FROM BLUEPRINT TO BOT: NEURAL NETWORKS PROVIDE DATA-DRIVEN DECISIONS FOR BIG DATA APPLICATIONS

In today's world, the construction industry is faced with the challenge of managing and processing vast amounts of data to make informed business decisions. However, this is no longer an insurmountable task thanks to the advancements in Artificial Intelligence (AI), Machine Learning (ML), and Neural Networks. These technologies have enabled companies like Lepei to develop innovative solutions that utilize raw data to extract valuable insights for the construction industry.

Lepei has developed a neural network that can create Building Information Models (BIM) and the company has gone even further by enabling the neural network to recognize and segment information for various business tasks. By analyzing data from videos, photogrammetry, and point clouds, Lepei's neural network can extract different types of information, which can then be processed to provide data-driven decisions for big data applications.

One example of the application of this technology is the Speedstac - Rebuild Ukraine project, which was initiated by Zenon Radewych. The project involved using drone survey data to evaluate the number of destroyed multi-story buildings, the number of destroyed apartments, their area, the number of people left homeless, the number of building modules to be produced for renovation, the number of investments needed for recovery, and the amount of construction waste to be recycled. All of this data was extracted and processed by Lepei's neural network to provide valuable insights for the project.

The physical space around us has become a huge knowledge bank for optimization and automation with the help of neural networks. By using Lepei's technology, companies in the construction industry can gain insights that were previously unavailable to them. Physical space can be transformed into a data source that can help optimize and automate various aspects of construction projects.

The process of extracting valuable information from raw data is complex, but Lepei's neural network simplifies it. The seven stages of the neural network, using the example of a simple house, are illustrated in the pictures provided by Lepei. They include drone video or photogrammetry, dense point cloud, semantic point cloud (different color per object type), object layers (exp: vegetation), object clustering (exp: vegetation), object processing, and finished BIM model.

Lepei's technology has proven to be a game-changer in the construction industry. It provides the ability to extract and process big data in a way that is not only efficient but also accurate. Companies that embrace this technology can gain a competitive edge by making data-driven decisions that optimize and automate their operations. As the construction industry continues to evolve, companies need to stay up-to-date with the latest technological advancements, and Lepei is leading the way in this regard.

www.lepei.pro