RasterFlow is now available in Public Preview

RasterFlow makes planetary-scale earth intelligence workflows easy and costs predictable.

We are excited to announce that RasterFlow is now in Public Preview, opening up the power of planetary scale Earth Intelligence to all Wherobots Professional Edition customers!

RasterFlow let’s you solve complex monitoring challenges with vision-language models or tailored models for specific use cases, without needing to manage complex raster preparation and inference infrastructure.

Teams are already running RasterFlow at planetary scale:

• The USDA Forest Service uses RasterFlow to deliver wildfire containment predictions to its Wildfire Risk Management Division at an operational cadence.
• Miraterra uses RasterFlow to predict agricultural field boundaries across the U.S. Midwest and Canada’s Prairie Provinces, then joins detailed microbial samples and geospatial embeddings to those boundaries.
• Taylor Geospatial worked with Wherobots to produce 8.2 billion global field boundaries for the Fields of the World program.

Join our Public Preview virtual event, Pixels to Predictions: Planetary-Scale Earth Observation with RasterFlow, for a live walkthrough of built-in models, predictable pricing, and real customer pipelines. Register Here →

RasterFlow is a serverless image preparation and computer vision engine that makes it easy to extract insights from large scale raster datasets. It builds mosaics from multiple raster data sources, runs inference with computer vision models, and vectorizes the results, through a high-level API that simplifies the complexity of raster pipelines and distributed computing. You pay RasterFlow usage using a predictable, low cost pricing model that scales with your area of interest.

With RasterFlow’s built-in models, users can instantly launch tasks for common Earth Intelligence use cases. For instance, RasterFlow includes Meta’s SAM3 model for text-prompted object detection, Taylor Geospatial’s Fields of the World model for agricultural field boundaries, Meta’s CHM v1 for estimating tree canopy height. You can also bring your own, and we are expanding the set of open models we offer out-of-the box (let us know which open models you want offered and we can add them).

Market leading pricing that you can predict

RasterFlow’s predictable pricing allows you to estimate the cost of your tasks before they run. The price for each task is based on the data volume processed, so you can accurately estimate the cost of any task before execution. And pricing scales linearly with the size of your area of interest, so you can extrapolate the costs from smaller test runs to planetary scale.

Data volume = Area in km² × Pixels per km² × Bands × Time periods

The four inputs are:

• Area: Size of your AOI in km²
• Pixels per km²: Determined by input resolution; finer imagery means more pixels
• Bands: For example, 4 for RGB + NIR
• Time periods: For example, 3 annual observations

Let’s walk through a concrete example. If you wanted to find every solar panel array across a 500 km² county, you could use the built-in SAM3 model and a simple text prompt (“solar panel”) to generate detections. Here’s the code to launch this task across your area of interest using RasterFlow’s built-in support for 30cm imagery from USDA’s National Agriculture Imagery Program (NAIP):

Pythonfrom rasterflow_remote import RasterflowClient
from rasterflow_remote.data_models import GeometryModelRecipes

rf = RasterflowClient()

detections = rf.predict_mosaic_geometries_recipe(
    aoi="s3://your-bucket/county.parquet",
    start=datetime(2022, 1, 1),
    end=datetime(2023, 1, 1),
    model_recipe=GeometryModelRecipes.SAM3_TEXT_GEOMETRY,
    text_prompt="solar panel",
    confidence_threshold=0.5,
)

print(detections.uri)  # GeoParquet containing detected solar-panel polygons

To calculate the price for this task, we first determine the number of input pixel values based on the size of the AOI (500 km²), the resolution of the dataset (30cm NAIP), the number of bands (4), and the number of time periods (1).

Price = Data volume × Task-specific rate

Then, for mosaic generation and inferencing tasks, we apply a “complexity factor” to account for differences in task processing. For instance, building a mosaic from NAIP imagery is simpler than creating a cloud-free composite from Sentinel-2 imagery, so we apply a 0.1× multiplier.

Similarly, different models have different complexity factors, so we apply the relevant inference complexity factor (in this case, 1.0× for SAM3).

Finally, each RasterFlow task has a specific price that may vary by compute region. Combining these factors, we arrive at our total costs for these tasks:

For more details on RasterFlow pricing, see our pricing page and documentation.

To see some example solar panel detections for Marion County, Oregon, here is an interactive visualization:

Check out our viewer to explore these results further. Or, for more details about our Text to Detections support with SAM3, see this blog post: Detecting Objects From Text Prompts with RasterFlow and SAM3.

Your mosaics, predictions, and vectors in your storage

Wherobots storage integrations connect RasterFlow directly to your own S3 buckets. Tasks can read your areas of interest and proprietary imagery from your buckets, then write mosaics, predictions, and vectorized results back to them. Wherobots securely manages the required roles and credentials, so you can focus on your workflows instead of wrangling permissions.

