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Transferring datasets between different buckets in Google Cloud Storage

Google Cloud Storage is great because it allows storing large datasets at a minimum cost. Moreover, if you do processing on the cloud, it’s one of the best providers. Datasets are stored in what’s called a bucket. Sometimes, though it becomes necessary to transfer datasets between one bucket to another bucket. The tutorial … 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

Creating Mosaic of Sentinel-1 using Snappy

In this tutorial, we are going to perform the mosaicing of two adjacent Sentinel-1 scenes using Snappy, the Python interface for SNAP.
world-veiew-of-sentinel-scene
If you haven’t, follow along this tutorial to see how you can setup the development environment for SNAP in your machine. To overview, the basic steps that we cover in this tutorial are:… CONTINUE READING

Processing Sentinel-1 SAR images using Snappy – SNAP Python Interface

The back-end on SNAP has been written in Java. But good news to Python enthusiast, they provide Python interface to Java API. It’s through their module called Snappy. In my previous tutorial, I showed you how you can install snappy in your machine and get geared up for the development work. And before we … CONTINUE READING

Setup development environment for SNAP

In this tutorial, we will learn how we can set up a development environment for Snappy. Snappy is the python interface for accessing the JAVA API of SNAP. SNAP can be used to process the Sentinel series of sensors. I prefer to have a separate environment for the Snappy so I can keep it clean … CONTINUE READING

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