The modern era of Earth observation (EO) is defined by an unprecedented data deluge, with public and commercial constellations capturing hundreds of millions of square kilometers daily. A primary driver of this volume is the PlanetScope constellation, which achieves a daily collection capacity of 300 million square kilometers, generating an annual data archive exceeding 10 petabytes. This immense scale renders traditional human-led imagery analysis physically impossible, even when localized to specific areas of interest. To overcome this challenge, Planet is pioneering advanced machine learning and artificial intelligence frameworks designed to automate information extraction at a global scale. The objective is to transition from a repository of images to a semantic, searchable model of the physical world. This paper details the current state of these efforts. We report on the technical progress and discuss the implications for real-time surveillance and global environmental research.
Beckers et al. (Wed,) studied this question.
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