Converting from Blosxom to Flatpress

Sunday, September 29, 2019 at 13:50:46

This blog has been offline for a little while as the original Blosxom implementation had been hacked. Blosxom was a wonderful CGI script that was elegant in its simplicity yet eminently extensible through the many plugins which existed and made it moderately feature rich. Best of all, it used plain text files to store all its entries which makes backup and conversion much simpler than a database. With my implementation of blosxom decommissioned, I needed to find a replacement. Google flat file blogging engines and there are a lot. However many of the projects have been orphaned, like blosxom, and no longer in active development. What I wanted to find was an engine that was simple, had some good features and an active community. Flatpress seems to fit the bill with a new maintainer - and active Flatpresser - Arvid Zimmerman.

The next step was to convert my archive of over 1000 blosxom blog entries to Flatpress. Big shout out to James O’Connor who wrote the Python script to convert the files. The process is broadly this:

  • download your Blosxom files, including all the sub-directories for categories, but make sure to maintain the date/time filestamp of individual files - this is used to timestamp the entry for Flatpress. WinSCP does this (Filezilla doesnt)
  • make sure the categories only ONE DIRECTORY DEEP. Move any sub-sub-directories up to the top level
  • rename all the directories to numbers. These are used to tag the entries and can then be recreated within FlatPress
  • copy the and template files to the directory the folders are stored in
  • edit the template file to have the header/footer you want. The content, date and categories will be changed for the entries
  • run the script
  • a new fp-content directory will be created with all your entries
  • upload this to your flatpress site and rebuild the index

The script does the following

  • renames the file to entry‹date›-‹time›.txt based upon the date modified date
  • copies the file to a new subfolder in FlatPress /content folder based upon year and month
  • deletes the first line from the file (and deletes the first line break)
  • prefixes the file with: VERSION|fp-1.1|SUBJECT||CONTENT|
  • suffixes with: |AUTHOR|miksmith|DATE|<1566926569>|CATEGORIES||

Any updates will be posted over at Flatpress.

FREE EPRINT: Editorial: Perspectives on the contemporary art-geoscience interface, Journal of Maps

Saturday, September 28, 2019 at 22:46:04

Tooth, S., Smith, M.J., Viles, H.A. and Parrott, F.
Journal of Maps

This Special Issue of the Journal of Maps is devoted to highlighting contemporary examples of interdisciplinary collaborations between the arts and the geosciences (e.g. geomorphology, geology, Quaternary studies), with a specific focus upon the exploration of locations using, at least in part, some form of mapping. As previous contributions to the journal have exemplified, mapping is essential for the exploration of locations, particularly by supplying visual representation to help with the characterisation of three core geographical concepts (Matthews & Herbert, 2008): space (e.g. distances, directions), place (e.g. boundaries, territories), and environment (e.g. biophysical characteristics).

FREE EPRINT: Testing and application of a model for snow redistribution (Snow_Blow) in the Ellsworth Mountains, Antarctica, Journal of Glaciology

Saturday, September 28, 2019 at 22:42:25

Mills, S.C., Le Brocq, A.M., Winter, K., Smith, M.J., Hillier, J., Ardakova, E., Boston, C., Sugden, D. and Woodward, J.
Journal of Glaciology

Wind-driven snow redistribution can increase the spatial heterogeneity of snow accumulation on ice caps and ice sheets, and may prove crucial for the initiation and survival of glaciers in areas of marginal glaciation. We present a snowdrift model (Snow_Blow), which extends and improves the model of Purves et al. (1999). The model calculates spatial variations in relative snow accumulation that result from variations in topography, using a digital elevation model (DEM) and wind direction as inputs. Improvements include snow redistribution using a flux routing algorithm, DEM resolution independence and the addition of a slope curvature component. This paper tests Snow_Blow in Antarctica (a modern environment) and reveals its potential for application in palaeo-environmental settings, where input meteorological data are unavailable and difficult to estimate. Specifically, Snow_Blow is applied to the Ellsworth Mountains in West Antarctica where ablation is considered to be predominantly related to wind erosion processes. We find that Snow_Blow is able to replicate well the existing distribution of accumulating snow and snow erosion as recorded in and around Blue Ice Areas. Lastly, a variety of model parameters are tested, including depositional distance and erosion vs wind speed, to provide the most likely input parameters for palaeo-environmental reconstructions.

FREE EPRINT: Quantification of Hydrocarbon Abundance in Soils using Deep Learning with Dropout and Hyperspectral Data, Remote Sensing

Saturday, September 28, 2019 at 22:37:41

Asmau Ahmed, Olga Duran, Yahya Zweiri, Mike Smith
Remote Sensing

Terrestrial hydrocarbon spills have the potential to cause significant soil degradation across large areas. Identification and remedial measures taken at an early stage are therefore important. Reflectance spectroscopy is a rapid remote sensing method that has proven capable of characterizing hydrocarbon-contaminated soils. In this paper, we develop a deep learning approach to estimate the amount of Hydrocarbon (HC) mixed with different soil samples using a three-term backpropagation algorithm with dropout. The dropout was used to avoid overfitting and reduce computational complexity. A Hyspex SWIR 384 m camera measured the reflectance of the samples obtained by mixing and homogenizing four different soil types with four different HC substances, respectively. The datasets were fed into the proposed deep learning neural network to quantify the amount of HCs in each dataset. Individual validation of all the dataset shows excellent prediction estimation of the HC content with an average mean square error of ~2.2×10-4. The results with remote sensed data captured by an airborne system validate the approach. This demonstrates that a deep learning approach coupled with hyperspectral imaging techniques can be used for rapid identification and estimation of HCs in soils, which could be useful in estimating the quantity of HC spills at an early stage.

FREE EPRINT: Assessment of low altitude UAS flight strategy on DEM accuracy, Earth Science Informatics

Saturday, September 28, 2019 at 22:34:25

Anders, N.S., Smith, M.J., Suomalainen, J., Cammeraat, L.H., and Keesstra, S.D.
Earth Science Informatics

Soil erosion, rapid geomorphological change and vegetation degrada- tion are major threats to the human and natural environment. Unmanned Aerial Systems (UAS) can be used as tools to provide detailed and accurate estimations of landscape change. The effect of flight strategy on the accuracy of UAS image data products, typically a digital surface model (DSM) and orthophoto, is unknown. Herein different flying altitudes (126-235 m) and area coverage orientations (N-S and SW-NE) are assessed in a semi-arid and medium-relief area where terraced and abandoned agricultural fields are heavily damaged by piping and gully erosion. The assessment was with respect to cell size, vertical and horizontal accuracy, absolute difference of DSM, and registration of recognizable landscape features. The results show increasing cell size (5-9 cm) with increasing altitude, and differences between elevation values (10-20 cm) for different flight directions. Vertical accuracy ranged 4-7 cm but showed no clear relationship with flight strategy, whilst horizontal error was stable (2-4 cm) for the different orthophotos. In all data sets, geomorphological features such as piping channels, rills and gullies and vegetation patches could be labeled by a technician. Finally, the datasets have been released in a public repository.

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