AI, data processing and actionable analysis: how space data is shaping the energy landscape

Energy Transition Talks

In this episode of our Energy Transition Talks podcast series, CGI space expert Harjit Sheera shares with Peter Warren how the volume of space data is not only ever-increasing, but also growing in impact and application across industries. Discussing how processing space data for accessibility and effective use was previously an arduous task, they explore how artificial intelligence (AI) and advanced processing platforms are helping organizations make the most of their space data. From environmental impact monitoring to emissions mapping and data layering, space data is changing the way we see and act on energy transition goals.

Improving and accelerating traditionally cumbersome space data with AI

Operating across the entire space stakeholder chain, CGI space experts work as advisors for space organizations, collaborate with regulatory agencies and support end users through application development and managed services.

In her almost 20 years of experience working in space, Harjit knows the legacy challenges space data poses, specifically in terms of harnessing and translating its vast volume. “It takes a lot of processing power, a lot of storage energy and a lot of standardization to make that data available to people who can turn it into something that the end user will see.”

Emerging processing engines (including those processing earth observation data, examining imagery or setting standardized requisite parameters) are using AI, machine learning and advanced algorithms to refine further and perform better, faster. This means greater volumes of data can be processed more efficiently and more, diverse user requirements can be addressed.

Specifically, AI helps identify key elements in satellite images and processes them faster, based on set user requirements. For example, Harjit shares the use case of farmers leveraging AI and satellite imagery data to monitor and demonstrate how they’re farming their land and what kind of crops they’re growing, to claim government subsidies.

Peter highlights the positive implications the advanced deep learning and crop recognition use case has for energy organizations who want to monitor, for example, leaks or the growth of vegetation under power lines and near utility company infrastructure. It all helps to reduce the cost of maintenance and potential damage.

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