Construction Site Data Annotation for Safety and Progress AI

Construction AI starts with a reliable view of what is on site. Our object detection annotation covers workers, machinery, vehicles, materials, structures and temporary works, using bounding boxes, polygons and segmentation masks on site photographs, fixed camera feeds and video. Class lists, occlusion rules, and labeling standards are agreed with your team before production, so every site and camera angle follows the same schema.

Safety models need clear examples of who is protected and who is not. Annotators label helmets, high-visibility vests, gloves, safety glasses, harnesses, and footwear, and tag each worker as compliant or non-compliant according to your site rules. Careful handling of distance, partial views, and crowded scenes helps models flag genuine breaches and limits false alerts.

Worker detection models locate each person on site, and activity recognition models describe what they are doing. We draw bounding boxes around workers, track them across frames, and tag actions such as lifting, welding, climbing, operating equipment, and walking through active zones. This video annotation work supports productivity analysis and early identification of unsafe behavior.

Cranes, excavators, loaders, trucks, and concrete mixers each need their own class and tracking logic. Annotators label every machine and keep a persistent ID across frames, including when equipment is partly hidden or moves between camera views. For crane projects, we also track the hook block, so models can follow load movement in real time.

Building and structure models need to recognize what is being built, from foundations to façades. We label walls, columns, beams, slabs, openings, rebar, formwork, scaffolding, and finishing elements, using polygons and semantic segmentation services for precise boundaries. Component-level labels let models identify individual elements and compare them with design intent.

Progress monitoring models compare site images over time to show how far work has advanced. We annotate completed, in-progress, and pending elements across repeated captures of the same area, keeping labels aligned from one visit to the next. Consistent tagging gives project teams a clear, comparable record of change.

Drone imagery gives a wide view of large and hard-to-reach sites. We annotate aerial images with boundaries, structures, stockpiles, haul roads, vehicles, and site features, using polygons and segmentation masks. These labels support mapping, measurement, and monitoring models that work from orthomosaics and aerial video.

Material models identify what is stored, delivered, and installed on site. Annotators label and classify items such as steel, timber, concrete, bricks, pipes, aggregates and formwork, noting quantity-related features where your project requires them. Accurate material labels support tracking, stock awareness, and delivery checks.

Quality inspection models learn from images where defects are clearly marked. We label cracks, honeycombing, spalling, misalignment, surface damage, leaks, and incomplete work using polygons and defect classes defined by your engineers. Severity tags and consistent boundaries help models separate minor blemishes from issues that need action.

Road and infrastructure projects rely on precise labels for surfaces, markings, and structures. We annotate carriageways, lanes, curbs, drainage, bridges, culverts, barriers, and signage across images, video, and aerial data. Semantic segmentation and polyline labels give models clean geometry for inspection and condition assessment.

Earthwork models need to understand ground conditions and work areas. Annotators outline excavation zones, cut and fill areas, stockpiles, trenches, spoil heaps, and haul routes, using polygons and segmentation masks. These labels help models track earthmoving activity and monitor how site terrain changes.

Floor plans and blueprints hold the design information that site data must be compared against. We label rooms, walls, doors, windows, stairs, dimensions, symbols, and text, and extract annotations using OCR and layout labeling. Clean structured labels connect drawings to site imagery and support model training on design documents.

LiDAR and photogrammetry capture sites in three dimensions. Our 3D data annotation covers point cloud segmentation, 3D bounding boxes, and classification of structures, equipment, ground, and vegetation. Annotators work to your class definitions and review labels across scans, so models learn reliable geometry from complex scenes.

Safety zone models need to know where people may and may not be. We label exclusion zones, barriers, swing radii, edge protection, and restricted areas, and tag workers or vehicles that enter them. These labels help models raise alerts when someone crosses into a hazardous space.

Site access models track who and what enters and leaves a site. Annotators label vehicles, gates, access routes, and entry points, and track movement across frames. Where your project requires it, we label vehicle types and plates, supporting logging and traffic control.

Waste and debris models help keep sites clean and safe. We label skips, loose debris, packaging, offcuts, rubble, and other waste in site imagery, using polygons for irregular shapes. Clear class definitions keep labels consistent between general waste and reusable materials.

Utilization models estimate whether machines are working, idle, or moving. Annotators tag equipment states and activities across video, including loading, digging, hauling, swinging, and standing idle. These labels help models measure how effectively equipment is used across shifts and areas.

Long site recordings must be broken into usable segments. Our video annotation services cut footage into labeled sections, track workers, vehicles, and equipment across frames, and tag events and activities with timestamps. The result is human-annotated data that supports model training on real site video, from single cameras to multi-camera setups.