Geo AI - Turn geospatial imagery data into powerful deep learning models
Detect objects, patterns, and change faster than ever before by managing the entire geospatial ML pipeline with our cloud-native platform.
Autonomously create & deploy ML models at scale
With Picterra you can create a scalable system to operate geospatial machine learning models in production and continuously develop them. Create deep learning models quickly and efficiently with help of smart annotation toolkit and user friendly UI. Leverage state of the art machine learning models architecture with the ability to customize the algorithms to your specific needs and benefit from best-in-class support from our team of experts.
1. Access to advanced detector settings
Accelerate model creation and production deployment by relying on a thoroughly tested and optimized standard detector settings. Experiment with advanced model settings such as backbone model, training tile size, or background sampling ratio.
5. Smart annotation & drawing tools
Intuitive model training toolbar that helps you structure all the steps required to create, train and deploy your custom model.
2. Auto-scaling on cloud infrastructure
Custom deep learning architecture optimized for geospatial imagery. Auto-scaling infrastructure with adjustable capacity to ensure predictable and efficient processing. Fast prototyping and large scale inference.
6. Model development & training UI
Easy to use web UI for no-code ML model training Import existing data using the API and advanced detector settings. Run your model on an entire image library with one click thanks to fully automated model production deployment.
3. Detector training report
Get insights into how your model is performing, understand accuracy across multiple classes and analyze your model behavior.
7. Optimized detection areas
Draw or upload existing detection areas. Ensure fast and accurate detectors by customizing the shape and size of detection areas. Useful when monitoring linear objects (e.g., railways, roads, rivers) or AOI scattered within the imagery.
4. Detector accuracy score
Assess performance of your model. Compute accuracy score with the help of representative accuracy areas and example regions your model will run on when deployed at scale.
8. Single or multi-detector workflows
Assign up to 10 classes in a single detector (instance & semantic segmentation). Achieve efficient and faster workflows when working with sub-classification or sub-categorization of objects within a specific class.