Remote Sensing Specialist and Geospatial Data Engineer at Agreena. I design and build production systems that process satellite data at scale, integrating multi-sensor observations with ground truth to deliver monitoring products for agriculture.
Most of my professional work lives in private repositories. I'm building portfolio projects here to demonstrate similar technical capabilities using public data.
Infrastructure & Engineering STAC, PySTAC, odc-stac, FastAPI, AWS (S3, EC2), Ray Anyscale, Docker, Python
Remote Sensing & Analysis Sentinel-1/2, Landsat, MODIS, LiDAR, land cover/land use data, deforestation datasets, water/hydrology data, xarray, rasterio, GDAL
Machine Learning & AI PyTorch, scikit-learn
Building open-source projects demonstrating production geospatial engineering:
- STAC API implementation for satellite data access
- React-based satellite imagery visualization
- Multi-temporal NDVI analysis with statistical methods
- Land cover classification using machine learning
- LiDAR-derived terrain and hydrological analysis
- Kubernetes and Terraform infrastructure
MSc Geoscience from University of Copenhagen.
Agreena (2022-present): Satellite data infrastructure, MRV systems, and agricultural monitoring products. Built STAC-native platforms, rotation-bias-corrected productivity analysis, multi-modal analysis pipelines, and LLM-powered agricultural insights.
QEye (2019-2022): QI Geophysicist, data scientist, and project manager. Time series analysis and software prototyping for geophysical applications across Copenhagen, Australia, and Malaysia.
LinkedIn: https://www.linkedin.com/in/p-kongstad/ Email: kongstad25@gmail.com
Copenhagen, Denmark (relocating to Seattle, End of 2026)