Artificial intelligence and machine learning have had a profound influence on a wide range of areas and businesses, where they have paved the way for the automation and optimization of operations as well as the development of new business opportunities. However, due to quick advances, these technological innovations are being used in research and development outside of our atmosphere and into space. Now, let's take a quick look at how NASA uses AI and Machine Learning for various space projects and earth science. NASA is constantly progressing in AI applications for space research, such as automating image analysis for the galaxy, planet, and star classification, developing autonomous space probes that can avoid space junk without human involvement, by using AI-based radio technology to make communication networks more effective and disturbance-free. However, the creation of autonomous landers (robots) that wander the surface of other planets is one of NASA's most critical AI applications.

Semantic segmentation of land cover classes is fundamental for agricultural and economic development work, from sustainable forestry to urban planning, yet existing training datasets have significant limitations. To generate an open and comprehensive training library of high resolution Earth imagery and high quality land cover classifications, public Sentinel-2 data at 10 m spatial resolution was matched with accurate GlobeLand30 labels from 2010, which were filtered by agreement with an intermediary Sentinel-2 classification at 20 m produced during atmospheric correction. Scene-level classifications were predicted by Random Forests trained on valid reflectance data and the filtered labels, and achieved over 80% model accuracy for a variety of locations. Further work is required to aggregate individual scene classifications for annual labels and to test the approach in more locations, before crowdsourcing human validation. The goal is to create a sustained community-wide effort to generate image labels not only for land cover, but also very specific images for major agriculture crops across the world and other thematic categories of interest to the global development community.