Raster Inference makes it easy for data developers to query aerial imagery using SQL

Introducing Wherobots Raster Inference to Unleash Innovation with Planetary Imagery

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Wherobots, the Spatial Intelligence Cloud founded by the original creators of Apache Sedona, today announced the general availability of Raster Inference for Wherobots AI. Raster Inference makes satellite or drone imagery analytically accessible for data developers who use SQL or Python. With flexible pay-as-you-go pricing and support for Wherobots hosted or custom computer vision models, solutions can be built from aerial imagery in hours versus weeks or months, at a fraction of the cost of starting from scratch.

Planetary imagery is transforming industries

Satellite and drone imagery are fueling advancements across agriculture, energy, transportation, climate science, clean-tech, and insurance. But these massive, unstructured datasets are complex and multidimensional, and the go-to computer vision tools used to extract insights from this imagery are not designed for the scale and variety of this data. Expertise is also needed to set up and manage specialized infrastructure, tune it to improve model performance, and shuttle inference results—the boundary polygons and coordinates that identify features of interest like buildings, flood damage, or crops—into a secondary solution capable of working with this data. It can take weeks or months for teams to stitch together what are often fragile solutions. These high costs make solutions from this data hard to attain, or simply “off limits.”

Aerial intelligence is now accessible

WherobotsAI Raster Inference makes the creation of solutions based on satellite and drone imagery significantly easier, lower cost, and accessible to existing data teams. Key features include:

• On-demand inference: Production ready inference pipelines can be created in seconds, on-demand, with no infrastructure to manage.
• Model Integration: Start quickly with Wherobots’ hosted machine learning models or easily import existing models.
• Unified environment: Easily join inference results with other datasets using Spatial SQL and Python with a spatial engine that’s up to 20x more performant than popular alternatives.
• Workload automation: Job Runs and an integration with Apache Airflow make it easy to automate insights.
• Improved model portability: Support for the MLM STAC (Machine Learning Model Spatial Temporal Asset Catalog) extension, a new standard co-developed by Wherobots to enhance model portability across platforms.
• Example notebooks: Wherobots provides example notebooks for Wherobots hosted models to help customers get started.

“Every day, petabytes of satellite data are produced. But without specialized talent, deep pockets, or significant time investments, this data often remains untapped, putting solutions out of reach,” said Mo Sarwat, co-founder and CEO of Wherobots. “Raster Inference changes the game by enabling teams to easily derive actionable insights from aerial imagery, on-demand. This solution puts unprecedented power into the hands of developers and data scientists, driving impactful innovations for businesses and the planet alike.”

Improving portability of geospatial AI

Wherobots co-developed the MLM STAC extension alongside experts from Université de Sherbrooke, CRIM, Terradue, Natural Resources Canada, and other collaborators. This standard addresses the challenge of model portability by requiring comprehensive metadata—including model properties, data, and processing requirements—to make geospatial AI models more sharable and deployable across platforms.

“There are very few model inference products built for the unique needs of geospatial workloads, yet there are countless use cases that can benefit from existing models, and aerial imagery,” said Jia Yu, co-founder and Chief Architect of Wherobots. "We co-developed the MLM STAC extension to make models useful between organizations, use cases, and across platforms. And Wherobots Raster Inference does all the heavy lifting required to run these models in the background—making it easy for any modeling or data team to get critical insights faster from their large, noisy overhead imagery data.”

Empowering spatial intelligence across industries

Fresh off of a $21.5M Series A funding round and availability on the AWS Marketplace, Wherobots is accelerating the delivery of modern solutions that close the intelligence gap between our physical and digital worlds. Wherobots is already working with companies to put Raster Inference to work. In the energy sector, the solution is working to analyze electrical grid performance and predict equipment failures. The company is also using the solution to help customers detect solar farms and conflate results with weather data to predict solar energy production variability. Wherobots expects to see Raster Inference's use cases continue to grow as a result of lowering the high economic hurdles associated with analyzing aerial imagery.

Data teams can get started now with Wherobots Raster Inference now with $400 in free credit by signing up through the AWS Marketplace.

About Wherobots

Wherobots is the Spatial Intelligence Cloud that unlocks planetary-scale answers from geospatial data. It enables high performance geospatial ETL, analytics, and computer vision at planetary-scale with a modern data lakehouse architecture. Developed by the original creators of Apache Sedona, Wherobots empowers data teams to utilize spatial data up to 20x faster at a fraction of the cost of alternative cloud services when used for geospatial analytics and computer vision. For more information, visit www.wherobots.com.

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