Innovative approach to improving maintenance operations using AI-powered satellite image analysis awarded at Europe’s premier space competition.
August 2nd, 2021 – Berlin, Germany – HACARUS, the leading provider of big insights from small data today announced that it was awarded at the 6th INNOspace Masters conference, for its proposal to the DB Netze challenge infrastructure monitoring challenge.
“This encouraging recognition serves as a clear indicator that the work HACARUS is doing to bring big insights from small data has an ever growing set of use cases where we can deliver value. I am thrilled that HACARUS is able to bring our positively disruptive technology also to the growing space field.” said Kenshin Fujiwara, CEO of HACARUS INC.
For the competition HACARUS’ solution proposal was an AI inspection platform that can provide instant insights of infrastructure assets’ status at a glance, and enable remote maintenance and surveillance. The system is able to adapt to changes over time, and can provide a bird’s eye view of assets’ current health. The human-centric design, including smart features such as heat maps and bounding boxes, provides operators with actionable insights for smarter maintenance operations. The core idea behind this approach is to swift maintenance operations from a time-based to needs-based system.
HACARUS will continue to utilize its expertise in Lightweight and Explainable AI to provide faster and better inspection to medical and manufacturing fields, as well as for future applications in space.
HACARUS INC. provides big insights from small data, and has since its founding in 2014 supplied solutions in 100+ AI projects across the Medical and Manufacturing fields. Headquartered in Kyoto, Japan and backed by Osaka Gas and Miyako Capital (Kyoto University) among others, its technology enables humans to make better, faster and more reliable decisions, based on data driven insights. HACARUS’ proprietary AI engine is built using Sparse Modeling, a method that understands data like a human would – by its unique key features and is far more resource, time and energy efficient when compared to Deep Learning. To learn more, visit https://hacarus.com