Is your feature request related to a problem? Please describe.
Recent news coverage of the Cottonwood Fire in Utah used NASA satellite before/after imagery to document the fire's impact. None of the articles credit MAAP, but the underlying fire-detection algorithm (FEDS) was tested and scaled on MAAP. DPS jobs produced the fire detection data, which was pushed to the API on VEDA — and that VEDA API is what powered the imagery/data used in the story. This is a real-world MAAP success story that isn't currently reflected anywhere on the MAAP website.
Describe the solution you'd like
Update the MAAP website (news/highlights/success-stories section, or equivalent) with a short writeup noting MAAP's role in this story: FEDS algorithm development/scaling on MAAP DPS, and the data pipeline feeding VEDA's API that the coverage relied on. Link out to the relevant news coverage.
Suggested draft content for the website:
MAAP Powers Fire Detection Behind Cottonwood Fire Coverage
In late June 2026, the Cottonwood Fire burned roughly 150 square miles northwest of Junction, Utah — one of the state's largest wildfires of the year, destroying 150+ structures including facilities at Eagle Point Ski Resort. NASA's Earth Observatory and multiple news outlets covered the fire's progression using before/after satellite imagery from Landsat 8/9 and VIIRS (NOAA-20, NOAA-21, Suomi NPP), showing the landscape transform from vibrant green (June 5) to charred black (June 29).
Behind the scenes, the fire detection data powering this coverage was produced using the FEDS (Fire Event Data Suite) algorithm, which was tested and scaled on the MAAP (Multi-Mission Algorithm and Analysis Platform). FEDS jobs run on MAAP's Data Processing System (DPS) generated the fire event data, which was then delivered through MAAP's API on VEDA — the same API that fed the imagery and data used in the public news coverage.
This is a strong example of MAAP's role as invisible infrastructure behind high-visibility, high-impact Earth science storytelling.
Coverage referencing the Cottonwood Fire:
Additional context
Per direct confirmation from the FEDS algorithm author (relayed by ticket requester, not independently verified by tooling): testing and scaling of the algorithm was done on MAAP; DPS jobs generated the fire data; that data was sent to MAAP's API on VEDA, and the VEDA API is what the news coverage pulled from. None of the public articles mention MAAP by name — this is an opportunity to claim credit on our own site. Fire facts sourced from the NASA Earth Observatory article for accuracy.
Is your feature request related to a problem? Please describe.
Recent news coverage of the Cottonwood Fire in Utah used NASA satellite before/after imagery to document the fire's impact. None of the articles credit MAAP, but the underlying fire-detection algorithm (FEDS) was tested and scaled on MAAP. DPS jobs produced the fire detection data, which was pushed to the API on VEDA — and that VEDA API is what powered the imagery/data used in the story. This is a real-world MAAP success story that isn't currently reflected anywhere on the MAAP website.
Describe the solution you'd like
Update the MAAP website (news/highlights/success-stories section, or equivalent) with a short writeup noting MAAP's role in this story: FEDS algorithm development/scaling on MAAP DPS, and the data pipeline feeding VEDA's API that the coverage relied on. Link out to the relevant news coverage.
Suggested draft content for the website:
Additional context
Per direct confirmation from the FEDS algorithm author (relayed by ticket requester, not independently verified by tooling): testing and scaling of the algorithm was done on MAAP; DPS jobs generated the fire data; that data was sent to MAAP's API on VEDA, and the VEDA API is what the news coverage pulled from. None of the public articles mention MAAP by name — this is an opportunity to claim credit on our own site. Fire facts sourced from the NASA Earth Observatory article for accuracy.