A map that reveals patterns of arrangement of buildings

A dataset containing 125,192,184 computer generated building footprints in all 50 US states is the source for a New York Times’s map of every building in America.

Published on 12th October, this map represents every building in the US by a black speck, reflecting the built legacy of the United States.

The dataset was publicly released by Microsoft early this year. The company’s computer engineers trained a neural network to analyze satellite imagery and then to trace the shapes of buildings across the country.

DNN architecture: The network foundation is ResNet34. The model is fully-convolutional, meaning that the model can be applied to an image of any size (constrained by GPU memory, 4096×4096 in the case).

The map reveals patterns in the arrangements of buildings. Traditional road maps highlight streets and highways; here they show up as a linear absence. As a result, “… you can read history in the transition from curving, paved-over cow paths in old downtowns to suburban sprawl; you can detect signals of wealth and poverty, sometimes almost next door to each other.”.

In the south of New Orleans, it’s possible to notice the layout of buildings along a narrow spit of land on either side of a Louisiana bayou, which may reflect the imprint of the region’s history under France: “… “long lot” development, which stretched skinny holdings laterally away from important waterways. Geography shapes settlement, but culture does, as well.”
Buildings along Louisiana bayou

Crotos: a project on visual artworks powered by Wikidata and Wikimedia Commons

Crotos is a search and display engine for visual artworks based on Wikidata and using Wikimedia Commons files.

The Wikidata extraction contains more than 133 866 artworks (September 2018) including 66 271 with HD image. This extraction is regularly automatically updated from Wikidata on the basis of the nature of the items and corresponds to visual artworks such as paintings, photographs, prints, illuminated manuscripts and much more.

The interface

Searches can be made by free or indexed search through a user interface. Results are displayed by chronological order with thumbnails. Links on thumbnails open a viewer with the image hosted on Wikimedia Commons.

It is possible to filter the results by type (painting, sculpture, print…) or to specify a period as a criterion.

By default, without criteria, a random selection is displayed. Besides with the Cosmos interface, it is possible to discover the artworks by indexation (par type d’œuvre, creator, movement, genre, collection…).


For each resulting image, the interface displays the title, the creator(s)  and the collection or the location where the artwork is maintained. These information are on Wikidata, a free, collaborative, multilingual, secondary database, collecting structured data to provide support for Wikipedia, Wikimedia Commons, the other wikis of the Wikimedia movement, and to anyone in the world.

Additional descriptors are date or period, nature of work, material used, inventory number, movement, genre, depicts, main subject, and so on. A full list of descriptors is mentioned here.

Contribution mode

The project has a contribution mode, useful for identifying missing information with facets. Finally, source is on github and the database of Crotos can be downloaded. Both are under Free Licence.

DH2017 – Computer Vision in DH workshop (Papers – First Block)

Seven papers have been selected by a review commission and authors had 15 minutes to present during the Workshop. Papers were divided into three thematic blocks:

First block: Research results using computer vision
Chair: Mark Williams (Darthmouth College)

1) Extracting Meaningful Data from Decomposing Bodies (Alison Langmead, Paul Rodriguez, Sandeep Puthanveetil Satheesan, and Alan Craig)

Full Paper

Each card used a pre-established set of eleven anthropometrical measurements (such as height, length of left foot, and width of the skull) as an index for other identifying information about each individual (such as the crime committed, their nationality, and a pair of photographs).

This presentation is about Decomposing Bodies, a large-scale, lab-based, digital humanities project housed in the Visual Media Workshop at the University of Pittsburgh that is examining the system of criminal identification introduced in France in the late 19th century by Alphonse Bertillon.

  • Data: System of criminal identification from American prisoners from Ohio.
  • ToolOpenFace. Free and open source face recognition with deep neural networks.
  • Goal: An end-to-end system for extracting handwritten text and numbers from scanned Bertillon cards in a semi-automated fashion and also the ability to browse through the original data and generated metadata using a web interface.
  • Character recognition: MNIST database
  • Mechanical Turk: we need to talk about it”: consider Mechanical Turk if public domain data and task is easy.
  • Findings: Humans deal very well with understanding discrepancies. We should not ask the computer to find these discrepancies to us, but we should build visualizations that allow us to visually compare images and identify de similarities and discrepancies.

2) Distant Viewing TV (Taylor Arnold and Lauren Tilton, University of Richmond)


Distant Viewing TV applies computational methods to the study of television series, utilizing and developing cutting-edge techniques in computer vision to analyze moving image culture on a large scale.

Screenshots of analysis of Bewitched
  • Code on Github
  • Both presenters are authors o Humanities Data in R
  • The project was built on work with libraries with low-level features (dlib, cvv and OpenCV) + many papers that attempt to identify mid-level features. Still:
    • code often nonexistent;
    • a prototype is not a library;
    • not generalizable;
    • no interoperability
  • Abstract-features such as genre and emotion, are new territories
Feature taxonomy
  • Pilot study: Bewitched (serie)
  • Goal: measure character presence and position in the scene
  • Algorithm for shot detection 
  • Algorithm for face detection
  •  Video example
  • Next steps:
    • Audio features
    • Build a formal testing set

3) Match, compare, classify, annotate: computer vision tools for the modern humanist (Giles Bergel)

The Printing Machine (Giles Bergel research blog)

This presentation related the University of Oxford’s Visual Geometry Group’s experience in making images computationally addressable for humanities research.

