What is Building Annotation?
Construction projects generate a sea of data. This data, often unstructured and complex, can be difficult to manage and analyze effectively. This is where building annotation comes in.
Definition of Building Annotation:
Building annotation is the process of adding tags or labels to construction data, making it understandable for both humans and, crucially, construction AI algorithms. Imagine trying to find a needle in a haystack – that’s what analyzing raw construction data can feel like. Annotating this data is like adding a bright flag to that needle, making it easy to locate and utilize.
Types of Data Annotation Needed for Construction AI:
Image and Video Annotation: Identifying and labeling objects in images and videos, such as structural elements, equipment, and potential hazards. This allows AI models to “see” and interpret visual data from construction sites.
Text Annotation: Extracting key information from documents like blueprints, contracts, and inspection reports. This helps in automating data entry, improving searchability, and enabling AI to understand the context of textual data.
