Raúl Carvajal Téllez

Developed Sentinel-2 and PRISMA-based algorithms and Dockerized pipelines to estimate inland lake water quality, implemented machine learning models and indices for pigment detection, and integrated outputs into an operational mobile monitoring database.

Applied CCDC and Pure Forest Index and estimated fAPAR with spectral proxies to monitor deforestation and ecosystem functional status in Caquetá, Colombia, supporting landscape-level conservation analysis.

Developed cloud, sun glint, and adjacency indices in Google Earth Engine to improve MODIS reflectance quality at multiple resolutions, enhancing inputs for machine learning water-quality models.

Developed GIS systems and interactive dashboards for COVID-19 monitoring, integrated public health datasets to identify vulnerable populations, and supported a health infrastructure monitoring system for hospital operations.

Developed a Google Earth Engine application combining Sentinel-1 and Sentinel-2 with ML classification to enable autonomous community monitoring of deforestation and forest-cover change.

Performed territorial GIS analyses and managed geodatabases to support Leticia's land use planning update, designing participatory methods and data-science approaches to prioritize local concerns.

Led RPAS-based orthomosaic generation and community boundary delineation using participatory methods to produce validated land tenure maps that strengthened territorial autonomy.

Master in Spatial Information Applications focusing on spatial data methodologies for environmental and resource applications.

Degree in Geographical and Environmental Engineering covering geographic information systems and environmental applications.

Postgraduate coursework in Remote Sensing and GIS applied to the study of natural resources and agricultural production.