GPlates Web Service

Calculate Paleo-geographic Coordinates


Reconstruct the geographic locations from present day coordinates back to their paleo-positions. Each location will be assigned a plate id and moved back in time using the chosen reconstruction model.


The URL below reconstructs two locations((lon:95,lat:54)(lon:142,lat:-33)) back to 140 million years ago using the "default" reconstruction model.,54,142,-33&time=140

Reconstruct GeoJSON Feature Collection


Reconstruct feature collection in GeoJSON format back in time. Each feature will be assigned a plate id and moved back in time using the given reconstruction model. Reconstructed features will be returned in GeoJSON format.


Reconstruct Coastlines


http GET request to retrieve reconstructed coastline polygons


Run Reconstruction Service In A Docker Container

The GPlates team has prepared a Docker container for users to run the service in their own computers. Running the GPlates Web Service locally inside a Docker container is especially important in the following scenarios:

  • User's program requires short response time and low latency.
  • User has a large volume of data to process and can provide much more computational power than GPlates server.
  • User has concerns about sending their data to a remote server.
  • User cannot access internet service all the time or the internet service is very poor.

The GPlates Web Service Docker container can be downloaded from Docker Hub.

EarthByte & GPlates

The GPlates Web Service is created and maintained by EarthByte group at the University of Sydney.

EarthByte is an internationally leading eGeoscience collaboration between several Australian Universities, international centres of excellence and industry partners. One of the fundamental aims of the EarthByte Group is geodata synthesis through space and time, assimilating the wealth of disparate geological and geophysical data into a four-dimensional Earth model including tectonics, geodynamics and surface processes. The EarthByte Group is pursuing open innovation via collaborative software development, high performance and distributed computing, “big data” analysis and by making open access digital data collections available to the community.