🚀 I’m a Geospatial Software Engineer & Earth Observation Researcher working at the intersection of Earth Observation, cloud-native geospatial systems, AI, and Big Data.
I’m passionate about building scalable, interoperable, and open geospatial infrastructure that makes Earth Observation data easier to discover, access, process, and use.
🏢 Currently, I work as a Geospatial Software Engineer at EODC – Earth Observation Data Centre, Vienna 🇦🇹, where I contribute to the development of modern Earth Observation data-access infrastructure and geospatial services.
My current work focuses on STAC, openEO, OGC APIs, federated data discovery, cloud-native EO architectures, and scalable geospatial services.
Previously, I worked as a Researcher at the Institute for Earth Observation – Eurac Research, Bolzano 🇮🇹, where I developed scalable EO and climate-data workflows and contributed to European research initiatives including Horizon Europe interTwin and ESA-related Earth Observation activities.
🧠 My work spans research software engineering, geospatial data engineering, machine learning, climate modelling, and reproducible science. I enjoy translating research concepts into robust software systems and open-source tools.
🎓 I earned my Master’s degree in Geoinformatics and Spatial Data Science at the University of Münster, Germany 🇩🇪, under the supervision of Prof. Edzer Pebesma.
During my studies, I worked with the Spatio-Temporal Modelling Lab, focusing on reproducible geospatial workflows and open science. I also contributed Python implementations complementing the open-access book Spatial Data Science with Applications in R.
- 🛰️ Earth Observation data-access platforms
- 🔎 Federated STAC discovery & metadata harmonisation
- 🌍 openEO processing workflows
- 🗺️ OGC APIs – Features, Records, Maps, Tiles, Coverages & EDR
- ☁️ Cloud-native geospatial architectures
- 📦 STAC + Zarr + object-storage workflows
- 🤖 Agentic AI for Earth Observation workflows
- 🔬 Research software engineering & reproducible science
Geospatial & EO
STAC · openEO · EODAG · GDAL · Rasterio · GeoPandas · Xarray · Zarr · Dask · OGC APIs
Software & Data Engineering
Python · FastAPI · Docker · Kubernetes · REST APIs · S3/Object Storage · Git
AI & Scientific Computing
PyTorch · TensorFlow · Machine Learning · Deep Learning · Climate AI
I lead or have contributed to tools including:
High-performance machine-learning workflows for statistical and deep-learning-based climate downscaling.
Developed as part of the Horizon Europe interTwin project.
Dask-based implementations of openEO processes supporting scalable and Zarr-native Earth Observation workflows.
Automated generation of STAC metadata for Earth Observation raster datasets.
Exploring how LLM-powered agents can translate natural-language Earth Observation requirements into interoperable and executable openEO workflows, with an emphasis on portability across processing backends.
- 🌡️ Developed a two-stage machine-learning downscaling framework increasing SEAS5 climate-forecast resolution from approximately 30 km to 1 km
- 🛰️ Contributed to ESA-aligned cloud-native Sentinel data workflows
- 🔄 Developed
raster2stacto automate creation of FAIR and interoperable EO metadata - ☁️ Built scalable EO processing workflows using Python, Dask, Xarray, Zarr, STAC, openEO and object storage
- 🌍 Worked on Digital Twin infrastructure within the Horizon Europe interTwin project
- 🔎 Currently developing and contributing to federated EO discovery and data-access services
- 🤖 Exploring Agentic AI + openEO for natural-language-to-executable Earth Observation workflows
- 🎤 Presented research at EGU and IEEE IGARSS, alongside international Earth Observation workshops and conferences
I'm particularly interested in:
- Cloud-native Earth Observation
- Geospatial platform engineering
- Federated EO data discovery
- Semantic metadata harmonisation
- STAC & openEO ecosystems
- OGC API standards
- GeoAI & Agentic AI
- Digital Twins
- Open-source research software
- FAIR & reproducible science
