Labeling Objects and Assessing Conditions
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
Key Results
600+ images annotated
6000+ labels added
1000+ images classified
Project Overview
The project involved a series of image annotation tasks using the Labelbox tool, with a focus on precisely identifying and labeling a diverse array of objects within images. These objects included buildings, trees, cars, poles, containers, roofs, and empty lots. The task required meticulous attention to detail to ensure that each object was accurately labeled to support various applications, such as machine learning and computer vision models. This comprehensive approach aimed to enhance the quality of the annotated data, thereby improving the effectiveness of subsequent analyses and applications.
Business Problem
The project was designed to tackle the challenge of accurately annotating a wide range of objects within images to support multiple applications, including object detection, image segmentation, and quality assessment. This involved meticulous and detailed labeling of various objects with clear and tight boundaries to ensure precise identification. Specifically, the project required a careful classification of roof conditions and other features to aid in decision-making processes related to construction, urban planning, and infrastructure management. By addressing these needs, the project aimed to enhance the quality and utility of the annotated data, thereby supporting more effective and informed decisions in these critical areas.
Solutions Delivered to the Client
Each object within the images was meticulously annotated in accordance with the client's detailed specifications, resulting in high-quality labeled datasets that are crucial for machine learning applications. This precise and thorough annotation process ensured that every object was accurately identified and classified, meeting the highest standards of data quality. Additionally, empty lots were carefully segmented to delineate potential areas for construction, offering valuable insights and detailed information that could significantly inform and guide urban development projects. This comprehensive approach not only supported the client's immediate needs but also contributed to more strategic and effective planning in urban development and infrastructure projects.
People involved
Mariia
Project Manager
Iryna
Internet Research Expert