Pythonfrom rasterflow_remote import RasterflowClient, DatasetEnum

client = RasterflowClient()

result = client.build_mosaics(
    datasets=[DatasetEnum.S2_MED_HARVEST],
    aoi="s3://my-company-data/aois/project.parquet", # one or multiple geometries
    start=datetime(2024, 1, 1),
    end=datetime(2025, 1, 1),
    bucket="s3://my-company-data/rasterflow/results",
)

print(result.first_row_mosaic)
"s3://my-company-data/rasterflow/results/mosaics/<run-id>/mosaic_index.parquet"

Outputs remain in open, interoperable formats: Zarr for mosaics and predictions, GeoParquet for vectorized results, and Iceberg tables through managed catalogs. Your data and results remain in the storage your applications already use, without a separate migration or export workflow.

Built-in visualization to inspect your Earth observation insights

Visual inspection is essential for validating inference results at scale, but large raster outputs are difficult to explore in their raw form. Wherobots lets users instantly layer mosaics, model predictions, and vectorized results on an interactive map, making it easy to assess quality, tune thresholds, and spot misaligned or spurious detections. Every completed RasterFlow task includes a one-click link to view its results in the Workload History.

RasterFlow also includes tasks that optimize existing Zarr stores for interactive viewing by adding image pyramids, downsampled overviews, and histogram statistics. The built-in map client then streams coarse tiles when zoomed out and full-resolution pixels when zoomed in, delivering responsive exploration at any scale.

For example, see this interactive visualization of model outputs from SAM3:

How RasterFlow compares to Google Earth Engine

Google Earth Engine provides a deep planetary imagery catalog and a strong environment for exploratory analysis. But for planetary scale workflows, Google Earth Engine has some significant limitations:

Limited cost predictability. Earth Engine bills in EECU-hours, so you only learn the total cost after the job finishes. With RasterFlow, you can calculate the cost of your tasks before you run them, eliminating uncertainty and potential billing surprises.

Build your own inference pipelines. Building a planetary scale earth observation pipelines with computer vision models requires integration with Vertex AI and custom pipeline development. Workflows that integrate with imagery data sources, patch tiles, handle seams, and maintains georeferences are costly to develop and maintain. RasterFlow packages mosaicking, inference, and vectorization as built-in tasks with a simple API.

Results are siloed in Earth Engine. Analysis results are stored in Earth Engine, which is separate from Google Cloud Platform or Google Cloud Storage. Using them elsewhere requires queuing export jobs, so a team building on AWS pays both export and egress costs.

Mosaicking cost. Because of these export costs, mosaicking costs on Earth Engine can be significantly higher for developers in AWS. For instance, generating and exporting a Sentinel-2 mosaic with all 12 bands for 150,000 km² costs about $50 in Earth Engine vs. $13.50 for RasterFlow.

Customer impact at planetary scale

Fields of the World: a global field-boundary layer

Fields of the World is a Taylor Geospatial effort to produce globally consistent agricultural field-boundary data for land-use monitoring, food-system analysis, and model development. Taylor Geospatial partnered with Wherobots to run their PRUE model on RasterFlow for the 2024 to 2025 global release. Read about the Fields Of The World (FTW) Project.

We achieved this with three tasks that you can run today:

1. Building mosaics to create the seasonal Sentinel-2 composites
2. Model inference to run the PRUE model globally
3. Vectorization to convert the per-pixel predictions into field boundaries in GeoParquet.

In total, we generated 348.7 TB across 540,794 objects, to produce 8.2 billion field boundaries. The full pipeline write-up is in Fields of the World: a GeoAI pipeline on RasterFlow.

Daily wildfire containment mapping with the USDA Forest Service

The USDA Forest Service FireCon model produces containment-suitability maps for active wildfires across the Western United States. Because fuel, terrain, weather, and fire conditions change continuously, yesterday’s map may not reflect the conditions crews face today. Previously, the cost and complexity of running the full pipeline limited how frequently the team could update these maps.

RasterFlow changes that by ingesting potential control location data, terrain characteristics, weather forecasts, and daily soil moisture readings, normalizing the data and running the full inference pipeline end-to-end in a matter of hours rather than a full day. That dramatic reduction in both runtime and cost means the team can afford to run the pipeline multiple times per day, giving frontline response teams a current view of the fire landscape. The results are also delivered as multi-resolution raster layers, so they render smoothly whether crews are looking at a broad regional view or zooming in on a specific fire line. In practice, this translates directly into better-informed containment and resource decisions on the ground, especially during fast-changing fire conditions where yesterday’s map simply isn’t good enough.

Get started today

RasterFlow is available now to Wherobots Cloud Professional Edition users:

1. Sign in to Wherobots Cloud.
2. Build a mosaic or run a built-in model by starting with one of these notebooks:
   • Detect objects with SAM3.
   • Build a cloud-free Sentinel-2 mosaic.
   • Find agricultural field boundaries.
   • Estimate canopy height with Meta CHM v1.
3. Register for the Public Preview webinar.