This article synthesizes insights from Kili Technology's recent geospatial AI roundtable discussion featuring industry experts from government, consulting, and technology sectors. Our panelists shared real-world implementation experiences, revealing critical gaps between AI promises and production realities while providing actionable guidance for organizations evaluating their geospatial AI strategies.

Geospatial data annotation requires far more than drawing bounding boxes on satellite imagery. As machine learning engineers develop advanced geospatial intelligence, annotation quality has become the critical differentiator for production model performance. This article explores advanced techniques that transform geospatial data labeling into a sophisticated discipline, covering multi-modal fusion, automation workflows, and validation methods for reliable AI systems.

Labeling geospatial data poses unique challenges due to complexity, variability, and geometric distortion. Manual labeling, automated labeling, and semi-automated labeling are common techniques used in the annotation process.