schatterje22

BIOMEplot

R package to plot Whittaker' biomes

BQR

Bayesian Quantile Regression.

BWF_Synthesis

ecoforecastR

General statistical and informatic tools for ecological forecasting in R

gEDA

A general approach to EDA

GEE_xtract

GEE code for extracting high-quality remotely sensed data at multiple scales

ggplot2-book

ggplot2: elegant graphics for data analysis

joint-ES-estimate

This repository offers the R script used in Rillig et al. (20XX) to estimate joint effect sizes using bootstrap resampling, to estimate p-values, and to visualize them.

LPDAAC-Data-Resources

This repository is a place to find data user resources that demonstrate how to use LP DAAC tools, services, and data.

machine-learning-book

Code Repository for Machine Learning with PyTorch and Scikit-Learn

schatterje22/BQR

Bayesian Quantile Regression.

schatterje22/gEDA

A general approach to EDA

schatterje22/GEE_xtract

GEE code for extracting high-quality remotely sensed data at multiple scales

schatterje22/LPDAAC-Data-Resources

This repository is a place to find data user resources that demonstrate how to use LP DAAC tools, services, and data.

schatterje22/mapping_demo_R

Demo - Some basics of mapping in R

schatterje22/Online_R_learning

Online R learning for applied statistics

schatterje22/swirl_courses

:mortar_board: A collection of interactive courses for the swirl R package.

schatterje22/XAI-tool4GEE

This is the repository for the paper titled, "Enhancing Land Cover Mapping and Monitoring: An Interactive and Explainable Machine Learning Approach using Google Earth Engine".

schatterje22/2017_LI-COR

schatterje22/animated-fire-map

In this repo, I will show you how to use R to access, process, and animate NASA’s Fire Information for Resource Management System (FIRMS) data

schatterje22/ARSET_ML_Fundamentals

Repository for Jupyter Notebook examples associated with the NASA ARSET Training, "Fundamentals of Machine Learning for Earth Science"

schatterje22/CTSM

Community Terrestrial Systems Model (includes the Community Land Model of CESM)

schatterje22/EB1A

EB1A Full Application - I-140 and I-485

schatterje22/ee_operationalization_demo

schatterje22/FriendsDontLetFriends

Friends don't let friends make certain types of data visualization - What are they and why are they bad.

schatterje22/GEDI-Data-Resources

This repository provides guides, short how-tos, and tutorials to help users access and work with data from the Global Ecosystem Dynamics Investigation (GEDI) mission.

schatterje22/generative-ai-for-beginners

12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

schatterje22/GeostatsPyDemos

Well-documented demonstration workflows with the GeostatsPy package.

schatterje22/GoogleEarthEngine

schatterje22/Groundnut_yield_prediction

Machine Learning project on Groundnut Yield Prediction

schatterje22/HLS-Data-Resources

This repository provides guides, short how-tos, and tutorials to help users access and work with Harmonized Landsat Sentinel-2 (HLS) data.

schatterje22/Mapping_and_data_viz_with_Python

My interactions with the course "A comprehensive guide for creating static and dynamic visualizations with spatial data" by Ujaval Gandhi

schatterje22/NASAaccess

NASAaccess R pkg generate gridded ascii tables of climate (CIMP5) and weather data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g., SWAT, VIC, RHESSys, ..etc).

schatterje22/OGH2023

OpenGeoHub 2023 Workshop: "Unsupervised classification (clustering) of satellite images"

schatterje22/R-package-tutorial

schatterje22/r-pharma-2023-tidymodels

R/Pharma 2023 Conference workshop materials.

schatterje22/RClimDex

Simple R package for ETCCDI/CRD climate change indices calculations

schatterje22/rgeeExtra

Extension for rgee

schatterje22/tensorflow-eo-training-2

A workshop taught in 2023 for NASA SERVIR, ACCA, and members of other environmental organizations in South America

schatterje22/WMS-to-Raster-

I geospatial world we come across many WMS layers ,it would be easy when we can convert WMS layer in RGB form to the original data sets . The Python code can be used for converting WMS to Raster using a shapefile and the height and width of the required Raster .