Machine learning techniques for extracting floorplan elements from architectural drawings

One or more non-transitory computer readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:

• performing one or more operations via one or more machine learning models to identify that at least a portion of an architectural drawing includes a first image of a floorplan area by determining that text extracted from the architectural drawing corresponds to at least one predetermined keyword associated with floorplans;
• in response, extracting the first image of the floorplan area from the architectural drawing;
• and
• generating a boundary segmentation based on the first image of the floorplan area, wherein the boundary segmentation includes one or more boundary types for one or more portions of the floorplan area.

A system, comprising:

• one or more memories that store instructions, and
• one or more processors that are coupled to the one or more memories and,
• when executing the instructions, are configured to:
• perform one or more operations via one or more machine learning models to identify that at least a portion of an architectural drawing includes a first image of a floorplan area by determining that text extracted from the architectural drawing corresponds to at least one predetermined keyword associated with floorplans;
• in response, extract the first image of the floorplan area from the architectural drawing;
• generate a boundary segmentation based on the first image of the floorplan area, wherein the boundary segmentation includes one or more boundary types for one or more portions of the floorplan area;
• and
• generate a room segmentation of the floorplan area based on a vectorized representation of the boundary segmentation, wherein the room segmentation comprises a partitioning of the floorplan area into a plurality of regions representing a plurality of rooms.