Datalabeler provides satellite imagery data sets with annotated images to make the varied objects recognizable from the Aerial view , at sky level heights , Drone images etc
Bounding Box – Utilizing data annotations to outline objects of interest within an image for object detection using bounding box annotations
Video Annotation – Using annotated lines to capture each object in the video so that computers or machines can recognize the moving objects.
Objects Localization with 2D Polygon – Using the polygon annotation technique to annotate unevenly shaped objects in drone and satellite imagery for object localization
Object counting is one of the most common ways to use an object detection model. A newsroom might use object counting to estimate the size of a crowd in an ongoing protest, while a retailer may use a similar model to predict the hours with the highest level of footfalls in a street . Other uses of object detection models include detecting and monitoring animal populations and identifying the valuation of houses for bank loans.
Inspections are now characterised by drones, especially in industries. We label people, objects and other field equipment in aerial images captured by drones using various annotation techniques.
We analyse pre and post aerial images of disaster-hit locations and label structures like buildings, ports, utility sheds, etc. for enabling smart and effective disaster management projects.
We analyse aerial images of agricultural fields captured by drones and label them using semantic segmentation technique.
Datalabeler is a cutting-edge AI provider specializing in creating high-quality AI training data sets. With our dedicated team of annotators working 24/7 developing datasets for GIS based projects. We ensure consistency in interpreting edge cases across the images where we classify every pixel in images containing buildings, flat surfaces, high and low vegetation, wires, masts, pedestrians, vehicles, etc.