Archive for the ‘Geophysical’ Category

Imagery Processing Pipeline Launches!

Tuesday, April 21st, 2015

Imagery Processing Pipeline Launches!

From the post:

Our imagery processing pipeline is live! You can search the Landsat 8 imagery catalog, filter by date and cloud coverage, then select any image. The image is instantly processed, assembling bands and correcting colors, and loaded into our API. Within minutes you will have an email with a link to the API end point that can be loaded into any web or mobile application.

Our goal is to make it fast for anyone to find imagery for a news story after a disaster, easy for any planner to get the the most recent view of their city, and any developer to pull in thousands of square KM of processed imagery for their precision agriculture app. All directly using our API

There are two ways to get started: via the imagery browser, or directly via the the Search and Publish APIs. All API documentation is on You can either use the API to programmatically pull imagery though the pipeline or build your own UI on top of the API, just like we did.

The API provides direct access to more than 300TB of satellite imagery from Landsat 8. Early next year we’ll make our own imagery available once our own Landmapper constellation is fully commissioned.

Hit us up @astrodigitalgeo or sign up at to follow as we build. Huge thanks to our partners at Development Seed who is leading our development and for the infinitively scalable API from Mapbox.

If you are interested in Earth images, you really need to check this out!

I haven’t tried the API but did get a link to an image of my city and surrounding area.

Definitely worth a long look!


Tuesday, May 20th, 2014


From the webpage:

Madagascar is an open-source software package for multidimensional data analysis and reproducible computational experiments. Its mission is to provide

  • a convenient and powerful environment
  • a convenient technology transfer tool

for researchers working with digital image and data processing in geophysics and related fields. Technology developed using the Madagascar project management system is transferred in the form of recorded processing histories, which become “computational recipes” to be verified, exchanged, and modified by users of the system.

Interesting tool for “reproducible documents” and data analysis.

The file format, Regularly Sampled Format (RSF) sounds interesting:

For data, Madagascar uses the Regularly Sampled Format (RSF), which is based on the concept of hypercubes (n-D arrays, or regularly sampled functions of several variables), much like the SEPlib (its closest relative), DDS, or the regularly-sampled version of the Javaseis format (SVF). Up to 9 dimensions are supported. For 1D it is conceptually analogous to a time series, for 2D to a raster image, and for 3D to a voxel volume. The format (actually a metaformat) makes use of a ASCII file with metadata (information about the data), including a pointer (in= parameter) to the location of the file with the actual data values. Irregularly sampled data are currently handled as a pair of datasets, one containing data and the second containing the corresponding irregular geometry information. Programs for conversion to and from other formats such as SEG-Y and SU are provided. (From Package Overview)

In case you are interested SEG-Y and SU (Seismic Unix data format) are both formats for geophysical data.

I first saw this in a tweet by Scientific Python.

American Geophysical Union (AGU)

Friday, March 22nd, 2013

American Geophysical Union (AGU)

The mission of the AGU:

The purpose of the American Geophysical Union is to promote discovery in Earth and space science for the benefit of humanity.

While I was hunting down information on DataONE, I ran across the AGU site.

Like all disciplines, data analysis, collection, collation, sharing, etc. are ongoing concerns at the AGU.

My interest in more in the data techniques than the subject matter.

Seeking to avoid re-inventing the wheel and learning new insights than has yet to reach more familiar areas.