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Category: Google Earth Engine Page 2 of 7

Land cover Mapping in Data Scarce Environments: Challenges and Opportunities. Another paper on Land Cover

Land cover maps are a critical component to make informed policy, development, planning, and resource management decisions. However, technical, capacity and institutional challenges inhibit the creation of consistent and relevant land cover maps for use in developing regions. Many developing regions lack coordinated capacity, infrastructure, and technologies to produce a robust land cover monitoring system … CONTINUE READING

Primitives as building blocks for constructing landcover maps – New paper out on RLCMS and Google Earth Engine

Land cover maps play an integral role in environmental management. However, countries and institutes encounter many challenges with producing timely, efficient, and temporally harmonized updates to their land cover maps. To address these issues we present a modular Regional Land Cover Monitoring System (RLCMS) architecture that is easily customized to create land cover products using … CONTINUE READING

Changing the size of the rectangle in Google Earth Engine

After the Geo for Good Summit 2019, all my focus has been geared toward integrating Machine Learning Models using Tensorflow with the Satellite data.  One of the issues for exporting the training and testing data especially with a large number of features or huge areas for the neighborhood pixels if you are using the Fully … CONTINUE READING

Connected Pixel Counts in Google Earth Engine

Connected Pixel Count is one of the ways where the concept of the Minimum Mapping Unit (MMU) can be applied. Basically, the connected pixel count gives the image with every pixel containing the information on the number of the connected neighbors including the pixel in context. The neighbors can be 4- or 8-connected neighbors, and … CONTINUE READING

Area Calculation using Google Earth Engine

There are a couple of ways to calculate the area of the image in the Google Earth Engine. The full implementation of both method can be accessed using this link.

  1. Pixel Count Method
    We can calculate the area of the image by counting the total number of unmasked pixels in that image. Then, multiply

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