RasterFlow run AI on satellite imagery at any scale RasterFlow is the production-grade inference engine for Earth intelligence. Bring your own PyTorch model or use the built-in library. Point it at any imagery source. RasterFlow returns structured spatial features, ready for spatial joins in WherobotsDB. The bridge between what satellites capture and what your AI reasons about. START BUILDING Run inference on satellite imagery at planetary scale No custom computer vision pipeline required. Combine raster and vector in one pipeline Satellite-derived features flow into spatial joins in WherobotsDB. From built-in models to bring-your-own Fields of the World, Tree Canopy Height, SAM2, or any PyTorch model. Unlock Geospatial Intelligence with AI From satellite imagery analysis to production spatial features, in one pipeline. AI for remote sensing imagery Hosted Models Raster and vector analysis Automate and scale Apply AI models to remote sensing imagery Use state of the art AI models on remote sensing imagery for object detection, segmentation and classification. Or bring your own model. Learn More Leverage out of the box models on your imagery See all the models that Wherobots supports and will be supporting soon on Huggingface. Learn More Combine raster and vector analysis Seamlessly store model inference results in Wherobots for further analysis and enrichment with vector or tabular data. All within a single platform and single development experience through Python or Spatial SQL. Learn More Automate and scale Scale your analysis from small-scale prototyping to planetary-scale production workflows, only pay for what you use. Learn More Benefits of AI for Earth Built-in models, or bring your own Fields of the World, Tree Canopy Height, Road Segmentation, SAM2. Or bring any PyTorch model. Learn more Solution notebooks for real-world use cases Agriculture, land cover, and infrastructure detection. Start from working code. Learn More From a single tile to continental coverage Automatic mosaicking, cloud removal, and distributed processing. Scales with your data. Learn More Integrated with WherobotsDB Inference outputs flow into WherobotsDB as vector features for spatial joins and downstream analytics. Learn More GPU-optimized serverless inference Cost-effective at any scale. Airplane detection runs at $2.50. No infrastructure to manage. Learn More Instant results in open formats GeoParquet and Apache Iceberg output. Query outputs immediately in WherobotsDB or your lakehouse. Learn More Turn satellite imagery into structured spatial features RasterFlow detects buildings, roads, vegetation, water, and land cover from satellite imagery. No manual classification. No custom computer vision pipelines. Point RasterFlow at imagery, get analysis-ready vector features back. START BUILDING Satellite imagery analysis in action Object detection, field boundary extraction, land cover classification, and road segmentation, powered by RasterFlow. Airplanes detected with SAM2/OWLv2 Find airplanes from open satellite imagery using Wherobots and the SAM2 and OWLv2 models from a single text prompt. Solar farms detected with RasterFlow Detect solar farms in open satellite imagery using Wherobots and the Allen Institute Satlas models. Load satellite imagery from any STAC catalog Load STAC items and collections directly into Sedona DataFrames. Connect to any SpatioTemporal Asset Catalog, including Sentinel-2, Landsat, and NAIP. Three lines of Python. No preprocessing pipeline required. STAC reader COGs MLM Learn More df = (sedona.read .format("stac") .load("https://earth-search.com/sentinel-2-c1-l2a")) PythonSatellite imagery inference with SQL and Python Run satellite imagery analysis with a few lines of SQL or Python. RasterFlow handles distributed inference, mosaicking, and vectorization. Post-process or join results in WherobotsDB.. SQL AI Remote Sensing Python Segmentation Learn More -- Find bounding boxes for 'solar panels' CREATE OR REPLACE TEMP VIEW detected_bboxes AS SELECT raster_column, RS_TEXT_TO_BBOXES('SAM 2 ', raster_column, 'solar panels') AS detection_result FROM my_imagery_table; Bring your own model Register any PyTorch model with RasterFlow using the Machine Learning Model (MLM) standard. Point it at satellite imagery and run distributed segmentation, detection, or classification. Same SQL interface, your model, planetary scale. MLM Custom ML Models Learn More user_mlm_uri = [PATH-TO-YOUR-MLM-JSON] predictions_df = sedona.sql(f""" SELECT rast, segment_result.* FROM ( SELECT rast, RS_SEGMENT('{user_mlm_uri}', rast) AS segment_result FROM df_raster_input ) AS segment_fields """) FAQ What is RasterFlow? RasterFlow is Wherobots’ serverless inference engine for satellite imagery analysis and Earth intelligence. It builds mosaics from multiple raster data sources, runs inference with PyTorch computer vision models, and vectorizes results into spatial features. RasterFlow supports semantic segmentation, regression, and patch-based processing at planetary scale. The engine manages its own compute resources and autoscales based on area, time range, dataset density, and model complexity. Raster Inference is now a part of RasterFlow. What models does RasterFlow include? RasterFlow includes curated, open-source models for common Earth observation tasks. Built-in models include Fields of the World for agricultural field boundary detection from Sentinel-2 imagery, Meta CHM v1 for canopy height estimation from aerial imagery, Tile2Net for urban infrastructure detection (sidewalks, crosswalks, pedestrian pathways), and ChesapeakeRSC for rural road detection. RasterFlow also supports bring-your-own-model: register any PyTorch model using the Machine Learning Model (MLM) standard, upload it to your cloud storage, and run distributed inference through the RasterFlow API or as an automated Job Run. What satellite imagery does RasterFlow work with? RasterFlow includes two built-in satellite imagery datasets. Sentinel-2 provides worldwide coverage at 10m to 30m resolution with 5-day revisit rates. RasterFlow automatically applies cloud and quality filtering when building Sentinel-2 mosaics, masking out cloud pixels, shadows, and sensor anomalies. NAIP (National Agriculture Imagery Program) provides high-resolution imagery (30cm to 1m) across the continental United States. RasterFlow can also build mosaics from your own GeoTIFF imagery stored in cloud storage.< Get started RasterFlow is part of Wherobots, the AI Context Engine for the Physical World. TALK TO US START BUILDING
