Geospatial & Mapping

Geospatial and mapping AI annotation labels satellite imagery, aerial photography, drone imagery, LiDAR point clouds, and other remotely sensed data so that AI models can learn to interpret Earth observation data for mapping, environmental monitoring, urban planning, infrastructure management, and intelligence applications. It includes land cover classification, building footprint extraction, road network mapping, object detection in overhead imagery, change detection between multi-temporal captures, and 3D surface modeling. Geospatial annotation requires understanding of remote sensing data characteristics, geographic coordinate systems, and the specific visual properties of each sensor type.

Geospatial annotation must preserve geographic accuracy that standard image annotation ignores. Building footprint polygons must match the actual building boundary at real-world coordinates, not just look visually correct in the image. Road centerline extraction must align with actual road geometry to support navigation applications. Land cover boundaries must be spatially precise enough to support area calculations and environmental reporting. This requires annotation tools that maintain geographic coordinate reference systems throughout the annotation process, and annotators who understand that spatial accuracy in real-world coordinates is as important as visual accuracy in the image frame.

GeoJSON is the standard format for vector annotation of geographic features with coordinate reference systems, covering points, lines, and polygons. GeoTIFF with associated label rasters is standard for semantic segmentation of aerial and satellite scenes. COCO JSON adapted for satellite imagery is used for object detection benchmarks. Shapefile (SHP) is common in GIS workflows. For change detection, temporally paired GeoTIFF images with change masks are standard. For 3D geospatial data, GeoLAS and LAS/LAZ formats are used for LiDAR point clouds. DataVLab delivers in any of these formats with validated coordinate reference systems and projection information.

Defense and intelligence geospatial annotation classifies military and dual-use objects in satellite and aerial imagery: military vehicles, naval vessels, facilities (production, storage, launch infrastructure), and activity patterns. This requires annotators with intelligence analysis training and, for classified programs, appropriate security clearances and controlled processing environments. European defense intelligence programs increasingly specify EU-sovereign annotation as a requirement, because geospatial intelligence data processed on non-EU infrastructure creates sovereignty and classification risk. DataVLab works with European defense geospatial programs under the appropriate sovereignty and security requirements.

Change detection annotation identifies differences between multi-temporal images of the same area: building construction or demolition, deforestation, flooding, wildfire burn scars, agricultural field changes, and facility modifications. For environmental applications, change detection datasets require annotation of pre-change and post-change imagery with clear labels indicating what changed, where, and when. For intelligence applications, change detection requires identifying facility modifications that may indicate activity changes. The annotation challenge is maintaining consistency between time periods despite differences in imaging conditions, seasonal vegetation changes, and atmospheric effects between captures.

DataVLab provides geospatial annotation for satellite imagery, aerial photography, drone imagery, LiDAR, and multispectral sensor data. We serve defense and intelligence programs, environmental agencies, government mapping organizations, commercial satellite operators, urban planning authorities, precision agriculture platforms, and infrastructure management organizations. EU-based annotation teams are available for European programs with sovereignty, GDPR, or defense classification requirements. We support all standard geospatial annotation formats with validated coordinate reference systems.