Aerial Image Annotation

Aerial images provide a unique perspective for understanding land use, infrastructure, vegetation, and environmental change. To train reliable computer vision models for these tasks, datasets must be annotated with a high degree of precision and consistency. Even small labeling inconsistencies can lead to unstable predictions once models are deployed.

DataVLab supports organizations working with aerial photos captured by drones, aircraft, or imaging platforms. We annotate objects, terrain features, man made structures, and environmental patterns with structured taxonomies designed for geospatial needs. Our workflows account for variations in altitude, angle, lighting, and geographic context.

Whether teams are building land classification models, construction monitoring systems, agricultural analysis tools, or large scale mapping pipelines, high quality annotation is essential for model accuracy. We combine trained annotators with multi level quality checks to ensure that each polygon, mask, or class is applied consistently across the dataset.

Aerial image annotation requires more than simple object labeling. Understanding boundary precision, geographic relationships, and changes across time plays a central role in how models interpret aerial content. Our team supports these requirements through clear guidelines, stable processes, and consistent reviewer feedback.