Picterra
Data labeling software
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What is Picterra
Picterra is a geospatial AI platform used to create and run detectors on satellite and aerial imagery to identify objects and patterns. It supports teams that need to label and review features in large raster datasets for use cases such as land-use monitoring, infrastructure mapping, and environmental analysis. The product combines annotation workflows with model training/inference and map-based visualization, oriented around remote-sensing imagery rather than general-purpose image/video labeling.
Geospatial-first annotation workflow
Picterra is designed around satellite and aerial imagery, with map-based navigation and geospatial context that fits remote-sensing labeling tasks. This reduces friction compared with general-purpose labeling tools when users need to work with large areas, tiles, and geographic coordinates. It is well suited to object detection and feature extraction in earth-observation datasets.
Integrated training and inference
The platform pairs labeling with detector creation so teams can iterate from annotations to a working model in the same environment. This supports rapid experimentation and validation on new areas without exporting data to separate systems for every iteration. It can be useful for operational workflows where detection results need to be reviewed and refined continuously.
Collaboration and review features
Picterra supports multi-user workflows for creating, reviewing, and improving labeled datasets and detector outputs. Review-oriented workflows help standardize labeling quality across analysts working on the same geography. This aligns with common needs in geospatial programs where subject-matter experts validate results.
Narrower than general labeling
Picterra focuses on geospatial imagery and related detection workflows, so it may not cover broader modalities such as video, audio, text, or complex multimodal labeling. Teams needing a single platform for many annotation types may require additional tools. Some advanced computer-vision annotation features common in general-purpose platforms may not be the primary focus.
Geospatial data preparation overhead
Remote-sensing projects often require preprocessing (tiling, projections, band handling, and data sourcing) that can sit outside the core labeling UI. If an organization lacks established geospatial data pipelines, onboarding can take longer than with standard image datasets. Integration effort may be needed to connect imagery repositories and downstream GIS/ML systems.
Vendor information not fully verifiable
Publicly verifiable details such as current headquarters location and official social links are not consistently available across reliable sources. Without confirmation from the vendor’s official channels, some company metadata cannot be stated with high confidence. This can complicate due diligence for procurement teams that require fully verified corporate records.
Seller details
Picterra SA
Lausanne, Switzerland
2016
Private
https://picterra.ch/
https://www.linkedin.com/company/picterra/
