ML Systems proposes shared data ontology to unify roof descriptions across trades and machines

A persistent communication gap exists between property assessors, computer vision models, and construction crews, each of whom describes the same roof using entirely different vocabularies and data formats. ML Systems argues this fragmentation worsens as more parties — including robotic systems — enter the workflow. The company proposes a 'Collective Ontology,' a structured data model that tags building claims under consistent code families and represents components as nodes with typed relational edges rather than flat material records. Central to the approach is the idea that a roof must be stored as an ordered stack of layers with fastening relationships, enabling correct sequencing for both construction and disassembly. The company's scheduling engine, REAPER, compiles job sequences as a directed acyclic graph, though the article acknowledges the system has not yet been validated on a real structure.