Analyst time is the constraint, not the flight.
DataTorch is the review-and-AI layer for overhead imagery. It reads the frames your capture and processing pipeline already produces, lets your own detectors take the first pass across them, and routes what is left to an analyst. Used in peer-reviewed and preprint work from the University of Arizona and in the International Journal of Remote Sensing.
Reviewing overhead imagery doesn’t scale by hand.
Analysts label and count objects across thousands of aerial, drone, or satellite frames. Running detectors for the first pass and keeping experts in the loop to verify rarely fits general-purpose tooling.
Keep your capture and processing pipeline.
DataTorch does not fly the aircraft, build the orthomosaic, or hold your GIS. It works on the frames your pipeline already exports to storage you control, and hands annotations back — so it slots in after processing without changing anything upstream of it.
Yours
Your capture and processing
01
Connect
Point DataTorch at the storage your exported frames land in — S3, Azure, or Google Cloud. Images are read in place.
02
Model first pass
Your own detectors and segmenters run from a pipeline step, on your compute, across the batch.
03
Analyst review
Predictions land on the frame against your schema. An analyst confirms, corrects, or rejects.
04
Export
Reviewed annotations leave as COCO, YOLO, or JSON, or through the API — into your analysis, and into your next training run.
Yours
Your GIS and analysis
Aerial, drone, satellite
Review overhead imagery from any source in one workflow.
Boxes, polygons, counts
Detection, segmentation, and counting against your label schema.
Assigned review
Analysts claim work from a task inbox, and every run keeps its log.
Your models in the loop
First-pass detection from your own detectors, on your compute.
01 / Expert review
A review workflow shaped for imagery at scale.
Bounding boxes, polygons, segmentation, and counts against a label schema shaped to your work, with assigned review. Model predictions land on the image; your analyst confirms or corrects in one click, and each run keeps its log.
02 / Your models
Your detection models do the first pass. Yours, not ours.
Wrap your own detectors and segmenters — Mask R-CNN, Detectron2, your own weights — as Agents in a pipeline for first-pass inference across a batch. They run on your compute and surface predictions in the analyst review flow; your imagery never leaves your control. Every frame an analyst confirms adds to the labeled set your next training run uses.
Data control
Nothing leaves your infrastructure.
The questions your IT and quality teams will ask first, answered without hedging.
Your bucket, your credentials
Connect your own S3, Azure Blob, or Google Cloud Storage. DataTorch reads the images in place — they are never copied into a vendor bucket.
Your models, your hardware
Model agents run as Python on compute you control. Your weights and your images never reach a third-party model.
Hosted, your cloud, or no network at all
Run on our hosted cloud, deploy the same product into your own cloud, or install it fully air-gapped via Helm with an offline signed license.
Your data stays portable
Annotations export to COCO, YOLO, or JSON, and the GraphQL API and Python SDK reach every project — so the work is yours to take elsewhere.
DataTorch is not a certified or validated system, and it does not replace your quality process. It is built to run inside the environment your quality team already controls — on your hardware, against your storage, with no outbound path for your images. We’ll go through the specifics with your team on a call.
Used in published remote-sensing research.
Research teams build on DataTorch to annotate and review overhead imagery:
See it on your own images.
Start free on our hosted cloud, private project included. Or book 30 minutes to scope an on-prem or bring-your-own-cloud deployment — no slides either way.
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