![]() ![]() You can just create a series of Slides if you like, but you can make the slideshow a bit more interesting by adding Sub-Slides and Fragments. These widgets give you the following options: Programmatic Access to OpenTopography’s Cloud Optimized GeoTIFF (COG) Global Datasets (Version v1.0).There are now little comboboxes on the top right of each cell. Use Rasterio to read COGs directly to polygon boundary:īeckley, M., Crosby, C., Nandigam, V. Using GDAL vsicurl with OpenTopography COGs to subset and mosaic the data:ĭownload, clip, and buffer Colorado River boundary to area of interest: ![]() Below is a visualization of some of the steps users will work though as part of the Jupyter Notebook.Īccessing COGs from OpenTopography's Bulk Storage using AWS Command Line Tools: Users will access the SRTM COGs via OpenTopography cloud storage subset and mosaic the data download, clip, and buffer a vector file of the Colorado River and then clip the DEM to the buffered area. The notebook walks through a simple workflow using SRTM topography for the Grand Canyon in Arizona. To run the notebook interactively: Access the notebook with Binder. Reading COGs and vector data with Python librariesĪccess the notebook by cloning our github repo here: OpenTopography Jupyter Notebooks.Benefits of using Cloud Optimized GeoTIFFs (COGs).In addition, the notebook highlights the power of Cloud Optimized Geotiffs (COGs) and how they can be used to reduce download file sizes as well as increase data access speeds.Įxplore the notebook for more information on: ![]() The notebook illustrates how to use open-source, command line tools to programmatically access data without the need for a web-map interface. We've created a Jupyter Notebook to demonstrate different methods of accessing data from OpenTopography's cloud-based bulk storage using the NASA Shuttle Radar Topography Mission (SRTM) dataset as an example. This drastically reduces file read-times as well as reducing the number of http GET requests to access the data. With the COG format, programs and web requests can read the top-level header and quickly extract data for a region of interest as opposed to having to read the entire file. COGs are quickly becoming a standard format for hosting raster data in the cloud due to the optimized file structure to enable faster access and subsetting. To facilitate programmatic access to cloud-based data, we have recently converted our entire catalog of global topographic data to Cloud Optimized GeoTIFFs (COGs). While most users access data through the OpenTopography web map interface, others prefer to programmatically access data from our cloud-based bulk storage. OpenTopography strives to provide a variety of access methods to our datasets to address the varying needs of our users. ![]()
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