Annotating 2D aerial view imagery to produce high pixel mapping for enabling autonomous flying systems
Data Annotation to Enable Autonomous Flying Through AI
Anolytics offers autonomous flying training data solutions incorporating semantic segmentation for drone mapping and imagery from the top aerial view, making autonomous flying possible. We at Anolytics can create high-quality training data for your computer vision models through various annotation and labeling techniques:
2D Aerial View Imagery Mapping
Aerial View for Human Tracking
Labeling drones, satellites, and UAV images to develop training data for AI computer vision models for autonomous flight
Segmentation for Geo Sensing
Providing geographical models with training data support through semantic segmentation of satellite, aerial, and drone imagery
Bounding Box for Object Detection
Utilizing data annotations to outline objects of interest within an image for object detection using bounding box annotations
Video Annotation for Object Detection
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
Significance of Training Data for Flight Automation
The AI technology is groundbreaking and exciting for use in classical aircraft, helicopters, and the eVTOL aircraft of the future. In order to accurately train neural networks to integrate into the aircraft’s visual cortex, a great deal of data is required. These computer-vision-based automated flying systems can work like a human pilot only if the computer vision systems are trained with high-quality data. Anolytics can assist in developing completely automated pilot systems with high-quality AI training data.
Why Outsource to Anolytics?
Anolytics is a cutting-edge AI provider specializing in creating high-quality AI training data for autonomous flight and, in particular, computer vision systems for in-flight decision-making. With our dedicated team of annotators working in the human-in-the-loop approach to developing training data for flying systems. 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.
