Recreating historical streetscapes using deep learning and crowdsourcing

For many, gazing at an old photo of a city can evoke feelings of both nostalgia and wonder. We have Google Street View for places in the present day, but what about places in the past? What was it like to walk through Manhattan in the 1940s? To create a rewarding “time travel” experience for both research and entertainment purposes, Google Research is launching Kartta Labs, an open source, scalable system on Google Cloud and Kubernetes that tackles the difficult problem of reconstructing what cities looked like in the past from scarce historical maps and photos.

Kartta Labs consists of three main parts:

  • A temporal map server, which shows how maps change over time;
  • A crowdsourcing platform, which allows users to upload historical maps of cities, georectify, and vectorize them (i.e. match them to real world coordinates);
  • And an upcoming 3D experience platform, which runs on top of maps creating the 3D experience by using deep learning to reconstruct buildings in 3D from limited historical images and maps data.

Maps & Crowdsourcing

Kartta Labs is a growing suite of open source tools that work together to create a map server with a time dimension, allowing users to populate the service with historically accurate data.
gif of editor in use

Warper

The entry point to crowdsourcing is Warper, an open source web app based on MapWarper that allows users to upload historical images of maps and georectify them by finding control points on the historical map and corresponding points on a base map.

Once a user uploads a scanned historical map, Warper makes a best guess of the map’s geolocation by extracting textual information from the map. This initial guess is used to place the map roughly in its location and allow the user to georeference the map pixels by placing pairs of control points on the historical map and a reference map. Given the georeferenced points, the application warps the image such that it aligns well with the reference map.

Warper runs as a Ruby on Rails application using a number of open source geospatial libraries and technologies, including but not limited to PostGIS and GDAL. The resulting maps can be exported in PNG, GeoTIFF, and other open formats. Warper also runs a raster tiles server that serves each georectified map at a tile URL. This raster tile server is used to load the georectified map as a background in the Editor application that is described next.

Editor

Editor is an open source web application which is a customized version of the OpenStreetMap editor; customizations include support for time dimension and integration with the other tools in the Kartta Labs suite. Editor allows users to load the georectified historical maps and trace their geographic features (e.g., building footprints, roads, etc.). This traced data is stored in vector format.

Extracted geometries in vector format, as well as metadata (e.g., address, name, and start or end dates), are stored in a geospatial database that can be queried, edited, styled, and rendered into new maps.

Kartta

Finally, the temporal map front end, Kartta (based on Tegola), visualizes the vector tiles allowing the users to navigate historical maps in space and time. Kartta works like any familiar map application (such as Google Maps), but also has a time slider so the user can choose the year at which they want to see the map. By moving the time slider, the user is able to see how features in the map, such as buildings and roads, changes over time.

3D Experience

To actually create the “time traveling” 3D experience, the forthcoming 3D Models module aims to reconstruct the detailed full 3D structures of historical buildings. The module will associate images with maps data, organize these 3D models properly in one repository, and render them on the historical maps with a time dimension.

Preliminary Results

Figure 2 – Bird’s eye view of 3D-reconstructed  Chelsea, Manhattan with a time slider
Figure 3 – Street level view of 3D-reconstructed Chelsea, Manhattan

Conclusion

We developed the tools outlined above to facilitate crowdsourcing and tackle the main challenge of insufficient historical data. We hope Kartta Labs acts as a nexus for an active community of developers, map enthusiasts, and casual users that not only utilizes our historical datasets and open source code, but actively contributes to both. The launch of our implementation of the Kartta Labs suite is imminent—keep an eye out on the Google AI blog for that announcement!
By Raimondas Kiveris – Google Research

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