancing Hurricane Forecasting With AI Models for Track and Intensity Prediction
Advancing Hurricane Forecasting With AI Models for Track and Intensity Prediction Open
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…
View article: From Tape Reels to Global Access: A History and Future Vision of NASA's Scientific Data Management
From Tape Reels to Global Access: A History and Future Vision of NASA's Scientific Data Management Open
Since its creation in the late 1950s, NASA has collected space science data and information that span astrophysics, Earth science, planetary science, heliophysics, and biological and physical sciences. While these data were critical to NAS…
View article: Earth Action in Transition: Highlights from the 2025 ESA-NASA International Workshop on AI Foundation Models for EO
Earth Action in Transition: Highlights from the 2025 ESA-NASA International Workshop on AI Foundation Models for EO Open
View article: How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks
How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks Open
Multimodal foundation models, such as GPT-4o, have recently made remarkable progress, but it is not clear where exactly these models stand in terms of understanding vision. In this paper, we benchmark the performance of popular multimodal …
View article: Toward Open Earth Science as Fast and Accessible as Natural Language
Toward Open Earth Science as Fast and Accessible as Natural Language Open
Is natural-language-driven earth observation data analysis now feasible with the assistance of Large Language Models (LLMs)? For open science in service of public interest, feasibility requires reliably high accuracy, interactive latencies…
View article: TerraMind: Large-Scale Generative Multimodality for Earth Observation
TerraMind: Large-Scale Generative Multimodality for Earth Observation Open
We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO). Unlike other multimodal models, TerraMind is pretrained on dual-scale representations combining both token-level and pixel-level…
View article: Balancing Practical Uses and Ethical Concerns:The Role of Large Language Models in Scientific Research
Balancing Practical Uses and Ethical Concerns:The Role of Large Language Models in Scientific Research Open
The rapid adoption of artificial intelligence (AI) in scientific research is accelerating progress but also challenging core scientific norms such as accountability, transparency, and replicability. Large language models (LLMs) like ChatGP…
View article: Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications Open
View article: Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications Open
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…
View article: Challenges in Guardrailing Large Language Models for Science
Challenges in Guardrailing Large Language Models for Science Open
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…
View article: Leveraging Security Observability to Strengthen Security of Digital Ecosystem Architecture
Leveraging Security Observability to Strengthen Security of Digital Ecosystem Architecture Open
In the current fast-paced digital environment, enterprises are striving to offer a seamless and integrated customer experience across multiple touchpoints. This improved experience often leads to higher conversion rates and increased custo…
View article: AI Foundation Model for Heliophysics: Applications, Design, and Implementation
AI Foundation Model for Heliophysics: Applications, Design, and Implementation Open
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 (…
View article: On Evaluation of Vision Datasets and Models using Human Competency Frameworks
On Evaluation of Vision Datasets and Models using Human Competency Frameworks Open
Evaluating models and datasets in computer vision remains a challenging task, with most leaderboards relying solely on accuracy. While accuracy is a popular metric for model evaluation, it provides only a coarse assessment by considering a…
View article: Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation
Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation Open
Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs). Model representation of these processes and t…
View article: INDUS: Effective and Efficient Language Models for Scientific Applications
INDUS: Effective and Efficient Language Models for Scientific Applications Open
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…
View article: A Modular, Tendon Driven Variable Stiffness Manipulator with Internal Routing for Improved Stability and Increased Payload Capacity
A Modular, Tendon Driven Variable Stiffness Manipulator with Internal Routing for Improved Stability and Increased Payload Capacity Open
Stability and reliable operation under a spectrum of environmental conditions is still an open challenge for soft and continuum style manipulators. The inability to carry sufficient load and effectively reject external disturbances are two…
View article: Learning Service Selection Decision Making Behaviors During Scientific Workflow Development
Learning Service Selection Decision Making Behaviors During Scientific Workflow Development Open
Increasingly, more software services have been published onto the Internet, making it a big challenge to recommend services in the process of a scientific workflow composition. In this paper, a novel context-aware approach is proposed to r…
View article: Foundation Models for Science: Potential, Challenges, and the Path Forward
Foundation Models for Science: Potential, Challenges, and the Path Forward Open
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…
View article: NASA’s Satellite Needs Working Group Management Office: Developing Solutions in an Agile, Open Science Environment
NASA’s Satellite Needs Working Group Management Office: Developing Solutions in an Agile, Open Science Environment Open
Every two years, the National Aeronautics and Space Administration (NASA) leads an assessment of U.S. Federal civilian agency Earth observation needs submitted through the Satellite Needs Working Group (SNWG) survey. In four survey c…
View article: Foundation Models for Generalist Geospatial Artificial Intelligence
Foundation Models for Generalist Geospatial Artificial Intelligence Open
View article: Foundation Models for Generalist Geospatial Artificial Intelligence
Foundation Models for Generalist Geospatial Artificial Intelligence Open
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…
View article: AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation Open
Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. There has been increasing interest from technology companies, government insti…
View article: A Summer School Session on Mastering Geospatial Artificial Intelligence: From Data Production to Artificial Intelligence Foundation Model Development and Downstream Applications [Technical Committees]
A Summer School Session on Mastering Geospatial Artificial Intelligence: From Data Production to Artificial Intelligence Foundation Model Development and Downstream Applications [Technical Committees] Open
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View article: Tropical Cyclone Wind Speed Estimation: A Large Scale Training Data Set and Community Benchmarking
Tropical Cyclone Wind Speed Estimation: A Large Scale Training Data Set and Community Benchmarking Open
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 …
View article: NASA’s Science Discovery Engine: An Interdisciplinary, Open Science Data and Information Discovery Service
NASA’s Science Discovery Engine: An Interdisciplinary, Open Science Data and Information Discovery Service Open
NASA’s Science Plan includes a strategy to advance discovery by leveraging cross-disciplinary opportunities between scientific disciplines. In addition, NASA is committed to building an inclusive, open science community over the next…
View article: Modern Scientific Data Governance Framework
Modern Scientific Data Governance Framework Open
Science has entered the era of Big Data with new challenges related to data governance, stewardship, and management. The existing data governance practices must catch up to ensure proper data management. Existing data governance policies a…
View article: Foundation AI Models for Science
Foundation AI Models for Science Open
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 …