Physical World Context for Aerospace & Earth Observation AI systems built on documents, databases, and the internet miss the physical world entirely. Wherobots gives aerospace teams the context layer for satellite imagery analysis at petabyte scale: process sensor data, run computer vision models, and turn Earth observation data into answers your AI stack can act on. Up to 20x faster than legacy pipelines. REQUEST A DEMO Companies Accelerating Outcomes with Wherobots and Apache Sedona Why Aerospace Teams Build Physical World Context with Wherobots Your AI reads documents, databases, and structured data. It does not see the physical world. Wherobots closes that gap for aerospace: satellite image analysis at petabyte scale, unified raster-vector processing, and AI inference that turns imagery into intelligence. Overwhelming data volumes Detection bottlenecks Preprocessing delays Fragmented analytics stack Multi-sensor data integration Overwhelming data volumes Industry Problem Overwhelming data volumes Aerospace teams manage 100+ petabyte satellite imagery archives growing by 80-100TB daily. Traditional satellite imagery processing pipelines bottleneck at scale, delaying insights by hours or days. AI systems that reason only over documents and databases cannot touch this data without a physical world context layer. Wherobots solution Planetary-scale processing Wherobots processes petabyte-scale satellite imagery archives with distributed geospatial analytics that scale automatically. Raster and vector datasets across entire satellite constellations, processed without infrastructure management, and ready to feed AI systems with physical world context. 100+ PB Processing Capacity Detection bottlenecks Industry Problem Change detection bottlenecks Monitoring environmental changes, infrastructure development, or disaster impacts from satellite imagery requires comparing billions of pixels across time. Traditional GIS and Earth observation platforms collapse under this load. Without spatial AI, these signals stay trapped in raw imagery. Wherobots solution Distributed change detection Wherobots executes pixel-level change detection and semantic segmentation across global satellite imagery stacks. Distributed geospatial AI identifies land cover changes, deforestation, and infrastructure development at continental or planetary scale, converting raw pixels into structured physical world context. 10× Faster Analysis Speed Preprocessing delays Industry Problem Preprocessing delays Raw satellite data requires atmospheric correction, orthorectification, radiometric calibration, and cloud masking before analysis, creating processing pipelines that take days to deliver analysis-ready data. Wherobots solution Streamlined data preparation Build efficient ETL pipelines for satellite imagery and Earth observation preprocessing using distributed Spatial SQL and Python. Transform raw imagery to analysis-ready geospatial datasets at scale with minimal configuration. ETL Time Days → Min Fragmented analytics stack Industry Problem Fragmented analytics stack Earth observation teams juggle separate tools for satellite imagery raster processing, vector analysis, ML inference, and geospatial data cataloging. No single platform provides the spatial AI infrastructure to unify these operations. Wherobots solution Unified geospatial platform Wherobots unifies raster and vector processing, spatial analytics, and ML inference in one environment. RasterFlow runs computer vision models on satellite imagery at scale. WherobotsDB’s 300+ spatial functions covering vector and raster data, with native Spark SQL for tabular operations, give teams a single platform for every geospatial AI workflow. 1 Platform Tools Unified Multi-sensor data integration Industry Problem Multi-sensor data integration is difficult Aerospace systems generate diverse satellite data from aircraft sensors, navigation systems, and Earth observation satellite constellations in varying formats that are difficult to integrate and analyze together for geospatial intelligence. Wherobots solution Multi-format data integration made easy Process and join satellite imagery and Earth observation data from multiple satellites, sensors, and formats. Wherobots Global Hub provides a unified view of all geospatial datasets with Apache Iceberg-based data lakehouse architecture. 50+ Formats Unified “The combination of speed, scalability, and ease of integration has boosted our engineering productivity and will accelerate how quickly we can deliver new geospatial data products to market.” Rashmit Singh CTO & Co-founder, SatSure See How Aerospace Teams Build AI That Sees the Physical World Schedule a demo to see how aerospace and Earth observation teams process petabytes of satellite imagery, run AI inference at scale, and build the physical world context layer their AI systems need. TALK TO US Explore Our Resources for Aerospace & Earth Observation Learn about satellite imagery processing innovations, Earth observation best practices, and geospatial analytics advancements for aerospace companies. Raster Data Joins at Scale Compare Wherobots with Google Earth Engine and BigQuery for satellite imagery raster operations, geospatial joins at scale, and running Earth observation models. LEARN MORE Introducing RasterFlow: Satellite Imagery Processing Engine for Earth Observation RasterFlow is a serverless satellite imagery preparation and inference engine that makes it significantly easier to process and analyze planetary-scale Earth observation datasets and generate geospatial intelligence. LEARN MORE Webinar: RasterFlow for Planetary-Scale Satellite Imagery and Earth Observation How RasterFlow works and why you should consider it for planetary-scale Earth observation models that you want to run instantly. WATCH NOW Frequently Asked Questions About Satellite Imagery Processing for Aerospace and Earth Observation How does Wherobots handle petabyte-scale satellite imagery archives? Wherobots leverages a modern data lakehouse architecture built on Apache Iceberg and cloud object storage. Your satellite imagery stays in cost-effective cloud storage while WherobotsDB provides distributed query processing for geospatial operations. The platform scales automatically to handle queries across petabytes of satellite data, and you only pay for compute when processing jobs run. What are the typical deployment options for Wherobots for aerospace companies? We deploy as a managed cloud service. But we also have options for bring-your-own-cloud and VPC. Can Wherobots process both raster imagery and vector geospatial data? Yes. Wherobots provides unified support for both raster and vector geospatial data in a single platform. WherobotsDB includes over 300 spatial functions covering geometry operations, raster analytics, and spatial joins. You can combine vector map data with satellite imagery and Earth observation analysis in the same SQL query or Python workflow, eliminating the need for multiple specialized tools. How does RasterFlow help with satellite imagery analysis? RasterFlow is Wherobots’ serverless inference engine for Earth observation. It runs computer vision models, including semantic segmentation, object detection, and classification, on satellite imagery at scale. Write a SQL query or Python script to detect buildings, roads, or land cover types across millions of satellite images. RasterFlow handles distributed model inference, mosaicking, and vectorization automatically. What makes Wherobots faster than traditional aerospace and Earth observation processing pipelines? Wherobots incorporates multiple performance optimizations: proprietary spatial indexing techniques, query optimization for geospatial operations, distributed parallel processing, and efficient I/O with cloud storage. These deliver up to 20x faster execution compared to processing the same workloads with general-purpose data platforms. Faster satellite imagery processing means quicker time-to-insight and lower compute costs. Does Wherobots work with our existing aerospace data infrastructure? Yes. Wherobots integrates with cloud data platforms including AWS, and can connect to data warehouses like Snowflake and Databricks. The platform is built on Apache Sedona APIs, providing compatibility with standard geospatial workflows. You can start with a subset of your data for pilot projects while keeping existing systems operational. Get started Ready to give your AI physical world context? TALK TO US
Raster Data Joins at Scale Compare Wherobots with Google Earth Engine and BigQuery for satellite imagery raster operations, geospatial joins at scale, and running Earth observation models. LEARN MORE
Introducing RasterFlow: Satellite Imagery Processing Engine for Earth Observation RasterFlow is a serverless satellite imagery preparation and inference engine that makes it significantly easier to process and analyze planetary-scale Earth observation datasets and generate geospatial intelligence. LEARN MORE
Webinar: RasterFlow for Planetary-Scale Satellite Imagery and Earth Observation How RasterFlow works and why you should consider it for planetary-scale Earth observation models that you want to run instantly. WATCH NOW
