Scaling Satellite Imagery Analysis for Site Viability on Google Cloud

Scaling satellite imagery analysis comes in two flavours — scaling to multiple use cases and scaling to navigate billions of pixels. But why would you leverage satellite imagery to begin with?

We put together a pattern and demo that primarily leverages commodity tools like BigQuery and Looker to address, for example, site viability based on historical flooding.

Site viability of any asset is a challenging, multi-faceted endeavour. One key area for consideration is flooding. Determining the propensity for a site to flood, or where on a site is likely to flood, may aid in improving the construction, operation, financing, insuring and leasing of a site for industrial assets.

Yet another lever to pull on with regard to improving the baseline is the idea of calibration. The definition of ‘flooding’ can be fluid (pun alert!) in the context of remote sensing. For example, water turbidity can impact the boundary between land and water, and inundated soil caused by flooding or heavy rainfall can be a costly problem despite not being strictly defined as flooding itself. With this in mind, there is a strong need for calibration, propensity scoring or additional metrics to enable an intelligent and automated site viability process.

This requires combined GIS, data science and data engineering smarts.

As the volume of remote sensing data grows, so does the number of potential use case — each has its own nuances and challenges. Scaling to new and multiple use cases is becoming more of a reality. Moreover, it is further enabled by the ability to scale data pipelines to handle billions of pixels with minimal effort.

Part two, the follow-up blog post by my colleague Simon Darr, will discuss how we processed Sentinel 2 satellite raster images in Google Cloud’s Cloud Build and BigQuery along with visualising the output in Looker (a common, non-GIS-specialist business intelligence tool). Serverless and commodity pipelines and tools such as these help to handle the scale of this data source as well as insuring reusability across various user roles.

Stay tuned for the details!

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