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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Linking Earth Observations for Assessing the Food Security Situation in Vietnam: a Landscape Approach
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Convert the GPS waypoints to Shapefile
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Ditch Snappy to use Graph Processor Tool (GPT) to process your Sentinel-1 Data
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Land cover Mapping in Data Scarce Environments: Challenges and Opportunities. Another paper on Land Cover
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Primitives as building blocks for constructing landcover maps – New paper out on RLCMS and Google Earth Engine
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Downloading 12.5 m ALOS PALSAR High-Resolution DEM
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Changing the size of the rectangle in Google Earth Engine
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Connected Pixel Counts in Google Earth Engine
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Area Calculation using Google Earth Engine
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Machine Learning and Artificial Intelligence Exploring the Google TensorFlow Ecosystem