The Visual Geometry Group has built a number of systems for humanists, variously implementing (i) visual search, in which an image is made retrievable; (ii) comparison, which assists the discovery of similarity and difference; (iii) classification, which applies a descriptive vocabulary to images; and (iv) annotation, in which images are further described for both computational and offline analysis

a) Main Project Seebibyte

  • Idea: Visual Search for the Era of Big Data is a large research project based in the Department of Engineering Science, University of Oxford. It is funded by the EPSRC (Engineering and Physical Sciences Research Council), and will run from 2015 – 2020.
  • Objectives: to carry out fundamental research to develop next generation computer vision methods that are able to analyse, describe and search image and video content with human-like capabilities. To transfer these methods to industry and to other academic disciplines (such as Archaeology, Art, Geology, Medicine, Plant sciences and Zoology)
  • Demo: BBC News Search (Visual Search of BBC News)

Tool: VGG Image Classification (VIC) Engine

This is a technical demo of the large-scale on-the-fly web search technologies which are under development in the Oxford University Visual Geometry Group, using data provided by BBC R&D comprising over five years of prime-time news broadcasts from six channels. The demo consists of three different components, which can be used to query the dataset on-the-fly for three different query types: object search, image search and people search.

The demo consists of three different components, which can be used to query the dataset on-the-fly for three different query types.
An item of interest can be specified at run time by a text query, and a discriminative classifier for that item is then learnt on-the-fly using images downloaded from Google Image search.

ApproachImage classification through Machine Learning.
Tool: VGG Image Classification Engine (VIC)

The objective of this research is to find objects in paintings by learning classifiers from photographs on the internet. There is a live demo that allows a user to search for an object of their choosing (such as “baby”, “bird”, or “dog, for example) in a dataset of over 200,000 paintings, in a matter of seconds.

It allows computers to recognize objects in images, what is distinctive about our work is that we also recover the 2D outline of the object. Currently, the project has trained this model to recognize 20 classes. The demo allows the user to test our algorithm on their images.

b) Other projects

Approach: Image searching
Tool: VGG Image Search Engine (VISE)

Approach: Image annotation
Tool: VGG Image Annotator (VIA)


DH2017 – Computer Vision in DH workshop (Keynote)

Robots Reading Vogue Project

A keynote by Lindsay King & Peter Leonard (Yale University) on “Processing Pixels: Towards Visual Culture Computation”.


Abstract: This talk will focus on an array of algorithmic image analysis techniques, from simple to cutting-edge, on materials ranging from 19th century photography to 20th century fashion magazines. We’ll consider colormetrics, hue extraction, facial detection, and neural network-based visual similarity. We’ll also consider the opportunities and challenges of obtaining and working with large-scale image collections.

Project Robots Reading Vogue project at Digital Humanities Lab Yale University Library

1) The project:

  • 121 yrs of Vogue (2,700 covers, 400,000 pages, 6 TB of data). First experiments: N-Grams, topic modeling.
  • Humans are better at seeing “distant vision” (images) patterns with their own eyes than  “distant reading” (text)
  • A simple layout interface of covers by month and year reveals patterns about Vogue’s seasonal patterns
  • The interface is not technically difficult do implement
  • Does not use computer vision for analysis

2) Image analysis in RRV (sorting covers by color to enable browsing)

    • Media visualization (Manovich) to show saturation and hue by month. Result: differences by the season of the year. Tool used:  ImagePlot
    • “The average color problem”. Solutions:
    • Slice histograms: Visualization Peter showed.

The slice histograms give us a zoomed-out view unlike any other visualizations we’ve tried. We think of them as “visual fingerprints” that capture a macroscopic view of how the covers of Vogue changed through time.
  • “Face detection is kinda of a hot topic people talk about but I only think it is of use when it is combined with other techniques’ see e.g. face detection within 

    3. Experiment Face Detection + geography 

  •  Photogrammer
Face Detection + Geography
  • Code on Github
  • Idea: Place image as thumbnail in a map
  • Face Detection + composition
Face Detection + composition

4. Visual Similarity 

  • What if we could search for pictures that are visually similar to a given image
  • Neural networks approach
  • Demo of Visual Similarity experiment:
In the main interface, you select an image and it shows its closest neighbors.
  • In the main interface, you select an image and it shows its closest neighbors.

Other related works on Visual Similarities:

  • John Resig’s Ukiyo-e  (Japenese woodblock prints project). Article: Resig, John. “Aggregating and Analyzing Digitized Japanese Woodblock Prints.” Japanese Association of Digital Humanities conference, 2013.
  • John Resig’s  TinEye MatchEngine (Finds duplicate, modified and even derivative images in your image collection).
  • Carl Stahmer – Arch Vision (Early English Broadside / Ballad Impression Archive)
  • Article: Stahmer, Carl. (2014). “Arch-V: A platform for image-based search and retrieval of digital archives.” Digital Humanities 2014: Conference Abstracts
  • ARCHIVE-VISION Github code here
  • Peter refers to paper Benoit presented in Krakow.

5. Final thoughts and next steps

  • Towards Visual Cultures Computation
  • NNs are “indescribable”… but we can dig in to look at pixels that contribute to classifications: http://cs231n.github.io/understanding-cnn/
  • The Digital Humanities Lab at Yale University Library is currently working with as image dataset from YALE library through Deep Learning approach to detect visual similarities.
  • This project is called Neural Neighbors and there is a live demo of neural network visual similarity on 19thC photos
Neural Neighbors seeks to show visual similarities in 80,000 19th Century photographs
  • The idea is to combine signal from pixels with signal from text
  • Question: how to organize this logistically?
  • Consider intrinsic metadata of available collections
  • Approaches to handling copyright licensing restrictions (perpetual license and transformative use)
  • Increase the number of open image collections available: museums, governments collections, social media
  • Computer science departments working on computer vision with training datasets.