Machine Learning is being used in a new open source project

Machine Learning is being used in a new open source project

Build Change helps save lives in earthquakes and windstorms. Its mission is to prevent housing loss caused by disasters by transforming the systems that regulate, finance, build and improve houses around the world.

The Linux Foundation, the nonprofit organization enabling mass innovation through open source, today announced it will host the Intelligent Supervision Assistant for Construction (ISAC-SIMO) project, which was created by Build Change with a grant from IBM as part of the Call for Code initiative. The Autodesk Foundation, a Build Change funder, also contributed pro-bono expertise to advise the project’s development.

ISAC-SIMO packages important construction quality assurance checks into a convenient mobile app. The tool harnesses the power of machine learning and image processing to provide feedback on specific construction elements such as masonry walls and reinforced concrete columns. Users can choose a building element check and upload a photo from the site to receive a quick assessment.

“ISAC-SIMO has amazing potential to radically improve construction quality and ensure that homes are built or strengthened to a resilient standard, especially in areas affected by earthquakes, windstorms, and climate change,” said Dr. Elizabeth Hausler, Founder & CEO of Build Change. “We’ve created a foundation from which the open source community can develop and contribute different models to enable this tool to reach its full potential. The Linux Foundation, building on the support of IBM over these past three years, will help us build this community.”

ISAC-SIMO was imagined as a solution to gaps in technical knowledge that were apparent in the field. The app ensures that workmanship issues can be more easily identified by anyone with a phone, instead of solely relying on technical staff. It does this by comparing user-uploaded images against trained models to assess whether the work done is broadly acceptable (go) or not (no go) along with a specific score. The project is itself built on open source software, including Python through Django, Jupyter Notebooks, and React Native.

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