Reality Data Analysis is a service that runs Artificial Intelligence / Machine Learning (AI/ML) on photos, maps, meshes or point clouds. It can detect objects or features in 2D and 3D for defect analysis, image anonymization, image indexing, asset management, mobile mapping, aerial surveying, and more.
Reality Data Analysis (RDA) is articulated around Reality Data and Jobs. Reality Data are the data be to analyzed (photos, maps, meshes or point clouds). To describe on which data the analysis should be run and potentially some extra metadata (e.g. photo positions), we introduce a new type of Reality Data named ContextScenes. The analysis usually requires one or more Machine Learning models (e.g. a deep learning neural network). We call them ContextDetectors. Given ContextScenes and ContextDetectors, an RDA job produces different kinds of annotations (e.g. detected objects), which we store again in a ContextScene.
All the above mentioned reality data are stored via the Reality Data Service. The rest of the presentation assumes that you are familiar with the Reality Data API.
- The Context Scene description.
- What a Context Detector is.
- An overview of the different RDA job types.
Finally, stay tuned for incoming TypeScript and Python SDKs.