kohdeAn

kohdeAn is a framework and data model intended to standardize the annotation of targets within multimodal datasets. It provides a flexible structure for representing targets across visual, textual, and audio domains, including identifiers, type, spatial or temporal location, attributes, and provenance. The name combines the Finnish word kohde, meaning target, with An, a common abbreviation for annotation or analytics, reflecting its purpose as a target annotation scheme.

Data model and format: The core concept is a schema that defines target types and their properties. An annotation item, or target, carries a unique id, a type (for example object, event, or concept), location data (bounding boxes, segments, or timestamps), attribute maps, and a traceable history. The canonical representation uses a JSON-based schema with optional YAML metadata, enabling easy interchange between tools.

Tools and workflow: kohdeAn includes components such as a validation engine, an editor, and converters to and from other common labeling formats. Validators check schema conformance, consistency of location data, and provenance completeness. The workflow emphasizes reproducibility and traceability, with support for versioned datasets and annotation lineage.

Applications and relevance: The framework is used to structure labeling tasks in computer vision, natural language processing, and multimodal research. Its emphasis on interoperability and extensibility makes it attractive for collaborative projects and for benchmarking labeling methodologies. Adoption remains uneven, with some labs and industry groups experimenting with kohdeAn while others rely on established formats.

Limitations and future directions: As with any standard, widespread adoption requires community agreement on schemas and semantics. Challenges include balancing expressiveness with simplicity, handling complex or hierarchical targets, and maintaining compatibility with legacy datasets. Ongoing work focuses on expanding type catalogs, refining validation rules, and integrating with data governance practices.