A Step-by-Step Guide to Acquiring Multisource Remote Sensing Data Using Python
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Read full blog here guide The intersection of computer vision, remote sensing, and earth observation has proven to be highly effective in monitoring and predicting Earth surface processes. These tasks often require combining multisource remote sensing data to provide complementary information to deep learning models. Since these models are data-intensive, performing large-scale analyses necessitates a sophisticated pipeline for downloading and processing multisource remote sensing data efficiently.
In this blog, I have share Python code snippets to download various remote sensing datasets, including Sentinel-1, Sentinel-2, MODIS, Digital Elevation Model (DEM), and precipitation data. The focus is on minimizing prerequisites — such as the need for multiple accounts — while offering flexible solutions to work with different areas of interest (AOI). Repository Link.
