Stages 04 – 05

Processing & AI

A delivered scene is raw material, not an answer. SatView runs it through a real processing chain, then reads it too — with models you control, on hardware you own.

04 · Process

Build the chain visually, run it asynchronously

A pipeline is a DAG you assemble in the browser. Jobs are submitted to the Python backend, tracked in the interface, and their outputs land back on the map as layers.

30+ native operations

Grouped by intent in the Processing Center: analysis (spectral indices, image statistics, change detection), geometry (reproject, extract ROI, orthorectification), filtering, pre-processing (band extraction, contrast enhancement, cloud masking, atmospheric correction DOS1) and fusion (image arithmetic, pansharpening, mosaic).

SAR & interferometry

SAR calibration, speckle filtering, and interferometric processing as first-class pipeline steps: InSAR coherence and InSAR displacement. Range-Doppler terrain correction is on the roadmap.

Classification & segmentation

K-means and threshold-index classification, morphological operations, watershed, graph-based and SLIC superpixel segmentation, connected components — each producing a labelled raster you can carry downstream.

Orfeo Toolbox & ESA SNAP

Industry-standard remote sensing toolchains driven through their CLI and GPT interfaces — not reimplemented, actually invoked.

Cloud-native

Process straight from a COG URL without downloading the full scene first, and push results back as cloud-optimized GeoTIFF.

Super-resolution

Upscaling for detail recovery on lower-GSD sources, available as a pipeline step like any other operation.

The Processing Center: an image library on the left, the full pipeline operation catalogue grouped into analysis, geometry, filtering, pre-processing, fusion, SAR, segmentation, classification and export, and a spectral indices parameter form set to NDVI with band assignments and a Run Processing button.

Processing Center · operation catalogue and job parameters

05 · Analyze

Inference that produces geometry, not just pictures

Every AI job returns a raster and a companion GeoJSON reprojected to WGS84 — so results are map layers, queryable objects and exportable evidence, not screenshots.

  • Object detection — ONNX models run with a sliding window, or classic blob detection. 15 classes out of the box (plane, ship, storage tank, harbor, helicopter, large/small vehicle, roundabout, bridge, sports fields and more) — bring your own model for anything else. Output: heatmap raster plus GeoJSON, with a confidence-threshold slider and per-class counts.
  • Change detection — Change Vector Analysis with Otsu thresholding, polygonized into vector change masks.
  • Semantic segmentation — ONNX models or spectral K-means, producing a labelled raster with its legend.
  • Detection layer — results rendered on the map as a GeoJSON layer, alongside the imagery they came from.
  • Auto-indexing — every detection feeds a searchable index, so “show me every ship detected in this area last month” becomes a query rather than a manual review.
  • Bring your own models — SatView ships with no detection engine baked in. Your administrator plugs in an open-source model, or one under its own specific license, through the Model Manager — upload, version and swap ONNX weights whenever you want. Nothing is locked to a vendor's model zoo, and SatView never bundles, sells or redistributes third-party AI models itself.
  • AI Vision — a multimodal model describes the scene and answers questions about it.
  • Natural-language query — plain English translated into catalog search parameters.
Where the models run matters. Inference happens on your Python backend, on your hardware. Imagery is not uploaded to a third-party inference API unless you explicitly configure one for AI Vision or natural-language query.
Object detection results over an airport: 99 objects detected and outlined on the map — 85 planes, 10 roundabouts, 2 bridges, a ground-track field and a soccer-ball field — with a confidence-threshold slider and a per-class checklist covering all 15 supported classes.

Object detection · 99 detections, confidence threshold and per-class breakdown

Exploitation

Reading the results

Time Series Viewer

Swipe comparison between acquisitions across a date range.

InSAR viewer

Reads back the coherence and displacement products as map layers.

Content search

Query the index of detected objects, with results on the map and in a list, or filtered to dark-vessel and recurring-entity candidates.

Intel reports

Generate a report and export it to PDF with cover page, tables and classification banner.

Now make it run without you

Monitoring rules turn this chain into a standing watch — re-acquiring on cadence, or retrying when cloud or quality fall short.

Autonomous monitoring Full feature catalog