NASA Aircraft, Sensor Technology, Aid in Texas Flood Recovery Efforts

In response to recent flooding near Kerrville, Texas, NASA deployed two aircraft to assist state and local authorities in ongoing recovery operations.

The aircraft are part of the response from NASA’s Disasters Response Coordination System, which is activated to support emergency response for the flooding and is working closely with the Texas Division of Emergency Management, the Federal Emergency Management Agency (FEMA), and the humanitarian groups Save the Children and GiveDirectly.

Persistent cloud-cover has made it difficult to obtain clear satellite imagery, so the Disasters Program coordinated with NASA’s Airborne Science Program at NASA’s Johnson Space Flight Center in Houston to conduct a series of flights to gather observations of the impacted regions. NASA is sharing these data directly with emergency response teams to inform their search and rescue efforts and aid decision-making and resource allocation.

The high-altitude WB-57 aircraft operated by NASA Johnson departed from Ellington Field on July 8 to conduct aerial surveys. The aircraft is equipped with the DyNAMITE (Day/Night Airborne Motion Imager for Terrestrial Environments) sensor.

The DyNAMITE sensor views the Guadalupe River  and several miles of the surrounding area, providing high-resolution imagery critical to assessing damage and supporting coordination of ground-based recovery efforts. This system enables real-time collection and analysis of data, enhancing situational awareness and accelerating emergency response times.

In addition, the agency’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) is flying out of NASA’s Armstrong Flight Research Center in Edwards, California, aboard a Gulfstream III. Managed by the agency’s Jet Propulsion Laboratory in Southern California, the UAVSAR team is planning to collect observations over the Guadalupe, San Gabriel, and Colorado river basins Wednesday, Thursday, and Friday. Because UAVSAR can penetrate vegetation to spot water that optical sensors are unable to detect, the team’s goal is to characterize the extent of flooding to help with understanding the amount of damage within communities.

Flights are being coordinated with FEMA, the Texas Division of Emergency Management, and local responders to ensure data is quickly delivered to those making decisions on the ground. Imagery collected will be sent to NASA’s Disaster Response Coordination System.

Additionally, the Disasters Program, which is part of NASA’s Earth Science Division, is working to produce maps and data to assess the location and severity of flooding in the region and damage to buildings and infrastructure. These data are being shared on the NASA Disasters Mapping Portal as they become available.

-end-

Liz Vlock/Aries Keck
Headquarters, Washington
202-358-1600
elizabeth.a.vlock@nasa.gov / aries.keck@nasa.gov

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Smarter Searching: NASA AI Makes Science Data Easier to Find

Imagine shopping for a new pair of running shoes online. If each seller described them differently—one calling them “sneakers,” another “trainers,” and someone else “footwear for exercise”—you’d quickly feel lost in a sea of mismatched terminology. Fortunately, most online stores use standardized categories and filters, so you can click through a simple path: Women’s > Shoes > Running Shoes—and quickly find what you need.

Now, scale that problem to scientific research. Instead of sneakers, think “aerosol optical depth” or “sea surface temperature.” Instead of a handful of retailers, it is thousands of researchers, instruments, and data providers. Without a common language for describing data, finding relevant Earth science datasets would be like trying to locate a needle in a haystack, blindfolded.

That’s why NASA created the Global Change Master Directory (GCMD), a standardized vocabulary that helps scientists tag their datasets in a consistent and searchable way. But as science evolves, so does the challenge of keeping metadata organized and discoverable. 

To meet that challenge, NASA’s Office of Data Science and Informatics (ODSI) at the agency’s Marshall Space Flight Center (MSFC) in Huntsville, Alabama, developed the GCMD Keyword Recommender (GKR): a smart tool designed to help data providers and curators assign the right keywords, automatically.

Smarter Tagging, Accelerated Discovery

The upgraded GKR model isn’t just a technical improvement; it’s a leap forward in how we organize and access scientific knowledge. By automatically recommending precise, standardized keywords, the model reduces the burden on human curators while ensuring metadata quality remains high. This makes it easier for researchers, students, and the public to find exactly the datasets they need.

