Scalable Spatial Intelligence for Sustainability & Agriculture Turn satellite, drone, and sensor data into reliable, timely crop, soil and ecosystem intelligence — at field, regional and planetary scale. REQUEST A DEMO Trusted by Leading Sustainability and Agriculture Organizations Use Wherobots to run computer vision models and yield models, complete complex raster analytics run spatial joins across petabytes of imagery and sensor streams so teams can predict yield, optimize inputs, monitor carbon and water, and measure landscape-level environmental outcomes. Processing Complexity GeoAI Limitations Overwhelmed Tools Fragmented Workflows Reduced Productivity Processing Complexity Industry Problem Why Traditional Pipelines Struggle with Multispectral Satellite and Drone Imagery Agricultural and environmental teams must process multispectral satellite, drone imagery, and IoT sensor data that grow daily and are expensive and slow to analyze with traditional pipelines. Wherobots solution Serverless, Planetary-Scale Raster and Vector Processing for Agricultural Data Pipelines Planetary-scale raster + vector processing with a Spatial Intelligence Cloud, enabling distributed ETL, mosaicking and analytics across petabytes without manual infrastructure ops. 100% Visibility GeoAI Limitations Industry Problem Why Scaling Computer Vision Inference Across Satellite Imagery Collections Is Complex and Costly Running computer-vision inference across large imagery collections is complex and costly to scale and operate. Wherobots solution Production-Grade Raster Processing: Ingest, Mosaic, Cloud Removal, and ML Inference at Scale Production-grade raster processing — ingest, mosaic, remove clouds, clean-up, run models at scale and vectorize outputs for downstream joins and outputs. This makes large-scale field detection, boundary extraction and raster-to-vector ML pipelines practical and low-cost. PB+ Data Scale Overwhelmed Tools Industry Problem Time Series and Change Detection Bottlenecks in Traditional GIS and ETL Pipelines Tracking phenology, crop stress, or deforestation requires pixel-level comparisons over time — operations that overwhelm traditional GIS and ad hoc ETL. Wherobots solution Fast Temporal Analytics and Distributed Change Detection for Crop Stress, Phenology, and Deforestation Monitoring Fast temporal analytics and distributed change-detection via Spatial SQL or python and raster workflows so teams can compute seasonal indices, detect anomalies and produce operational alerts across millions of acres. Real-time Event Response Fragmented Workflows Industry Problem Why Agricultural Data Teams Struggle Managing Separate Tools for Raster Processing, Vector Analytics, and Model Inference Teams juggle separate tools for raster preprocessing, vector analytics, model inference and cataloging. Integrations slow delivery. Wherobots solution A Unified Platform Combining RasterFlow, WherobotsDB, and Data and Model Hub for End-to-End Geospatial ML Workflows A unified platform combining RasterFlow, Data + Model Hub and WherobotsDB so you can prep imagery, run ML, and join predictions with farm/parcel or weather data all within a single environment. Zero Idle Costs Reduced Productivity Industry Problem Why Building and Maintaining Geospatial Pipelines Demands Specialized Teams and Costly Compute Building and maintaining geospatial pipelines typically demands specialized teams and costly compute. Wherobots solution A Managed Cloud Service with SQL and Python Notebooks That Delivers Predictable Economics for Geospatial Development at Scale Managed cloud service with SQL + Python notebooks and IDEs or jobs that reduce development time and deliver predictable economics for large-scale agricultural workloads. Customers report major reductions in processing time and cost as they scale to millions of acres. Managed Could Server Data teams using Wherobots and Apache Sedona for Sustainability & Agriculture “With Wherobots on AWS, not only can we easily scale to millions of acres, we also can rest assured that our costs won’t spiral out of control.” G. Bailey Stockdale CEO, Leaf Agriculture Ready to revolutionize your sustainability and agriculture data pipelines? Schedule a demo to see how leading sustainability and agriculture teams process petabytes of imagery and IoT data points and work with raster and vector data in the same platform. Talk to us Geospatial Agriculture and Sustainability Resources: Notebooks, Templates, and Guides from Wherobots Our solution accelerators are ready-to-run notebooks and templates that get your team from