satellite imagery


VANE Query Language: Intuitive Access to Satellite Imagery

Georgy Potapov of OpenWeatherMap introduces the release of the VANE language service, an entirely online service that presents a new concept for accessing satellite imagery. Potapov shares OpenWeatherMap’s vision of how developers can work with satellite and weather data to help the overall geoinformation market to grow.

Artistic concept of Landsat 9. Source: NASA.Artistic concept of Landsat 9. Source: NASA.

Landsat 9 Will Launch in 2020

The Landsat series of satellites has been imaging the Earth’s surface for nearly 50 years, providing vital imagery for a range of purposes from the natural sciences to civil administration and conflict monitoring. NASA and the USGS recently announced that the next iteration of the program, Landsat 9, is due to launch in 2020.

Estimates of per capita consumption in four African countries. Stanford researchers used machine learning to extract information from high-resolution satellite imagery to identify impoverished regions in Africa. (Image credit: Neal Jean et al.)Estimates of per capita consumption in four African countries. Stanford researchers used machine learning to extract information from high-resolution satellite imagery to identify impoverished regions in Africa. (Image credit: Neal Jean et al.)

Using Machine Learning to Map Poverty from Satellite Imagery

Satellite images are now being used to map poverty levels around the world using machine learning used to analyze specific poverty data using a convolutional neural network.

Sudden Landslide Identification Product (SLIP) developed by NASA detects landslide potential by analyzing satellite imagery for changes in soil moisture, muddiness, and other surface features. The Landsat 8 satellite capture the left and middle images on September 15, 2013, and September 18, 2014—before and after the Jure landslide in Nepal on August 2, 2014. The processed image on the right shows areas in red indicating a probable landslide and areas in yellow showing a possible landslide. Source: NASA.Sudden Landslide Identification Product (SLIP) developed by NASA detects landslide potential by analyzing satellite imagery for changes in soil moisture, muddiness, and other surface features. The Landsat 8 satellite capture the left and middle images on September 15, 2013, and September 18, 2014—before and after the Jure landslide in Nepal on August 2, 2014. The processed image on the right shows areas in red indicating a probable landslide and areas in yellow showing a possible landslide. Source: NASA.

Using Remote Sensing to Automate the Detection of Landslides

The Sudden Landslide Identification Product (SLIP) developed by NASA detects landslide potential by analyzing satellite imagery for changes in soil moisture, muddiness, and other surface features.