From Orbit to A.I.—Harnessing Machine Learning with Landsat Data | NASA

From Orbit to A.I.—Harnessing Machine Learning with Landsat Data | NASA

Over the past few years, machine learning techniques have been increasingly used to analyze the vast amount of data collected by the Landsat mission, which has been circling the globe for over 50 years. The data has been used to classify different types of land cover, detect changes to landscapes over time, and map the impact of human activity on the environment. With the field constantly evolving, researchers are developing new deep learning models to improve the accuracy and efficiency of the analysis and extract even more information from the data. Here are just a few examples of how the combination of Landsat data and machine learning is providing a better understanding of our planet's past, present, and future.

The Landsat program conceived of in the 1960s, has been running longer than any remote sensing program. The idea was simple: position a satellite in a nearly polar orbit fixed to the solar angle so that each daytime pass would cross the equator at roughly the same local time.

Data representing bands of the spectrum are captured and processed into grayscale recordings that can be combined to create natural looking or false color views. Nine Landsat satellites have been launched in partnership with NASA since 1972.

Learn more about the USGS Landsat Program:

https://www.usgs.gov/landsat-missions

Credit: NASA's Goddard Space Flight Center (GSFC)/Scientific Visualization Studio

Chris Burns [KBRWyle]: Lead Producer & Lead Writer

Duration: 5 minutes, 34 seconds

Release Date: April 21, 2023

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