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Achievement: StarstruckAchievement: Pull Shark

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@SERVIR @UAH-Lab-for-Applied-Science

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BzGEO/README.md

Emil A. Cherrington, Ph.D.

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My professional work blends Earth observation (EO) 🛰️ and data science 💻, as one does these days 🙂. While I've been dabbling in scientific coding 👨‍💻 for a decade, starting with R badge and Py badge in 2015, and moving to JavaScript for cloud computing with Google Earth Engine 🌎 (GEE) in 2017, I have been working professionally with EO and geospatial 🗺️ data since mid-2004. Over the years, I have applied EO and geospatial science specifically in the areas of land cover change detection, ecosystem monitoring, disaster response, and integrated water resource management, and I have supported the former NASA Earth Action Capacity Building Program for the better part of the previous twenty years. Most of the GitHub repos I am hosting ⬇️ are hence linked to that work. Across my career, I have worked with multispectral, thermal, hyperspectral, synthetic aperture radar (SAR), and LiDAR data [airborne + spaceborne], and I was one of the editors of the 2019 SERVIR-SilvaCarbon SAR Handbook 📔.

I currently serve as a Principal Research Scientist 👨‍🔬 with the Lab for Applied Science 🔬 within the Earth System Science Center of the University of Alabama in Huntsville (UAH), and I am also an affiliate member of UAH's graduate faculty. From 2022-2026, I was a Google Developer Expert for Earth Engine (before that GDE category was discontinued). I am an Early Adopter for NASA's PACE hyperspectral 🛰️ mission (for which I developed a toolkit), and also for the NASA-ISRO SAR (NISAR) 🛰️ mission. As of early 2026, I am also a member of the User Working Group for NASA's Ocean Biology Distributed Active Archive Center (OB.DAAC). My educational background 👨‍🎓 includes a [2016] double Ph.D. in forest ecology from AgroParisTech (France) and Technische Universität Dresden (Germany), a [2004] master's degree in forest resources from the University of Washington, and an undergraduate degree [2001] in biology from Loyola University Maryland. If you're so inclined, you can check out the recording of my Dec. 2016 doctoral dissertation defense, which combined the mapping of forest types with EO big data (i.e., geospatial machine learning, #GeoML / #GeoAI). 😉

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  1. GeoAI_compendium GeoAI_compendium Public

    References regarding geospatial artificial intelligence (#geoAI) and geospatial machine learning (#geoML)

    11 1

  2. bz_forest_mrv bz_forest_mrv Public

    Sample Google Earth Engine (GEE) scripts for forest monitoring for Belize, Central America

    JavaScript 4 2

  3. PACE_OCI_Toolkit PACE_OCI_Toolkit Public

    Google Earth Engine (GEE) JavaScript-based tools for working with NASA PACE OCI data

    JavaScript 19 4

  4. Hyperspectral_Toolkit Hyperspectral_Toolkit Public

    Google Earth Engine (GEE) JavaScript-based tools for working with various hyperspectral datasets (e.g., AVIRIS, EMIT, Hyperion, PACE OCI, Planet Tanager)

    JavaScript 5 1

  5. PACE_OCI_conversion PACE_OCI_conversion Public

    R scripts for converting PACE LandVI data from NetCDF to GeoTIF

    R 4

  6. Python script to convert from ICESat... Python script to convert from ICESat-2 ATL08 HDF data to shapefile. Usage: 'python icesat2_shp.py <path> --variables [<var1>,<var2>,...,<varN>] --outFormat <extension> --filterBounds [<W>,<S>,<E>,<N>] --verbose'
    1 
    # based on Kel Markert's GEDI HDF to shapefile conversion script
    2 
    # https://gist.github.com/KMarkert/c68ccf53260d7b775b836bf2e11e2ec3
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    import os
    
              
    
              
    
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    import fire
    
              
    
          
    
    
    
  
    

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