data to product fast. Each card links to a notebook, blog, or resource to accelerate your ramp. Fields of the World (FTW): Planetary-Scale Field Boundary Production with Wherobots and Taylor Geospatial Engine Wherobots collaborates with Taylor Geospatial Engine on the Fields of the World (FTW) for planetary scale field boundary production. LEARN MORE RasterFlow: Wherobots' Planetary-Scale Inference Engine for Earth Observation and Agricultural AI RasterFlow is Wherobots’ planetary-scale inference engine for Earth Intelligence. Request access to the private preview to see it in action. LEARN MORE How to Run Zonal Statistics on ESA WorldCover Data Using WherobotsDB Run the RS_ZonalStatsAll using ESA WorldCover in WherobotsDB in a few simple steps. LEARN MORE Frequently Asked Questions About Spatial Intelligence for Sustainability and Agriculture How does Wherobots handle petabyte-scale satellite imagery and IoT sensor data for agriculture? Wherobots uses a modern data lakehouse architecture with Iceberg and cloud object storage, combined with distributed execution to scan and process imagery at planetary scale. Imagery is stored in cost-efficient cloud buckets and distributed queries scale automatically. Compute is billed only when jobs or interactive operations are run.Wherobots’ Spatial Catalog and WherobotsDB let you join raster, vector, and structured data in the same SQL and Python workflow so you can build attributable, repeatable models and reports. How does Wherobots join satellite imagery with farm records, tractor telemetry, and weather data? Wherobots’ Spatial Catalog and WherobotsDB let you join raster, vector, and structured data in the same SQL and Python workflow so you can build attributable, repeatable models and reports. How does Wherobots support sustainability reporting and carbon accounting? Wherobots combines multisensor imagery, terrain and soils data, and vegetation and biodiversity mapping in production pipelines so teams can generate repeatable, auditable spatial products for sustainability reporting and carbon project monitoring. Wherobots’ unified raster and vector workflows reduce the operational burden of building these analytics at scale. What deployment options does Wherobots offer for geospatial agricultural data workloads? Wherobots offers managed cloud service deployment, BYOC (bring-your-own-cloud), and Virtual Private Cloud (VPC) models depending on your security and operational requirements. How does Wherobots support time series analysis and change detection for agriculture and sustainability? Wherobots provides fast temporal analytics and distributed change detection via Spatial SQL and Python raster workflows. Teams can compute seasonal indices, detect anomalies, and produce operational alerts across millions of acres. This makes it practical to track phenology, crop stress, and deforestation through pixel-level comparisons over time — operations that overwhelm traditional GIS and ad hoc ETL pipelines. What is RasterFlow and how does it work for agriculture and Earth Observation? RasterFlow is Wherobots’ serverless image preparation and inference engine designed to generate Earth Intelligence from planetary-scale Earth Observation datasets. It combines imagery ingestion, mosaicking, cloud removal, distributed model inference, and vectorization into a single on-demand workflow. RasterFlow ingests satellite or drone imagery, builds an inference-ready mosaic by removing cloud cover and edge effects, runs PyTorch computer vision models at scale across the mosaic, and vectorizes model outputs into geometries stored as Apache Iceberg tables in your cloud storage. Results can be post-processed directly in WherobotsDB for field-level crop insights, boundary detection, and other agricultural and sustainability applications. Get started Ready to see location-level risk insight across your book? Request Demo try wherobots now
Fields of the World (FTW): Planetary-Scale Field Boundary Production with Wherobots and Taylor Geospatial Engine Wherobots collaborates with Taylor Geospatial Engine on the Fields of the World (FTW) for planetary scale field boundary production. LEARN MORE
RasterFlow: Wherobots' Planetary-Scale Inference Engine for Earth Observation and Agricultural AI RasterFlow is Wherobots’ planetary-scale inference engine for Earth Intelligence. Request access to the private preview to see it in action. LEARN MORE
How to Run Zonal Statistics on ESA WorldCover Data Using WherobotsDB Run the RS_ZonalStatsAll using ESA WorldCover in WherobotsDB in a few simple steps. LEARN MORE
