SiteMind v0.1.1 — Reproducibility archive

SiteMind v0.1.1 reproducibility archive

This release preserves the generated artifacts from the documented SiteMind perception, camera–LiDAR fusion and next-worksite planning run.

Asset

sitemind-experiment-artifacts-v0.1.1.tar.gz

• SHA-256: c031d334ac91b79f29ceaa429bd2271b6abd6f421199bca2fb2947e44acd8051
• Restores the complete retained artifacts/ tree except for duplicate neural-network checkpoints.
• Includes predictions, uncertainty maps, calibrated and temporal fusion results, evaluation reports, audits, sequence metadata, final GIFs and their per-frame JSON records.
• Includes the original training configuration, metric history and training log.

The two fine-tuned checkpoints remain available from the v0.1.0 release. External source data and official baseline weights are identified by URL and SHA-256 in docs/DATA_MANIFEST.md.

Selected frames and derived visualizations originating from GOOSE / GOOSE-Ex are provided under CC BY-SA 4.0 with upstream attribution. The archive contains no complete dataset archive or raw ROS bag.

SiteMind v0.1.0 — Fine-tuned checkpoints

SiteMind v0.1.0 checkpoints

This release publishes the two PP-LiteSeg checkpoints trained for SiteMind's camera-based terrain perception module.

Files

best_safety.pth

• Selected at epoch 11 by the validation safety score used in scripts/train_ppliteseg.py.
• Recommended for the conservative camera-to-BEV risk-mapping and planning pipeline.
• SHA-256: dbd5c83af8120136051f120ed7c114885b6c346ecdbe69ce2b05e83ea2f51430

best_miou.pth

• Selected at epoch 13 by validation mean IoU.
• Provided for semantic-segmentation evaluation and comparison.
• SHA-256: 7cbee2d7019170ea549a0b78072f8cb9ab97c6732af007e2e90e150a050d2b2c

Both files are full training checkpoints containing model, optimizer, scheduler and mixed-precision states. The model uses the 64-class GOOSE ontology and a 512 × 512 input resolution.

Training configuration

• Architecture: PP-LiteSeg B75
• Training data: GOOSE-Ex 2D training split
• Validation data: GOOSE-Ex 2D validation split
• Objective: equal-weight cross-entropy and Generalized Dice loss
• Optimizer: AdamW
• Epochs: 15
• Random seed: 42

The validation split was not used for parameter updates. Refer to README.md and docs/REPRODUCIBILITY.md for reported evaluation results, limitations and commands.

Attribution and terms

The checkpoints are derived from the GOOSE / GOOSE-Ex data and the PP-LiteSeg architecture. GOOSE is published under CC BY-SA 4.0. These fine-tuned checkpoints are provided under CC BY-SA 4.0; retain this notice and cite the original GOOSE / GOOSE-Ex and PP-LiteSeg publications when redistributing or using them.

The release contains no dataset images, labels or raw ROS bags.