Integrate Google Earth Engine(GEE), Pandas framework and Matplotlip - Spatial Data Mining

Published on Apr 04, 2020
Discovery Pattern in Spatial data, extract potentially useful information from data stored in images and visualize the result in dynamic charts.

Topographic illumination correction for Sentinel 2 - Python API

Published on Apr 26, 2020
Variation in the reflectance within the same land cover type can be seen in mountain areas caused due to sun position, slope, and aspect of the landform.

Chirps Precipitation to Excel - GEE and Pandas

Published on Apr 29, 2020
Extract Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset using Google earth engine API and convert it into. Excel and charts using Python Pandas data frame.

Introduction to the Google Earth Engine Python API

Published on Apr 13, 2020
This tutorial will go over how to set up the API on your local machine as well as some basic Python scripts utilizing the Google Earth Engine Python API

Land Cover Mapping - Part 4 (Analyze Statistics)

Published on Jun 08, 2020
Statistics are simple tools that helps us for a better understanding of our images. Spatial statistics is one of the most rapidly growing areas of statistics.

Geo-pandas data frame to GEE feature collection using Python

Published on Apr 27, 2020
The simplest way to convert shapefile, CSV it into ee.FeatureCollection is to infuse the Geopandas data frame with GEE python API.

Land Cover Mapping - Part 3 (Validation)

Published on Jun 04, 2020
The surface of the Earth is continuously changing at many levels; local, regional, national, and global scales. Changes in land use and land cover are pervasive, rapid, and can have significant impacts on people, the economy, and the environment.

Calculate Zonal Statistics and export as CSV

Published on Jul 10, 2020
Get statistics in each zone of the image or image collection in google earth engine and export the data in CSV. Statistics are simple tools that help us for a better understanding of our images. Spatial statistics is one of the most rapidly growing areas of statistics.

Most used spectral Indices with free satellite data

Published on May 14, 2020
Today many different indices exist and each one has its own significance in the study. Here are the most popular indices that be calculated from free data satellites.

Google earth engine with python

Published on May 07, 2020
In addition to the web-based IDE Google Earth Engine also provides a Python API that can be used on your local machine.

Learn Google Earth Engine from Scratch

Published on Sep 18, 2020
Google Earth EngineĀ is a geospatial processing service with the motto "To organize the world's information and make it universally accessible and useful."

All you need to know about NDWI: Normalized Difference Water Index

Published on May 12, 2020
The Normalized Difference Water Index (NDWI) is remote sensing derived index estimating the leaf water content at the canopy level.

Supervised Classification in GEE

Published on May 10, 2020
Supervised classification is the technique most often used for the quantitative analysis of remote sensing image data.

All you need to know about EVI

Published on May 11, 2020
EVI is similar to Normalized Difference Vegetation Index (NDVI) and can be used to quantify vegetation greenness. However, EVI corrects for some atmospheric conditions and canopy background noise and is more sensitive in areas with dense vegetation.