Jorge Luis Rodríguez Galvis

I am a PhD candidate at King Abdullah University of Science and Technology (KAUST), specializing in remote sensing, machine learning, and biodiversity monitoring. My work focuses on developing scalable methods for environmental monitoring in arid ecosystems using satellite and UAV imagery, with an emphasis on integrating AI models to assess vegetation structure, species composition, and ecological change over time.

My academic background bridges geodesy and ecological remote sensing, and I am particularly passionate about the intersection of foundation models and environmental sustainability. My research combines field-collected biodiversity data with advanced AI pipelines to enhance ecological monitoring in data-scarce regions.

I also collaborate on developing automated workflows for vegetation biomass estimation and spectral unmixing, and I maintain open-source code for remote sensing preprocessing and machine learning training pipelines.

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Current Projects

• FLORO (Foundation Learning Of Remote Sensing Observations for Ecological Research)
  A multimodal, multitask Vision Transformer model trained on 400K+ remote sensing tiles to support ecological applications like vegetation classification and biomass estimation in drylands. Learn more →

• Historical Biodiversity Monitoring
  Using declassified KH-9 HEXAGON imagery to assess environmental change over 50 years, co-registering with modern UAV and SkySat data.

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Previuos Projects

• UAV-based Disease Mapping in Agriculture
  Fieldwork-driven multispectral monitoring of late blight in Solanum tuberosum, with custom classifiers and reflectance correction techniques. Learn more →

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Selected Field Experience

• Over 40 ecological sites surveyed across Saudi Arabia’s drylands
• UAV multispectral image acquisition, ground-truth sampling, and species-level annotation
• DSM and CHM modeling, vegetation community delineation, and biomass ground calibration

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Contact

📧 jorlrodriguezg@gmail.com
📘 Google Scholar
🔗 ORCID

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This website is part of my effort to share reproducible research and tools for the geospatial, biodiversity, and AI communities.