Abstract
Manual annotation remains the source of ground truth for point cloud semantic classification, but its tools assume a contributor who can download billions of points and run desktop software. We present a browser-based framework that lets contributors classify remotely hosted datasets they neither own nor download. Points are labeled with a volumetric brush, and only the octree nodes the brush boundary crosses are fetched: regions fully inside are classified without being loaded, so the cost of a stroke follows its boundary rather than the points it encloses. Edits are held in a small ledger file, separate from the source data, that survives the discarding and reloading of points a streaming viewer performs and can be applied back to the source files at any time. Contributors receive disjoint regions as URLs and work asynchronously, returning ledgers that a project manager merges; classified subsets can also be exported as tiled LAS/LAZ. Class schemas are user-defined and travel with the edits. Where geometry alone is ambiguous, contributors label through pose-registered survey photographs. We demonstrate the framework on cultural heritage stonework, post-fire aerial LiDAR, and coral reef photogrammetry. It is open-source and deployed in production across OpenHeritage3D, the ALERTCalifornia Atlas Archive, and OpenOcean3D.