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D0mP3dr0/README.md

Luis Felipe Comodo Seelig

Geospatial ML researcher. I build graph neural networks for Earth observation: terrain correction from sparse spaceborne LiDAR, physics-informed RF propagation over heterogeneous terrain, and mobile-device geolocation. M.Sc. student in Defense Engineering at IME (Brazil).

Every repository here is a reproducibility package for a manuscript: code, stamped results and the audit scripts that regenerate every reported number.

  • Published: Seelig & Salles, ARPIA: a multi-scale heterogeneous GNN for mobile-device geolocation, Ad Hoc Networks 189, 2026.
  • Tools: Python, PyTorch, PyTorch Geometric, GDAL/rasterio, QGIS.
  • Contact: s33l1g@gmail.com · LinkedIn

Popular repositories Loading

  1. dem-gnn-levelA-reproducibility dem-gnn-levelA-reproducibility Public

    Code of the study Graph Attention Networks for Multi-Task Terrain Reconstruction from Sparse Spaceborne LiDAR

    Python

  2. MovementGNN-COO MovementGNN-COO Public

    Reproducibility package for "MovementGNN: A Physics-Informed Graph Attention Network for Military Terrain Traversability Classification" (Defence Technology, 2026). Includes training scripts, analy…

    Python

  3. movementgnn-terrain-mobility movementgnn-terrain-mobility Public

    Reproducibility package: graph-based terrain mobility classification with predictive-entropy Transition Zone (MovementGNN)

    Python

  4. gnn-rf-blocked-evaluation-r2 gnn-rf-blocked-evaluation-r2 Public

    Reproducibility package for 'Physics-Consistent Multi-Target RF Field Reconstruction over Heterogeneous Terrain Graphs under Spatially Blocked Evaluation' (IEEE Access): code, results, and seed-42 …

    Python

  5. hermes-agent hermes-agent Public

    Forked from NousResearch/hermes-agent

    The agent that grows with you

    Python

  6. terrain-under-canopy-audit terrain-under-canopy-audit Public

    Audit scripts and stamped artifacts for 'Terrain Models Under Tall Amazon Canopy: Label and Reference Error Outweighs the Choice of Estimator' (IEEE JSTARS)

    Python