Bengaluru-based KaleidEO Space Systems has become the first Indian company to demonstrate the power of Edge computing in space, by successfully generating insights from an image taken by an orbiting satellite in real-time.

The team at KaleidEO has successfully demonstrated deep learning based algorithms to analyze imagery in-orbit, as captured by Satellogic – a satellite constellation and data provider company based out of Montevideo, Uruguay.

The hardware to run the algorithms and implementation support was provided by KaleidEO’s partner Spiral Blue, a startup based out of Sydney, Australia. The demonstration further builds SatSure’s positioning within the space data analytics domain globally, with KaleidEO now adding the capability of analyzing satellite imagery at the satellite itself.

KaleidEO has built edge algorithms for cloud detection, road network and building footprint identification, water-body detection, and image template matching for change detection. The success of this technology demonstration is a ramp up to KaleidEO’s own mission of launching four satellites in 2025, which would have this edge computing capability. KaleidEO is a subsidiary company of SatSure Analytics India Pvt Ltd.

“It is an important moment for SatSure and the Indian Space industry as KaleidEO becomes the first Indian company to unlock the potential of Edge computing in space. This capability will enable us to address national security needs, real-time disaster response by governments and revolutionize the way we collect, process, analyze and downlink imagery and insights from satellite data for the benefit of all,” Rashmit Singh Sukhmani, co-founder and CTO of SatSure said.

As part of this demo, a novel engineering pipeline was demonstrated that can handle various stages of the image processing and movement of data seamlessly. An efficiency of almost 80 times faster was achieved, and the data volume reduction between the input and output (analysis ready data) was ~ 99%, reducing the downlink cost.

The demonstration was done on sub-meter resolution imagery, which very few companies in the world have achieved because of the challenges of handling larger sized images.