Forestry Engineering student building production-grade geospatial ML pipelines.
UAV photogrammetry · Sentinel-1/2 remote sensing · deep learning for forest inventory
Córdoba, Spain

The bottleneck in forestry isn't data — it's tooling. UAV imagery, Sentinel time series, LiDAR and NDVI are already free. What's missing are pipelines a forester can actually run, on real datasets, on hardware they own. That's the gap I work in.

I'm a forestry engineering student with a strong interest in geospatial analysis and machine learning. Forest management both generates and depends on very large volumes of data — UAV surveys, satellite time series, LiDAR, field inventories — and a good part of it stays underused for lack of accessible tooling. That is where I focus my work: building pipelines that bring technology into field data collection and make it faster, automate the processing and analysis of that data, and return results that are easy to interpret for specialists and non-specialists alike.

Most of it happens in the dehesas of southern Spain: flying the UAV campaigns, walking the plots, and then sitting down with the imagery. I enjoy working at that intersection between forestry and code, and I'm always glad to talk with people working on similar problems.

Instance segmentation of individual tree crowns on UAV orthomosaics. Validated on 14,506 ha → 357,185 trees detected, with documented box & mask precision/recall/mAP. Producer–consumer inference architecture that eliminates VRAM exhaustion on 4 GB laptop GPUs.

Sentinel-2 L2A pipeline on the free Copernicus CDSE API. AOI-driven scene search, cloud filtering, OAuth2 with exponential backoff, in-memory MGRS tile merging, and six spectral indices (NDVI, SAVI, EVI, NBR, NDRE, NDWI) with a validation dashboard.

Sentinel-1 + Sentinel-2 forest-disturbance monitoring: STAC-native Dask cube with omnibus Wishart change detection. Sierra de la Culebra wildfire → 6,380 ha of confirmed decline (33% of AOI), cross-validated between optical (ΔNDVI/ΔNBR) and SAR (VH backscatter drop).

Local, privacy-first image annotation platform (FastAPI + React + SAM 2.1). Three-level embedding cache runs on 4 GB VRAM; exports to YOLO-seg, YOLO-det and COCO. Benchmarked against Roboflow, CVAT and Label Studio for the local-first use case.

Internships in geospatial ML / precision forestry / remote sensing / computer vision (Spain or remote) · R&D collaborations on operational forest monitoring · Conversations at the intersection of forestry, earth observation & applied AI