This message, related to the development of the theme, only displays on the localhost homepage to notify you of any important theme changes.


Version 2.0.0 - July 20, 2020

Below are the following changes that could be breaking changes for your site. For more details on any change, please refer to PR #154.

The major breaking change is:

  1. Users that have front matter that utilize images (backwards compatibility for featured and associated parameters still remains) will need to adjust from [images]="SRC" to the new format.
[[images]]
    src = "" // Link to image
    alt = "" // Alt text for image
    stretch = // Optional: See screenshots for referenced values and outcomes

If you utilize any of the following, there might be a breaking:

  1. User custom templates may require adjustment.
  2. User custom i18n languages, or custom templates referencing i18n translations may require adjustment.
  3. User custom template for comments will require adjustment if it uses the theme’s CSS and/or JS.
  4. User custom CSS may need to adjust due to a variety of class name changes and specificity changes.

While I realize this is inconvenient, I hope that it is worth it to you in the long run. Thanks for using the theme, and feel free to submit issues as needed.

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Ben's Blog

... | data | geospatial | python | remote sensing | ...

Comparing USGS ShakeMap versions

The M 7.1 - 72 km ENE of Namie, Japan earthquake.

7-Minute Read

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Recently, I came across the question if and how it is possible to download superseded versions of ShakeMaps distributed by the USGS Earthquake Hazards program. This post comprises what I encountered on my first walk through the rich resources offered by the USGS.

Satellite imagery classification - III

Classification with the help of the Python eobox package.

20-Minute Read

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This is the third and last part of a blog post series about using remote sensing data to classify the earth’s surface. In this post we will finally walk through the typical steps it takes to classify remote sensing images with a supervised classifier to derive a land use/land cover map.

Satellite imagery classification - II

Downloading and preparing OpenStreetMap data

17-Minute Read

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This is the second part of a three parts series about using remote sensing data to classify the earth’s surface. Please read the first part of the series if you are interested in an introduction to the post series and / or in the first part where we downloaded parts of Landsat scenes by leveraging the Cloud Optimized GeoTIFF format.

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Mini Autobiography