Ignacio San Miguel Sanchez, PhD

Machine learning scientist with a forestry background and 10+ years of experience across international consulting and academia, specializing in leveraging remote sensing data (Radar, LiDAR, Optical) and machine learning for environmental monitoring. His expertise spans disturbance mapping, forest recovery, land cover classification, and change detection analyses.

Before his current role, he spent nearly four years at Pachama as a Remote Sensing Scientist on the R&D team, where he developed and optimized critical carbon monitoring capabilities. His work included building end-to-end pipelines for Canopy Height Model (CHM) and Above-Ground Biomass (AGB) estimation—from data ingestion and preprocessing of multi-sensor remote sensing data to advanced modeling, validation, and accuracy reporting. He played a key role in bringing transparency to the carbon market by evaluating forest structure products and collaborating with cross-functional teams on carbon data quality.

Prior to Pachama, he worked as a consultant on diverse projects for the European Space Agency (ESA) and Canadian Space Agency, applying machine learning and data fusion techniques for large-scale forest and land-cover mapping. During his postdoctoral research, he built a SentinelHub web application for rapid, global farm-scale assessments and developed and validated ML and deep learning models in Python. His PhD research focused on mapping and regionalizing fire mortality patterns in Canada's boreal forests.

He holds a BSc, MSc, and PhD in Forestry, contributing to advancing remote sensing methodologies for environmental applications across academic and industry settings.

Outside of work, he can usually be found biking, skiing, camping, getting lost in the woods, or playing guitar.