Data Annotation

Annotation or Labelling is the process of tagging or classifying objects in each frame captured by an AV / Sensor. This data then needs to be curated so that it is understood by the deep learning model, and relevant objects need to be identified and tagged or labeled.

ASPL offers annotation services primarily but not limited to the following industries:

Industries we serve:

Why ASPL?

At our core, we excel in handling large volumes of data with precision, making us stand out. Renowned names in automotive, retail, and railways trust us as their go-to partner for annotating diverse sensor data like LIDAR, RADAR, and Camera in various formats. Our dedicated team of over 500 experts ensures smooth project delivery, boasting an impressive 95% acceptance rate. We're known for our top-notch tools and efficient processes, guaranteeing quality and productivity.

Annotation - Automotive

Object detection and localization

Annotate objects in urban and street environments that are relevant to the autonomous vehicles.

Object tracking and scene understanding

Annotation of huge volumes of road scenes that enables you to train and detect moving object detection or autonomous driving.

Full pixel segmentation for street scenes

Each pixel assigned to the class of your selected objects will be annotated. It is therefore the closest to a true representation of reality in 2D space, regarding class assignments.

LiDAR 3D point cloud labeling

Transform your raw point cloud dataset into annotated images with bounding boxes around objects of interest. We can also do semantic segmentation of point cloud data.

Polygons and Polylines

Annotate object instances with polygons and polylines instead of pixel segmentation.

Event Labeling

Annotate events like lane change, overtaking, sudden braking on video data.

Traffic Signs & Signals

Annotate traffic signs and signals with character recognition.

Free Space

Annotate free space for eventual motion planning in autonomouzs driving.

Annotation - Additional Industries