This page was created by Yipeng Zhou. The last update was by Kelly O'Neill.
TopoS
The Military-Topographic Survey of European Russia (MTSER) was tsarist in content but utterly trans-imperial in form. The middle of the nineteenth century was the golden age of topographic mapping, with grand projects conducted across several continents. In fact, as the century progressed it became clear that the world could be divided into states that could, and states that could not, demonstrate their mastery of space by producing large scale maps. Topographic surveys could make or break an empire's reputation.
The MTSER, one of many mapping projects pursued in the late imperial period, produced thousands of maps. They are organized according to an index grid and are executed at a scale of 1:126,000 with the same cartography. It is possible to stitch them together into a mosaic - a master map - that will allow us to visualize all kinds of historical information.
But it isn't easy.
That is why this map series makes an excellent starting point for a long-term project that fuses historical expertise with technological innovation. Computers can't read maps; meanwhile, the human brain can't process thousands of maps at once. So why not deploy machine learning methods to bridge the gap?
The payoff is exciting. Can you imagine knowing where all the forests were 150 years ago? Or seeing the systems of mills and bridges that connected factories and fields to markets from St. Petersburg to Odessa? Understanding the empire's spatial structure is as important as understanding its political and social hierarchies, ethnographic composition, cultural or economic capacity. The goal of the TopoS project is to help us all "see" imperial space and, as a result, better understand its history.
At a Glance
Phase 1: Produce high-resolution image files of the map series; complete and analyze the comprehensive catalog of map sheets.Phase 2: Harvard’s Arts and Humanities Research Computing Group will assist with testing and implementing a cutting-edge computer vision pipeline that will identify and extract a sample feature set.
Phase 3: Expand the machine learning model to extract the full catalog of feature classes, wrapping up the project with the publication of the research products and the educational tools designed along the way.
