Outdoorclassificatie

Outdoorclassificatie refers to the systematic categorization of outdoor environments, objects, or phenomena according to predefined criteria. The term is used in Dutch-language sources to describe methods that span geography, environmental science, urban planning, and remote sensing. It covers both image-based classification and sensor-based tagging of outdoor spaces.

Approaches rely on multiple data sources such as satellite imagery, aerial photography, drone footage, street-level images, and on-site sensor networks. Classification methods range from rule-based systems to supervised and unsupervised machine learning, up to contemporary deep learning models. The typical workflow includes defining the class scheme, collecting labeled data, training models, and validating performance, often with metrics such as accuracy and Cohen’s kappa.

Common classification schemes identify land cover types (forest, water, urban, agriculture), land use, vegetation density, weather or climate indicators, and safety or risk levels for outdoor facilities. Applications include environmental monitoring, urban planning, park and recreation management, tourism and outdoor recreation planning, and inventories of outdoor advertising assets.

Challenges include seasonal and weather-related variability, lighting changes, occlusion, scale differences, and data gaps. The need for standardized classifications and interoperable data models is important for comparability across projects. Privacy, consent, and ethical considerations may arise when collecting imagery in public or semi-public outdoor spaces.