3D data · Saudi Arabia & the UAE

Gulf road LiDAR point cloud data

3D point clouds of Gulf roads: city streets, highways, roundabouts and desert roads, by day and by night. Every frame is labelled with 3D boxes for cars, trucks, pedestrians, camels and road signs, and every object is tracked from frame to frame.

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Specification

Sensors: LiDAR point clouds with synchronised camera images and vehicle pose
Scenes: City streets, highways, roundabouts and desert roads; day, night, dust and glare
Labels: 3D bounding boxes, object classes, tracking IDs across frames, lane and road-edge lines
Classes: Cars, trucks, buses, motorcycles, pedestrians, camels, road signs and barriers
Formats: PCD or KITTI-style BIN point clouds, JSON labels
Quality check: Every frame is cross-checked by several trained reviewers

Example of the data

car	4.6 × 1.9 × 1.5 m	heading 87°
camel	2.2 × 0.8 × 2.0 m	heading 12°
truck	9.8 × 2.5 × 3.6 m	heading 91°

What it is used for

Autonomous driving perception
Train 3D object detection on the roads, vehicles and animals of the Gulf.
Driver assistance (ADAS)
Test emergency braking and lane keeping on local road layouts.
Smart-city traffic analytics
Count, classify and track road users at roundabouts and junctions.
HD maps
Extract lanes, road edges and signs for high-definition maps.

Questions

Which road scenes are covered?

City streets, highways, roundabouts and desert roads, by day and night, including dust and strong glare.

Which objects are labelled?

Cars, trucks, buses, motorcycles, pedestrians, camels, road signs and barriers, each with a 3D box and a track ID. Your own class list can be used instead.

Which formats do you deliver?

PCD or KITTI-style BIN point clouds with JSON labels, or the format your pipeline reads.

Can I see samples first?

Yes. Request samples with the form on this page and we will send them with the full specification.