Lamarr.ai
An autonomous solution for building envelope diagnosis using aerial data and AI.
Idea in Artificial Intelligence
Introduction
Lamarr.ai brings an autonomous software solution to rapid building envelope diagnostics using cloud-based computing for image analysis, photogrammetry, and building energy simulation to reduce audit errors, life-threatening conditions for inspectors and critically improve cost and time efficiency. The solutions we provide are: Infrared (IR) image segmentation, envelope thermal anomaly diagnostics and feature detection fully autonomously using data captured by UAVs, to identify building performance issues early, rapidly, accurately, safely and cost effectively.
Problem
More than half of all U.S. commercial and residential buildings were built before 1980, and this building stock performs with general lower efficiency. New buildings that may have construction defects that deteriorate are also not sustainable. To address make these buildings more sustainable, auditors need to diagnose problems and design solutions. Challenges to the associated diagnostic process include i) inaccessibility to areas such as roofs, ii) significant time-consuming inspection activities, with possibility of human error, and iii) life-threatening settings for detailed inspection. Lamarr's innovative cloud-based image analysis from drone platforms solves these auditing problems by comprehensively informing retrofitting design decisions.
Opportunity
Lamarr.Ai provides a novel technology for remote building envelope data collection and diagnostics, to be used as a web-based software to support virtual audits to rapidly, accurately, and safely inform energy retrofits of existing buildings. The technology uses an Unmanned Aerial Vehicle (UAV – a.k.a. drone) platform, equipped with infrared sensors and onboard processing, which autonomously detect heat transfer anomalies and assess envelope material conditions swiftly and precisely using Computer Vision (CV) and Machine Learning (ML) techniques.