It also sets the stage for broader applications. The techniques used in GKR, like applying focal loss to rare-label classification problems and adapting pre-trained transformers to specialized domains, can benefit fields well beyond Earth science.

Metadata Matchmaker

The newly upgraded GKR model tackles a massive challenge in information science known as extreme multi-label classification. That’s a mouthful, but the concept is straightforward: Instead of predicting just one label, the model must choose many, sometimes dozens, from a set of thousands. Each dataset may need to be tagged with multiple, nuanced descriptors pulled from a controlled vocabulary.

Think of it like trying to identify all the animals in a photograph. If there’s just a dog, it’s easy. But if there’s a dog, a bird, a raccoon hiding behind a bush, and a unicorn that only shows up in 0.1% of your training photos, the task becomes far more difficult. That’s what GKR is up against: tagging complex datasets with precision, even when examples of some keywords are scarce.

And the problem is only growing. The new version of GKR now considers more than 3,200 keywords, up from about 430 in its earlier iteration. That’s a sevenfold increase in vocabulary complexity, and a major leap in what the model needs to learn and predict.

To handle this scale, the GKR team didn’t just add more data; they built a more capable model from the ground up. At the heart of the upgrade is INDUS, an advanced language model trained on a staggering 66 billion words drawn from scientific literature across disciplines—Earth science, biological sciences, astronomy, and more.

“We’re at the frontier of cutting-edge artificial intelligence and machine learning for science,” said Sajil Awale, a member of the NASA ODSI AI team at MSFC. “This problem domain is interesting, and challenging, because it’s an extreme classification problem where the model needs to differentiate even very similar keywords/tags based on small variations of context. It’s exciting to see how we have leveraged INDUS to build this GKR model because it is designed and trained for scientific domains. There are opportunities to improve INDUS for future uses.”

This means that the new GKR isn’t just guessing based on word similarities; it understands the context in which keywords appear. It’s the difference between a model knowing that “precipitation” might relate to weather versus recognizing when it means a climate variable in satellite data.

And while the older model was trained on only 2,000 metadata records, the new version had access to a much richer dataset of more than 43,000 records from NASA’s Common Metadata Repository. That increased exposure helps the model make more accurate predictions.

The Common Metadata Repository is the backend behind the following data search and discovery services:

• Earthdata Search
• International Data Network

Learning to Love Rare Words

One of the biggest hurdles in a task like this is class imbalance. Some keywords appear frequently; others might show up just a handful of times. Traditional machine learning approaches, like cross-entropy loss, which was used initially to train the model, tend to favor the easy, common labels, and neglect the rare ones.

To solve this, NASA’s team turned to focal loss, a strategy that reduces the model’s attention to obvious examples and shifts focus toward the harder, underrepresented cases. 

The result? A model that performs better across the board, especially on the keywords that matter most to specialists searching for niche datasets.

From Metadata to Mission

Ultimately, science depends not only on collecting data, but on making that data usable and discoverable. The updated GKR tool is a quiet but critical part of that mission. By bringing powerful AI to the task of metadata tagging, it helps ensure that the flood of Earth observation data pouring in from satellites and instruments around the globe doesn’t get lost in translation.

In a world awash with data, tools like GKR help researchers find the signal in the noise and turn information into insight.

Beyond powering GKR, the INDUS large language model is also enabling innovation across other NASA SMD projects. For example, INDUS supports the Science Discovery Engine by helping automate metadata curation and improving the relevancy ranking of search results.The diverse applications reflect INDUS’s growing role as a foundational AI capability for SMD.

The INDUS large language model is funded by the Office of the Chief Science Data Officer within NASA’s Science Mission Directorate at NASA Headquarters in Washington. The Office of the Chief Science Data Officer advances scientific discovery through innovative applications and partnerships in data science, advanced analytics, and artificial intelligence.

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