SnA partners to implement AI and machine learning across computer vision, natural language processing, and content services by enhancing, annotating, and labeling data.
Geospatial data annotation entails preparing maps, satellite images, and other visuals using a variety of annotation techniques. This typically includes details about location, object characteristics, and numerous other attributes, enabling AI systems to interpret real-world phenomena within specific geographic regions. The process often involves annotating extensive datasets gathered from diverse sources and formats. This enriched data provides additional context, making it easier to comprehend various events. AI systems can detect visual patterns and extract insights from images that might be overlooked in large spreadsheets. Consequently, predictions become more accurate, faster, and simpler.
Expert annotators plot points on each vertex of the target object. Polygon annotation allows all of the object’s exact edges to be annotated, regardless of shape.
The SnA team divides images into smaller segments before annotating them. Their computer vision specialists identify target objects within images down to the pixel level.
SnA annotators classify images or objects within images based on custom multi-level taxonomies, including land use, crops, residential property features, among others.
SnA teams detect instances of semantic objects of a certain class in digital images and videos, providing a deeper understanding of the scene.
SnA teams annotate images and videos captured by multi-sensor cameras with 360-degree visibility to create precise, high-quality ground truth datasets.
Data annotators mark specific points within an image to identify its component parts. This technique is especially helpful for small objects and variations in shape by distributing dots throughout the image.
