Earth Engine and Geemap: Geospatial Data Science with Python
Interactive web apps created using geemap and streamlit
A collection of 300+ examples for using Earth Engine and the geemap Python package
A Python package for installing commonly used packages for geospatial analysis and data visualization with only one command.
Python scripts for deploying Earth Engine Apps to heroku
A streamlit multipage app template for geospatial applications
A streamlit app template based on streamlit-option-menu
A collection of Jupyter notebooks for GEE Courses
A collection of Earth Engine Apps created using geemap and voila
Geo Assist is a spatial library to manage spatial data in-memory.
A Python package for installing optional dependencies for geemap and leafmap.
Evaluate Wildfire Environmental Impact and Assess Burn Severity Consequences using Cloud Based Geoprocessing via Earth Engine Streamlit App.
Assessed the performance of Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms for land cover classification in a predominant agricultural landscape using the fusion of time-series Sentinel-1 and Sentinel-2.
Python tools for agricultural analysis powered by Google Earth Engine.
Interactive geemap tutorials on heroku
This course introduces beginners to Google Earth Engine using Python in Google Colab. Explore remote sensing, process geospatial data, create dynamic visualizations, and understand drought indices. Develop skills for environmental monitoring and decision-making without needing local installations.
An Earth Engine web app developed using Solara and geemap
Ejecicicos en español sobre Jupyter Notebook mediante el paquete de Python, ipyleaflet, y ipywidgets: "geemap", para el mapeo interactivo en Google Earth Engine-GGE, de incendios forestales.
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