Data Labeling

What is data labeling?

Data labeling enables machines to gain an accurate understanding of real-world conditions and opens up opportunities for a wide variety of businesses and industries. Data labeling is the process of detecting and tagging unstructured data to structured datasets. The process can be manual or assisted by software. Data labeling is also used when constructing ML algorithms for autonomous vehicles, healthcare, and e-commerce space. Data labeling is an important part of data preprocessing for ML, particularly for supervised learning, in which both input and output data are labeled for classification to train ML models for future data processing.

Importance or purpose of data labeling?

Data labeling helps machines to learn certain patterns and correlate the results, and then use the data sets to recognize similar patterns in the future to predict the results. Humans are powering machine learning by data labeling, to train ML algorithms and the important & these important functions are assisted by professionals working in a Data Labeling company that ensures that data is accurately labeled for best results in the AI system.

The Data-labeling industry spreads globally and is part of every industry as we all know data labeling as the biggest obstacle to AI adoption in the industry and so Data Labeling serves as a huge part of AI. It has continued to grow well and It will grow more and more in the near future as well. 

Data categorization service

AI is going to make us work more productively, live longer, and have cleaner energy.” 

And in my view, it’s possible with the help of Qualitative Data Labeling services 

Advantage of data labeling

Helps to gain accurate results 

Helps to gain Accurate ML algorithms 

Helps to save time and improve accuracy 

  1. LIDAR Annotation and Labeling 

LIDAR stands for LIght Detection and Ranging. It is a remote sensing technology emitting light that travels around the object and back to the receiver creating points every time it hits the object building a 3D map of the entire scene. We help annotate or label cars, pedestrians, bicyclists, trees, animals, traffic lights, billboards, garbage bin’s, etc in this map by drawing bounding boxes or cuboids precisely to train the machine learning algorithms to interpret the world.

b) Bounding box annotation

Bounding boxes for object detection, classification, and localization in images and videos. In Bounding box annotation we train algorithms to detect the various objects on the streets like lanes, traffic, potholes, signals, and other objects. Bounding boxes are imaginary boxes drawn on an image, shape, or text and then we label the contents of the box to help a model recognize it as a distinct type of object. Bounding boxes are one of the most common ways that image or video annotation is done.

c) Polygon annotation

Human-powered polygon annotation for both irregularly shaped and coarse object training data in images and video. Learning Spiral is providing quality polygon annotation service that helps to annotate objects in angled photos and polygon shapes. Our annotators help to detect the exact shape of an object by drawing pixel-perfect polygons to meet your specifications.

d) Keypoint and Skeletal annotation

Our workforce annotates facial and motion to train the machine learning algorithms. 

Where we label the objects in an image using points to determine the shape of it. Key point annotation is used to label facial/skeletal features, automotive parts, etc. 

e) Semantic Segmentation

Semantic Segmentation a classic Computer Vision problem that involves taking as input some raw data (2D, 3D images) and labeling the regions of interest highlighted. Under Semantic Segmentation, we do the process of clustering various parts of images together belonging to the same object class. Leverage our fully-managed human-powered pixel-level image segmentation and annotation to build pixel-perfect semantic segmentation tasks at scale.

f) Geospatial imaging

Geospatial imagery encompasses aerial, drone imagery, satellite images. We help them built-in image analysis to assess and make business-critical decisions about their business. Whether that be their mapping farm, construction site, mine, real estate project, roof or crop insurance, disaster recovery situation, or forest.

Learning Spiral, Data Labeling company offers qualitative Data annotation & Data Labeling services and is here to Empower your algorithm with our human data labeling. Our ISO 27000 certified facilities are equipped to handle the most secure data, and our training data expertise helps reduce ramp time and increase quality.