scatter_plot KOS Classify
Deep-learning point-cloud classification with a live 3D viewer, a 49-tool routine toolbox (ground, vegetation, buildings, noise…), fence & cross-section editing, macro pipelines, and on-box or local-GPU model training.
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Train a new model
Teacher–student training on procedurally generated synthetic scenes (no dataset needed). Runs on the server — larger runs take longer.
dataset Train on your labelled data
Upload classified clouds (.las/.laz/.ply/.pcd/.xyz) that already carry ground-truth class codes. They're split into Train / Validation / Test — 70 / 15 / 15, or 80 / 10 / 10 once you upload 20+ files — and used to train a real model (is_real_data). Runs on the server (CPU); for a GPU, use the Desktop app and pick this dataset folder.
Training runs
memory Local-GPU desktop & CLI
Train and classify on your own GPU, then push results here. Heavy models (RandLA-Net and the CUDA-only ones) run far faster locally. Download the app or CLI, sign in with a token, and pick your GPU.
Weights leave the server only through the app, are used in a temp folder, and securely wiped after each run. Results and any trained model are pushed straight back here.
key Access tokens
Paste one into the app/CLI to sign in. A token is shown only once — copy it immediately.