Back To Projects
Back To Projects

Structural damage classification of civil structures for a global oil and gas conglomerate

SUPA's experts scaled client's data annotation, accurately annotating 12,000+ images to boost damage classification workflow.

Problem statement

The Client required a specialised team to identify and assess damage of civil structures with a high accuracy of at least 90%.

SOLUTION

SUPA’s engineering experts collaborated closely with the Client by co-creating the annotation workflow and assembling a team of annotators experienced with engineering-related projects.

RESULT

SUPA’s team of 25 annotators successfully delivered the annotations with a consistent >90% accuracy.

Overview

The Problem

The Client is an integrated Oil & gas company looking to build an automated damage classification model that could:

  1. Identify the structure of interest within an image.
  2. Detect any structural damage and classify the severity of the damage based on the inspection team’s internal best practices.

The Client does not have an internal labeling team. Additionally, their previous 3rd party vendor struggled with generating annotations with a minimum accuracy of 90%.

The Solution

SUPA recognized the importance of understanding the task’s engineering context and the open-endedness associated with assessing structural damage. The team:

  1. Collaborated with the client to co-create labeling instructions and workflow: Our project managers worked closely with the client’s civil engineers through Proof-of-Concepts to curate a scalable and comprehensive workflow.

  1. Deployed expert annotators: Only annotators with proven experience in engineering projects were assigned to this project to ensure consistent, high-quality annotations.

  1. Ran a continuous feedback loop: Project managers conducted weekly feedback meetings to address new edge cases and expand the team’s knowledge base.

The Result

SUPA successfully delivered all the data required by the Client, completing over 22,500 annotations at a consistent >90% accuracy.

This enabled the Client to enhance their classification model, enabling a faster, safer, and more accurate inspection process.

Why SUPA?

SUPA’s engineering experts go beyond generic annotation work. They understand the underlying context of the task, working closely with the Client from Day 1 to build a tailored work process to successfully deliver the annotation project.

Other Projects

Discover the work we do

View All
View All

Data labeling for autonomous vehicle training

Discover how SUPA's specialized data labeling services enhanced an autonomous driving company's models, achieving 95% accuracy

problem
The client needed to enhance their autonomous driving model's accuracy in interpreting vector spaces to meet safety standards and effectively navigate diverse environments.
solution
SUPA provided expert data labeling services, combining rigorous annotator training and human-machine collaboration, along with a dedicated quality control team to ensure high-quality, consistent data.
result
SUPA continues to deliver labeled data to the Client with 95% accuracy, significantly improving the client’s model predictions and meeting tight delivery timelines since 2022.
95% accuracy
Autonomous Vehicles
Computer Vision
Data labeling for autonomous vehicle training

STEM Dataset

Bilingual Multimodal STEM Dataset — a curated collection of 500 Math and Physics questions in Malay and English, some enriched with relevant images.

problem
AI models often struggle with bilingual and multimodal STEM tasks due to a lack of high-quality, domain-specific datasets in languages like Malay and English.
solution
We created a curated dataset of 500 Math and Physics questions in Malay and English, complemented by a public leaderboard to benchmark AI model performance.
result
AI teams now have a reliable resource for fine-tuning and evaluating models on real-world STEM tasks, setting a new standard for bilingual and multimodal AI development.
500 high-quality Math and Physics questions
Evaluation Leaderboard
STEM-focused AI evaluation
STEM Dataset