With over two decades of experience, RMSI delivers high-quality, enterprise-grade annotation and labeling services tailored to your AI training needs. By combining skilled human annotators with automation and a robust, workflow-driven platform, we ensure accuracy, scalability, and efficiency at every stage.
From image labeling to creating complex, domain-specific datasets, we transform unstructured data into valuable insights – helping you build smarter AI models, faster and within budget.
Annotating images or videos with bounding boxes around objects of choice. Widely used to train autonomous driving prediction models for lanes, pedestrians, cyclists, vehicles, etc. Computer vision models for spatial cognition from 2D images or videos and Lane detection for AV’s.
Drawing polygons by identifying the exact shape of the object in aerial and satellite imagery. Usually used to mark the shape of an irregular object which cannot be accurately captured by bounding boxes with precision. Polygons are useful for creating training data sets for AI and machine learning in multiple industries.
Feature used for labeling GIS data like roads, lanes, highways, airstrips, rail road’s, coastal line etc. The most common application of lane annotation is for autonomous vehicle. By annotating road lanes and sidewalks, the autonomous vehicle can be trained to understand boundaries.
Semantic Segmentation is an aspect of image processing and computer vision process used to locate objects and boundaries defined in real world as two dimensional function. Semantic segmentation is the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc on roads, in retail shops.
We create geographic models using remote sensing technology, such as satellite, aerial, and drone imagery, to produce and curate training data for machine learning and computer vision models. Geo-annotation is useful for aerial view and drone imagery annotation, bounding box, semantic segmentation, etc.
Key Point annotation is used to label facial/skeletal features, automotive parts, etc. in an image. We label images using points to determine their shape. We provide Key Point annotation in human faces by marking facial features and joint positions using single points for emotion detection and facial recognition, such as for retail, supermarkets, and grocery stores.
As a prominent player in the development of self-driving technologies, the client sought to annotate dynamic objects from Camera and LIDAR data to generate reliable and high-quality AI training data, thereby...
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With the evolving infrastructure landscape, accuracy and data intelligence are important. The client is an AI innovator in infrastructure asset management, reimagining how large-scale data is collecte...
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With AI transforming the defense landscape around the world, nations are heavily investing in AI powered technologies to enhance their military capabilities. Recognising its potential in various aspects of w...
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