Computational Geoscientist · Physically-Based Modeling · Machine Learning · Remote Sensing · UAS
Medellín, Colombia 🇨🇴

I work at the intersection of computational Earth sciences, physical modeling, and artificial intelligence. Currently Remote Sensing Lead & Researcher at SIATA (Early Warning System of Medellín and the Aburrá Valley) and Adjunct Professor of Geotechnical Instrumentation at Universidad EAFIT.

My work centers on designing operational, real-time early warning pipelines that fuse physically-based numerical engines, weather radar grids, dense IoT telemetry, drone photogrammetry, and machine learning to monitor and forecast landslides and debris flows across the tropical Andes.

• M.Sc. Water Resources Engineering — Universidad Nacional de Colombia (2022)
• B.Sc. Geological Engineering — Universidad Nacional de Colombia (2017)

Project	Description	Stack / Highlights	Status
shalstab	High-performance Python implementation of the SHALSTAB physically-based landslide susceptibility model.	Python, NumPy, Numba, Rasterio, Rich CLI, PyPI
raptor	Containerized photogrammetry pipeline for DJI UAVs (M3T / H20T / M3M). Generates RGB, radiometric thermal, and multispectral orthomosaics with interactive Leaflet viewer.	Python, FastAPI, Leaflet, Docker, Computer Vision
trigrs-pywrap	Python wrapper and automation pipeline for the USGS TRIGRS Fortran transient infiltration and slope stability model.	Python, Fortran Wrapper, Geospatial Rasters
debris-flow-susceptibility	Machine learning and geospatial morphometry workflows for assessing torrential flow susceptibility in tropical basins.	Python, scikit-learn, GeoPandas, NumPy

• Multi-Hazard Early Warning Pipelines: Operational hazard forecasting models deployed at SIATA covering the Aburrá Valley metropolitan region.
• Sensor & Radar Integration: Ingestion of dual weather radars (C-Band / X-Band) and real-time environmental IoT networks into predictive hazard models.
• UAS & Remote Sensing: Photogrammetric UAV surveys in steep Andean catchments for 3D terrain modeling and hazard zonation.
• Academic Publications: Peer-reviewed articles on slope stability, climate change IDF scenarios, and debris flows (Google Scholar).

• Scientific Python & ML: NumPy, Numba, SciPy, scikit-learn, GeoPandas, Rasterio, FastAPI, R.
• Physics & Geohazard Engines: SHALSTAB, USGS TRIGRS, FLO-2D, OpenDroneMap, NetCDF/HDF5.
• Earth Observation & Remote Sensing: Weather Radar, InSAR/SAR, Multispectral & Radiometric Thermal UAV, LiDAR, Google Earth Engine, GDAL, QGIS, ArcGIS Pro.
• Engineering Standards: Modern packaging (pyproject.toml, PyPI), Automated CI/CD, Containerization (Docker), Strict Linting (Ruff).

_{© 2026 Federico Gómez Cardona · federicogmz.github.io}