600+ images annotated, 6,000+ labels added

Overview

The client needed accurately labeled image data to support applications such as object detection, image segmentation, and quality assessment. The project focused on identifying and classifying a wide range of objects and features within images.

We annotated objects, including buildings, trees, cars, poles, containers, roofs, and empty lots, while also classifying roof conditions. The resulting labeled datasets provided structured visual data for machine learning, computer vision, and infrastructure-related applications.

The challenge

Complex visual data — images contained a wide range of objects that needed to be identified and labeled accurately for different applications.
Precise object boundaries — annotation required detailed labeling and clearly defined boundaries to ensure reliable object identification.
Condition assessment — the project also required classification of roof conditions and other visual features to support construction, urban planning, and infrastructure decisions.

What we did

1 Identified image objects — reviewed images to locate relevant objects including buildings, trees, cars, poles, containers, roofs, and empty lots.
2 Applied detailed annotations — labeled individual objects according to the client's specifications, maintaining clear and precise boundaries.
3 Classified visual features — classified images and roof conditions to provide additional information for infrastructure and planning applications.
4 Created structured labeled datasets — organized the annotations into high-quality datasets suitable for machine learning and computer vision applications.

Results

600+ images annotated — creating structured visual data across a diverse range of objects and conditions.
6,000+ labels added — providing detailed annotations for individual objects and features within the images.
1,000+ images classified — adding classification data to support analysis of visual conditions and characteristics.
High-quality labeled datasets — producing structured image data to support machine learning, computer vision, and infrastructure-related applications.

Our methodology

How we structure visual training data

High-quality image annotation requires consistent object identification, precise labeling, and classification according to clearly defined requirements.

Object identification

Images are reviewed to identify relevant objects and visual features within each frame.

Precise image annotation

Objects are labeled according to client specifications with clearly defined boundaries.

Condition classification

Relevant visual characteristics, including roof conditions, are classified to provide additional information for analysis.

Dataset quality control

Completed annotations are reviewed to maintain accuracy and consistency across the resulting datasets.

The Tinkogroup team excelled in their performance, delivering outstanding results with exceptional accuracy. Their meticulous attention to detail ensured high-quality outcomes, while their communication was consistently clear and effective throughout the entire process. This seamless interaction and precision in their work significantly contributed to the success of the project, highlighting their professionalism and commitment to excellence.

— Spokesperson, Software Company