Client Success Story

Annotating Bird Nests in Drone Images to Train an Object Detection Model

15,000+

Images Annotated

95%+

Annotation Accuracy

Service

  • Image Annotation Services

Platform

  • Client’s Proprietary Annotation Platform
The client

A Digital Data Collection & AI Development Company

This German technology company specializes in digital data collection, analysis, and management solutions. Utilizing drone technology and AI-powered object detection, they enable asset-intensive industries (such as energy, telecommunications, construction, and real estate) to conduct faster inspections, better analyze infrastructure conditions, and make data-driven maintenance decisions. Their integrated approach combines aerial data capture with advanced software and machine learning to help clients manage large-scale infrastructure efficiently and safely.

CLIENT OBJECTIVE

An Automated Bird Nest Detection Model to Ensure Power Grid Safety

Bird nests on power grid infrastructure pose significant operational risks, including equipment damage, power outages, and fire hazards. Traditional manual inspection methods (to locate these bird nests and evaluate the danger they pose to the power lines) are quite labor-intensive, costly, and dangerous—requiring personnel to work at heights, near high-voltage equipment.

Compounding this challenge, many bird species are legally protected, requiring power companies to balance infrastructure maintenance with wildlife conservation. For example, regulations often prohibit disturbing nesting birds during breeding seasons, creating narrow maintenance windows and compliance complexities.

To address these dual challenges of safety and conservation, the client was developing an automated bird nest detection system that would enable efficient power grid monitoring and proactive maintenance planning—without disturbing natural habitats or putting workers at risk.

PROJECT REQUIREMENTS

Aerial Image Annotation Services

Our client needed drone image annotation services, specifically two dedicated resources for:

  • Classifying 15,000+ aerial drone images to determine nest presence (binary classification: nest present/nest absent)
  • Annotating nest locations by drawing precise bounding boxes around each identified bird nest
  • Maintaining consistency in labeling across diverse environmental conditions, tower configurations, and nest variations (size, appearance, placement)
  • Ensuring high annotation accuracy
OUR SOLUTION

A Structured Annotation Framework with Multi-Layer Quality Assurance

To deliver the high-quality training data required for the client's AI model, we implemented a systematic data annotation approach that prioritized accuracy, consistency, and scalability. All drone image labeling was conducted using the client's proprietary annotation platform.

1

Comprehensive Annotation Guidelines and Training

We began by developing detailed annotation guidelines in collaboration with the client, establishing clear criteria for identifying bird nests and marking boundaries. These guidelines included visual examples of various nest types, materials, and configurations, as well as edge cases (such as partially obscured nests, debris versus actual nests, and ambiguous scenarios). Our annotation team underwent thorough training on these standards to ensure uniform interpretation across all images.

2

Object Classification and Bounding Box Annotation

Each image underwent a two-step annotation process to capture both binary classification data and spatial location data:

  • Classification: Determining whether a bird nest was present or absent in the image
  • Annotation: (for images containing nests): Drawing precise bounding boxes around each nest's location, capturing the full extent of the nest structure
3

Multi-Tier Quality Assurance Workflow

To maintain the accuracy standards critical for AI model training, we implemented a rigorous quality assurance process:

  • A team of trained annotators labeled images according to the established guidelines.
  • The labeled dataset underwent cross-verification by secondary annotators to identify inconsistencies.
  • Senior reviewers conducted quality checks on completed batches, focusing on edge cases and areas of potential ambiguity.
  • All identified issues were documented and shared with the annotation team.
  • Annotation guidelines were refined as needed to address recurring challenges.
4

Handling Edge Cases and Ambiguities

For images where nest presence was unclear or debatable, we established an escalation protocol to resolve any discrepancies. Ambiguous cases were flagged and directed to subject matter experts for review, with decisions documented to maintain consistency in similar future scenarios. This approach minimized subjective interpretation while preventing annotation drift across the dataset.

Raw image

Raw image

Annotated image

Annotated image

Project Outcomes

15,000+ Images Annotated

With binary classification and bounding box coordinates.

95%+ Annotation Accuracy

Maintained across the dataset through standardized protocols.

Error Rate Below 5%

Meeting the accuracy thresholds required for safety-critical applications

Their structured approach meant we got reliable data we could train on immediately, without spending weeks cleaning it up ourselves. Great work.

- Project Manager

CONTACT US

Get High-Quality Annotated Datasets for AI and Machine Learning Applications

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Reach out to our team to know more, or request a free image labeling sample.