A Python package for installing commonly used packages for geospatial analysis and data visualization with only one command.

Geo Assist is a spatial library to manage spatial data in-memory.

Earth Engine and Geemap: Geospatial Data Science with Python

A collection of 300+ examples for using Earth Engine and the geemap Python package

A collection of Jupyter notebooks for GEE Courses

A streamlit app template based on streamlit-option-menu

A streamlit multipage app template for geospatial applications

A Python package for installing optional dependencies for geemap and leafmap.

Interactive web apps created using geemap and streamlit

A Colab notebook for land cover mapping and monitoring using Earth Engine

Python tools for agricultural analysis powered by Google Earth Engine.

A collection of Earth Engine Apps created using geemap and voila

Python scripts for deploying Earth Engine Apps to heroku

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.

geemap with streamlit

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.

A template for building a mkdocs website

Evaluate Wildfire Environmental Impact and Assess Burn Severity Consequences using Cloud Based Geoprocessing via Earth Engine Streamlit App.

Spatial Data Management with Google Earth Engine

A streamlit web app visualizing global surface water datasets.

An Earth Engine web app developed using Solara and geemap

Interactive geemap tutorials on heroku

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.

A collection of Jupyter notebooks for geospatial applications

Interactively visualize and contextualize high-resolution spaceborne LiDAR data from NASA's ICESat-2 mission, using the OpenAltimetry API along with the Google Earth Engine Python API and the python package geemap for mapping.

A QGIS plugin for Earth Engine and geemap

Jupyter notebooks for the GEE book

这是一个 GIS & RS 开发学习记录仓库。

A multi-page streamlit web app template for geospatial applications

Random Forest classification tool using LANDSAT 8 for location-based risk analysis, featuring Google Earth Engine and interactive visualizations of Land Cover.