Optimizing Energy Consumption for the Real Estate Market with Computer Vision
In this 8-week challenge, 50 AI engineers collaborated to develop a solution for identifying a building’s different types of elements from 2D images.
This challenge requires experience in Computer Vision, Object Detection, and Machine Learning.
There is a high cost and long duration to complete a city permit compliant energy model report; it requires at least three months of manual labor for a team of 4-5 people. The current process deters real estate developers from focusing on emission reduction, and the high cost of the model discourages smaller real estate developers from entering the market. This project aims to improve the efficiency of this manual effort by identifying different types of building elements (walls, railings, etc.) in the 2D image.
The project goals
This project aims to create an AI system that can accurately identify different types of elements (walls, railings, etc.) in the 2d image.
The partner will provide the dataset with the bounding boxes of the relevant type of elements manually drawn.
Why join? The uniqueness of Omdena AI Challenges
A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, preparation, and modeling for deployment.
And the best part is that you will join a global and collaborative team of changemakers.Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.