Using neural networks to classify 3D scans

For my capstone project in machine learning at EPFL, I wrote a classifier capable of sorting 3D scans of archaeological objects by culture.

Digitization of museum collections is currently a major challenge faced by cultural heritage and natural history museums. Museums are expected to digitize the collections to improve not only the documentation of artifacts, but also their availability for research, reconstruction and outreach activities, and to make these digital representations available online.

Machine learning setup

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Summarizing a text using topic modeling

Much of biodiversity is discovered in museum collections, sometimes years after the specimen has been collected. Ploughing through expedition notes and logs is then required and therefore having a way to summarize the contents of a large text corpus can be very interesting. In this example, I will graphically summarize “On the Origin of Species” by Charles Darwin (it seemed a suitable choice) to demonstrate this technique.

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Comparing the distribution of Corvus corone and Corvus cornix

In this post, I will use a divergent color scale to plot two distributions on the same map. As an example, I chose to plot the European distribution of two species of corvids: the carrion crow (Corvus corone) and the hooded crow (Corvus cornix). There has been some adjustments to the taxonomical status of the hooded crow (see Parkin et al., 2003 for details), hoewever, currently, they are regarded as different species.

In this map, I will use a divergent color scale to show areas in Europe where each species is dominant, and also show areas where both species are present.

Distribution of Corvus corone and C. cornix in Europe
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Simple distribution maps using ggplot

In a previous post, I discussed how to plot GBIF occurrence data using OpenStreetMaps. Here, I will plot a distribution map. Distribution maps differ from occurrence maps in that occurrences are aggregated and plotted as a heat map. Additionally, the map has to be projected using an equal area projection.
I will illustrate these two features by plotting the distribution of the tawny owl (Strix aluco) in Europe.

Distribution of the tawny owl in Europe
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Plotting GBIF occurrence data on a map using OpenStreetMap

In a previous post, I dicussed how to get occurrence data from the Global Biodiversity Information Facility (GBIF). For my current project at the Natural History Museum in Berlin, I work on penguins. In this post, I will plot occurrences of penguin species on a map. Occurrence maps show the geographical position of occurrences.

The penguin map
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Spectrograms

Spectrograms are a common visualization of sound data. Visualizing sound data can be useful when doing a presentation or for publication. Additionally, machine learning algorithms for classifying sound data generally use spectrograms as their starting point, instead of the sound data itself, as many advanced algorithnms for classifying images are readily available. The example uses the R packages warbleR (Araya-Salas & Smith-Vidaurre, 2017), seewave (Sueur, Aubin, Simonis, 2008) and tuneR (Ligges et al., 2018).

This example draws the spectrogram of the call of a tawny owl (Strix aluco).

Tawny owl (Strix aluco). Alvaro Ortiz Troncoso, XC494801. Accessible at www.xeno-canto.org/494801.
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Hearing and vocalization ranges of marine animals

For a project on noise pollution in the oceans at the Natural History Museum in Berlin, I recently made this plot of the hearing and vocalization ranges of selected marine animals. Range plots are generally not-so-common plots. In this example, I plotted the hearing and vocalization range (frequency) for several species of whales, dolphins, seals, turtles and fishes.

Hearing and vocalization ranges of marine animals
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Ecological data sets in R-core

If you are using R, then you are probably familiar with the mtcars data set, that is used in many R tutorials. The “car analogy” is so common in text books, that this so-called technique has its own Wikipedia page. For the rest of us, who don’t understand anything about motor cars, R-core comes with a wide selection of example data sets, some of which relate to ecology or biodiversity.

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