The vast availability of planetary imagery is driving advancements across sectors such as agriculture, energy, transportation, climate science, and insurance. However, the scale and complexity of unstructured geospatial datasets often make the analysis process slow, expensive, and inaccessible. Existing tools frequently require specialized infrastructure, advanced expertise, and prolonged development to transform imagery into actionable intelligence. These challenges have left valuable insights "off limits" for many organizations.

WherobotsAI Raster Inference removes these barriers by making aerial imagery analysis accessible, streamlined, and cost-efficient. Key features of the platform include:

- On-demand inference: Production-ready inference pipelines with no infrastructure management, available in seconds.

- Model integration: Use Wherobots' hosted machine learning models or import custom models seamlessly.

- Unified environment: Join results with other datasets using Spatial SQL and Python, backed by a spatial engine that outperforms alternatives by up to 20x.

- Workload automation: Job Runs and Apache Airflow integration simplify automation for consistent insights.

- Improved model portability: The platform supports the MLM STAC extension, a standard co-developed by Wherobots to improve model sharing and deployment.

- Example notebooks: Ready-to-use resources help teams get started quickly with hosted models.

"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."

Wherobots' co-development of the MLM STAC (Machine Learning Model Spatial Temporal Asset Catalog) extension further addresses geospatial AI challenges by improving model portability. Collaborating with Universite de Sherbrooke, CRIM, Terradue, Natural Resources Canada, and others, the standard ensures that comprehensive metadata accompanies AI models, making them easier to share and deploy 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."

Backed by $21.5M in Series A funding and now available on the AWS Marketplace, Wherobots is delivering modern solutions to close the intelligence gap between the physical and digital worlds. Current applications include analyzing electrical grid performance in the energy sector, predicting equipment failures, and detecting solar farms to forecast energy production variability by combining results with weather data. The company anticipates continued expansion of use cases as Raster Inference reduces the economic challenges of aerial imagery analysis.

Data teams can start using Wherobots Raster Inference now and receive $400 in free credit by signing up through the AWS Marketplace.

Related Links
Wherobots
Earth Observation News - Suppiliers, Technology and Application