Tutorials/Post - Remote Sensing, GIS, Earth System, Geo-AI/ML

Category: Google Earth Engine Page 1 of 8

Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine

Air pollution from burning sugarcane is an important environmental issue in Thailand. Knowing the location and extent of sugarcane plantations would help in formulating effective strategies to reduce burning. High-resolution satellite imagery combined with deep-learning technologies can be effective to map sugarcane with high precision. However, land cover mapping using high-resolution data and computationally intensive … CONTINUE READING

Screenshot of loading custom base map module.

Adding custom basemaps to Google Earth Engine code editor

I happened across an interesting Github repository from Samapriya Roy the other day for creating custom basemaps to add to your Google Earth Engine map. Traditionally when using or sharing your work in GEE you have the option between a standard Google cartographic basemap or the ‘satellite’ view ( in quotations here since it’s my … CONTINUE READING

Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning

The large-scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such … CONTINUE READING

Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine

Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near-real-time applications. Due to its cloud penetrating capability, many studies have focused on providing efficient and high-quality methods for surface water mapping using Synthetic Aperture Radar (SAR). However, few studies have explored the effects of SAR pre-processing steps … CONTINUE READING

Creating NDVI based thresholded thematic map from MODIS

In this tutorial, we will use the MODIS based V6 Terra Vegetation Indices 16-Day Global 250m product. This MODIS NDVI and EVI products are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. We will use this product to create NDVI based threshold classified thematic … CONTINUE READING

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