Autonomous Flying Training Data Annotations
We offer autonomous flying training data annotation solutions for incorporating semantic segmentation for drone mapping and imagery. We can create high-quality training data for your computer vision models through various annotation and labeling techniques.
2D Aerial View Imagery Mapping
2D aerial view imagery mapping involves annotating overhead images to label terrain, structures, obstacles, and navigation features. These detailed labels enable self-flying systems to understand their surroundings, plan safe routes, optimal flight paths, altitudes, takeoff, and landing zones for precise navigation and route planning.
Aerial View for Human Tracking
Aerial view human tracking involves annotating drone, satellite, and UAV imagery to identify and track human presence from above. These labeled datasets train computer vision models to accurately detect, monitor, and analyze human movement across large areas in real time. We label buildings, towers, trees, power lines, and restricted areas to train AI for collision avoidance and regulatory compliance.
Segmentation for Geo Sensing
Segmentation for geo sensing involves applying semantic segmentation to satellite, aerial, and drone imagery to label land features, infrastructure, and natural elements. These high-resolution annotations provide accurate training data for geographical models used in mapping, monitoring, and spatial analysis.
Airspace Compliance Annotation
Labeling controlled airspaces and geofencing data to ensure legal and operational compliance, improving AI performance across real-world detection and monitoring applications. This includes identifying safe landing areas based on slope, surface type, and clearance, enabling autonomous takeoff and landing.
Video Annotation for Object Detection
Video labeling for object detection involves labeling objects across video frames using bounding box annotation. This enables AI models to recognize, follow, and analyze moving objects over time, allowing for accurate motion detection and real-time understanding in computer vision systems.
Emergency & Edge-Case Annotation
We annotate rare events such as GPS failures, signal loss, or near misses to improve system safety and resilience. Our object localization with 2D polygon annotation involves precisely outlining uneven or complex-shaped objects in drone and satellite imagery. This technique enables AI models to accurately locate and distinguish objects with irregular boundaries, improving detection accuracy in aerial and geospatial applications.
Top Two Reasons to Choose Anolytics for Autonomous Flying Systems
Reason #1
There is a requirement for a huge amount of data to accurately train neural networks to integrate into the aircraft’s visual cortex. The computer-vision-based automated flying systems can work like a human pilot only if the computer vision systems are trained with high-quality data. Hence, we help in developing automated pilot systems using high-quality AI training data.
Reason #2
We have a dedicated team of annotators using human-in-the-loop approach for 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.
Interested in Working with Us?
In today's tech-driven world, a career in Artificial Intelligence (AI) can be highly rewarding. Join our team of Annotation Specialist, and be a part of the company that creates high-quality training datasets.
Learn More About Our Autonomous Flying
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Get in Touch with us
USA Office
16 Horseshoe Ln, Levittown, NY 11756, United States
Delivery Centers (India)
A-83, Sector-2, Noida, Uttar Pradesh
C-01, Sector-59, Noida, Uttar Pradesh
C-40, Sector-59, Noida, Uttar Pradesh