How does

OpenSpace

Use AI?

Reduces project risk and increases operational efficiency on construction sites.

Project Overview

Automated 360° reality capture and AI analysis for construction sites. The system maps imagery to floor plans, tracks progress, and provides actionable insights for project management.

Layman's Explanation

Imagine having a super-powered intern who walks a construction site every day with a 360° camera, perfectly organizes all the photos, and points out what has changed. OpenSpace does this automatically, creating a "Google Street View" for your building project so you can check progress from anywhere without getting your boots dirty.

Details

OpenSpace automates the creation of a comprehensive, visual digital twin for construction projects. Site team members simply attach a 360° camera to their hard hat and walk the jobsite. The platform's core technology, the Spatial AI Engine, takes over from there. It uses spatial computing techniques like SLAM (Simultaneous Localization and Mapping) to automatically process the video feed, stitch images together, and pin them to the correct location on project floor plans without any manual input.

Once the visual data is mapped, computer vision models analyze the imagery to identify and track the progress of installed components, such as walls, mechanical systems, and electrical work. This enables automated progress tracking and quantification, comparing the as-built conditions directly against the design models (BIM) to spot discrepancies early. The system continuously learns from each capture, improving its accuracy over time.

More recently, OpenSpace has integrated generative AI, fine-tuned on vast amounts of construction data. This allows stakeholders to ask natural language questions about the project (e.g., "Show me the status of drywall on the third floor") and receive summarized, contextual answers. This combination of spatial computing, computer vision, and generative AI transforms raw visual data into actionable intelligence, streamlining workflows for project managers, owners, and contractors.

Analogy

It’s like a fitness tracker for your construction project. Instead of counting steps and heart rate, it tracks installed drywall and electrical conduits, giving you a complete health dashboard of the building's progress and flagging any issues before they become major problems.

Machine Learning Techniques Used

  • Structure from Motion & SLAM: for automatically mapping 360° imagery to floor plans.
  • Large Language Models: for interpreting natural language queries and summarizing project data.
  • Transfer Learning: for fine-tuning general AI models with construction-specific data.
  • Object Detection & Tracking: for automatically quantifying the progress of installed materials.
  • Reinforcement Learning: for continuously improving the system's mapping and alignment accuracy over time.
  • Time Series Analysis: for predictive analytics to forecast potential project delays or risks.
  • More Use Cases in

    Construction & Real Estate

    4

    /5

    Novelty Justification

    The project combines multiple advanced ML techniques (SLAM, computer vision, LLMs) to solve a high-value problem in the traditionally tech-laggard construction industry. While competitors now exist, OpenSpace was an early mover in creating a comprehensive, automated digital twin solution, making its application highly innovative for the sector.

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