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

Category: Remote Sensing Page 1 of 4

Surface Water Detected by the BCE Algorithm

Deep Learning approach for Sentinel-1 Surface Water Mapping leveraging Google Earth Engine

Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety of approaches are available for mapping surface water, but deep learning approaches are not commonplace as they are ’data hungry’ and require large amounts of computational resources. However, with the availability of various satellite sensors and rapid … CONTINUE READING

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

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

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

Ditch Snappy to use Graph Processor Tool (GPT) to process your Sentinel-1 Data

Last week, I was doing some testing using the Python API interface for sentinel-1 toolbox with Snappy and the SNAP desktop to see the time taken by each of them. The workflow for processing the data is described here. The results were astonishing. Each and every steps were significantly slower, especially the co-registration process … CONTINUE READING

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