Physical World Context for Sustainability & Agriculture AI systems built on documents and databases cannot see what is happening on the ground. Wherobots gives agriculture and sustainability teams the context layer to turn satellite crop monitoring, drone imagery, and sensor data into reliable crop, soil, and ecosystem intelligence, from field level to planetary scale. REQUEST A DEMO Why Agriculture Teams Build Physical World Context with Wherobots Crop health, soil conditions, water stress, carbon stocks: these signals live in satellite imagery, drone data, and sensor streams. Wherobots provides the spatial AI infrastructure to process this physical world data at scale, run remote sensing agriculture models, and deliver the intelligence your operations depend on. Satellite and Drone Data at Scale Scaling AI Inference on Satellite Imagery Change Detection Bottlenecks Fragmented Geospatial Workflows Specialized Teams and Costly Compute Satellite and Drone Data at Scale 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 Scaling AI Inference on Satellite Imagery 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 with RasterFlow RasterFlow handles the full pipeline: ingest satellite or drone imagery, build inference-ready mosaics, remove cloud cover, run PyTorch computer vision models at scale, and vectorize outputs for downstream joins. Satellite crop monitoring, field boundary detection, and land cover classification become production workflows, not research projects. PB+ Data Scale Change Detection Bottlenecks 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 Geospatial 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 Specialized Teams and Costly Compute 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 See How Agriculture Teams Build AI That Sees the Field Schedule a demo to see how agriculture and sustainability teams process petabytes of satellite imagery, run crop monitoring models at scale, and build the physical world context layer behind better field-level decisions. 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 How does Wherobots handle petabyte-scale satellite imagery for satellite crop monitoring? WherobotsDB runs distributed spatial SQL against cloud object storage directly, scanning imagery at planetary scale without moving data. Compute runs in serverless sessions or scheduled jobs and scales to workload size. Teams pay for compute when jobs run, not for idle infrastructure. Imagery, vector field boundaries, and sensor data participate in the same SQL and Python workflow, so satellite crop monitoring pipelines from raw ingest to field-level output run in one environment. How does Wherobots join satellite imagery with farm records, tractor telemetry, and weather data? WherobotsDB natively handles raster imagery (satellite bands, elevation models, precipitation grids), vector data (field boundaries, parcel records, land cover polygons), and tabular data (farm records, telemetry logs, weather observations) in the same query engine. A crop stress analysis can join NDVI-derived raster outputs with parcel boundaries and soil survey data in a single spatial SQL query. No separate pipeline stitching tools together. Outputs feed directly into your downstream models or reporting systems. How does Wherobots support sustainability reporting and carbon accounting? Wherobots processes multisensor satellite imagery, terrain data, soils datasets, and vegetation indices in production pipelines to generate repeatable, auditable spatial products for carbon project monitoring and sustainability reporting. RasterFlow handles imagery preprocessing and model inference. WherobotsDB joins outputs with field boundaries, land cover classifications, and ecosystem data in spatial SQL. The pipeline produces traceable, version-controlled outputs that meet the documentation requirements of carbon accounting frameworks. What deployment options does Wherobots offer for agricultural data workloads? Wherobots deploys as a managed cloud service, in your own cloud account (bring-your-own-cloud), or in a dedicated VPC. All three configurations connect to your existing cloud storage without data migration. Agriculture and sustainability teams with data governance or compliance requirements can run Wherobots within their own infrastructure perimeter. Wherobots is SOC 2 Type 2 attested. 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. How does Wherobots support time series analysis and change detection for agriculture and sustainability? WherobotsDB runs pixel-level temporal comparisons across satellite imagery archives using distributed Spatial SQL and Python raster workflows. Teams compute seasonal vegetation indices, detect crop stress anomalies, and generate deforestation alerts across millions of acres. Phenology tracking and change detection workflows that overwhelm traditional GIS run as scheduled Wherobots Jobs on a defined cadence. What is RasterFlow and how does it support remote sensing agriculture? RasterFlow is Wherobots’ serverless inference engine for Earth Observation datasets. It ingests satellite or drone imagery, builds inference-ready mosaics by removing cloud cover and edge effects, runs PyTorch computer vision models at scale, and vectorizes outputs as Apache Iceberg tables in your cloud storage. For remote sensing agriculture workflows, RasterFlow makes field boundary detection, satellite crop monitoring, land cover classification, and yield estimation production-grade at planetary scale. Results post-process directly in WherobotsDB for field-level joins and downstream reporting. 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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
