Machines Reading Maps
These slides: slides.com/staceymaples/mrm
"This is unreasonably cool, and so visually pleasing, it nearly tickles. It also feels like I’m looking at one of those moments where everything changes."
Machines Reading Maps Summit April 20-21, 2023
- Invite-only Stakeholder Introduction to MRM
- Public Introduction to MRM
- Invite-only Community-building discussion
- MRM/Recogito Workshop
How does MRM work?
Image Cropping
Text Spotting
- Using TExt Spotting TRansformers (TESTR), a generic end-to-end text spotting framework using Transformers for text detection and recognition in the wild.
- TESTR is particularly effective when dealing with curved text-boxes where special cares are needed for the adaptation of the traditional bounding-box representations.
- Different from OCR, which is adept at extracting separate words, where here we are interested in FULL LOCATION PHRASES
Human Annotations
SynMap+ Training Data
- Use a generative adversarial style transfer network (CycleGAN) to convert an OSM image to the historical style,
- Associate the font, style and placement strategy according to the underlying geographical features
- Use "rule-based" labeling from the QGIS PAL API to place the text labels on the synthetic map background,
- designed an approach to automatically generate the polygon, centerline and local height annotation for the text labels.
SynthText Training Data
PatchTextSpotter & PatchtoMapMerging
Coordinate Converter
Coordinate Converter
PostOCR & Entity Linker
Next Steps: Build Software
"This past summer, a CESTA intern georeferenced 100+ [19th century] South African maps (at farm level). It would be great to harvest the place names and make them digitally searchable. Is that something we could discuss please?"
-Grant Parker, Classics Dept.
Why let
Machines Read Maps?
Metadata search is insufficient
The infrastructure that those maps are served from is well suited to this work
There are millions of scanned maps available, publicly, now.
Modern data is insufficient
Existing spatial data sources only contain information about the present (modern placenames), but even those are incomplete...
Source: GNS, National Geospatial-Intelligence Agency
More Info:
Who
Machines Reading Maps (MRM) is a collaborative project of
- University of Southern California Digital Library
- Computer Science & Engineering Department at the University of Minnesota (US)
- The Alan Turing Institute (UK).
- The David Rumsey Map Collection (davidrumsey.com)
The project is funded by the
- United States’ National Endowment for the Humanities (NEH)
- United Kingdom’s Arts and Humanities Research Council (AHRC) under the first round of NEH/AHRC New Directions for Digital Scholarship.
- David & Abby Rumsey
Machines Reading Maps These slides: slides.com/staceymaples/mrm
Machines Reading Maps
By Stace Maples
Machines Reading Maps
- 300
