This tutorial shows you how to retrieve basic properties (metadata) of an Earth Engine image and how to calculate descriptive statistics (e.g., min, max, mean, std, sum). Requirements geemap - A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets Installation conda create -n gee python=3.7 conda activate gee conda install mamba -c conda-forge mamba install geemap -c conda-forge Resources Notebook: https://github.com/giswqs/geemap/blob/master/examples/notebooks/30_image_props_stats.ipynb Demo Video https://youtu.be/eixBPPWgWs8 Import libraries imp ..read more

This tutorial shows you how to find out the greenest day of the year for each pixel location. The method can be generalized to find out the corresponding time of the maximum value of a timeseries for each pixel, such as the day with the hottest temperature record for each location during the past decades, the day with the largest amount of rainfall for each location during the past year. Requirements geemap - A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets Installation conda create -n gee python conda activate gee conda install mamba -c conda-fo ..read more

This tutorial shows you how to extract pixel values from Earth Engine images interactively and save pixel values as a csv and shapefile. It will also show you how to perform interactive region reduction, which allows users to draw a rectangle or circle to compute descriptive statistics (e.g., mean, min, max, std, sum) of pixels within a user-drawn geometry. The region reduction results can be exported as a csv for shapefile. Requirements geemap - A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets Installation conda create -n gee python=3.7 conda ac ..read more

Notebook: https://geemap.org/notebooks/51_cartoee_projections Video: https://youtu.be/3dS2EkAuAxM https://youtu.be/3dS2EkAuAxM ..read more

Notebook: https://geemap.org/notebooks/49_colorbar https://youtu.be/qiKns09X1Ao ..read more

This tutorial shows you how to perform unsupervised classification (e.g., KMeans clustering) in Earth Engine. Unsupervised classification algorithms available in Earth Engine Source: https://developers.google.com/earth-engine/clustering The ee.Clusterer package handles unsupervised classification (or clustering) in Earth Engine. These algorithms are currently based on the algorithms with the same name in Weka. More details about each Clusterer are available in the reference docs in the Code Editor. Clusterers are used in the same manner as classifiers in Earth Engine. The general workflow for ..read more

Notebook: https://geemap.org/notebooks/47_image_thumbnails Video: https://youtu.be/qwXZDSbfyE8 https://youtu.be/qwXZDSbfyE8 ..read more

I have developed a series of video tutorials for using geemap and the Earth Engine Python API. These videos are available on my YouTube channel. If you can't access YouTube in your country, you can try Tutorials Introducing the geemap Python package for interactive mapping with Google Earth Engine (video | gif | notebook) Using basemaps in geemap and ipyleaflet for interactive mapping with Google Earth Engine (video | gif | notebook) Introducing the Inspector tool for Earth Engine Python API (video | gif | notebook) Creating a split-panel map for visualizing Earth Engine data (video | gif ..read more

Leafmap Tutorial 79: Visualizing satellite ?image time series interactively with only one line of code ?? Video: https://youtu.be/Jn2YndzEoQo Notebook: https://leafmap.org/notebooks/79_timeseries Demo: https://youtu.be/w5JnVPmHTxs Full tutorial: https://youtu.be/Jn2YndzEoQo ..read more

The recording of my presentation at the TensorFlow Working Group at SERVIR Title: Automated Segmentation of Remote Sensing Imagery with the Segment Anything Model Video: https://www.youtube.com/watch?v=45NpHeq1X6I Slides: https://bit.ly/TFWG GitHub: https://github.com/opengeos/segment-geospatial ..read more