Ways Satellite Technology Enables Public Sector Make Data-Driven Decisions

Overhead space systems–the phrase used to describe the numerous satellite systems operating in Earth's orbit–are a vital part of today's digital infrastructure.

Fremont, CA: There is no greater vantage point for capturing visual data activities on Earth than space. As a result, both corporations and government organizations significantly invest in space-based sensing technologies that claim to give an unprecedented amount of actionable data that can be stored, analyzed, and used for insight.

Overhead space systems–the phrase used to characterize the numerous satellite systems operating in Earth's orbit–are a vital part of today's digital infrastructure in both the public and private sectors. Sensor technology has advanced in recent years, as have the satellites that transport it into space. Thanks to advances in artificial intelligence and machine learning technologies, overhead space systems can record and convey visual data significantly more effectively than ever before.

Satellites around Earth now acquire the vast bulk of photos shot in space. In addition, these contemporary satellites are lighter, smaller, and less expensive than their predecessors, allowing them to carry significantly higher sophisticated payloads in far increasing quantities. They've also benefitted from similar cost and performance trends in the cameras they've used—compare the current smartphone camera with one from ten years ago—allowing today's satellites to collect exceptionally high-definition photographs and video.

The onboard hardware satellites aren't the only big technological advancement when it comes to space photography. Thanks to advances in AI and machine learning, today's satellites have digital intelligence to assess this new data.

Satellites effectively act as data vacuum cleaners, collecting petabytes of satellite photos every week. The camera's technology sifts and targets just the most valuable information pre-processed utilizing spectrum calibrations tuned to distinct commands, requiring AI-driven pre-sorting. In addition, the satellite uses machine learning to improve image selection to improve its ability to target the photographs that its user wants to collect. However, while the satellite's computer systems are excellent at acquiring and pre-processing photos, the limited volume does not lend itself to large-scale data storage or high-powered analytics.

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