1904labs & Bayer Crop Science organise an open-source geospatial hackathon

The solutions will be developed through the implementation of HCDAgile Methodologies and an Open Source Geospatial Stack including ol-kit.

1904labs & Bayer Crop Science organise an open-source geospatial hackathon
1904labs & Bayer Crop Science organise an open-source geospatial hackathon

1904labs & Bayer Crop Science organise an open-source geospatial hackathon

1904labs in conjunction with Bayer Crop Science, Saint Louis University, Geospatial Innovation Center at T-REX, and AWS is presenting an opportunity to hack maps using an open-source geospatial stack.

The Geospatial Hackathon will be held on September 14-20, 2020 and will incorporate both a collaborative effort and a competitive event where teams of technologists and designers will create innovative geospatial software solutions.

The solutions will be developed through the implementation of HCDAgile Methodologies and an Open Source Geospatial Stack including ol-kit.

Details

A series of workshops will be organized with a focus on training for technologies including: PostGIS, GeoServer, CKAN, React, OpenLayers, and ol-kit. One of the days will be focused on their methodology, HCDAgile. It will help bring together Agile and Human-Centered Design to build products that users actually want and need. 

Robert Cardillo, president of the Cardillo Group and former Director of the National Geospatial-Intelligence Agency, will be the keynote speaker. The hackathon will be for two days followed by a day of final presentations, judging, and a retrospective session.

Team Types

One can participate as both competitive and collaborative team members. Optimal team size is 3-8 people. 

Competitive

As in a traditional hackathon, competitive teams will be tasked to create an innovative solution using the provided technology stack.

  • Divisions
    • College
    • Professional
  • 20 Teams max
    • 3-8 Members per team
    • Prizes will be awarded per division

Collaborative

Hackathon participants will also have the option to join in a collaborative effort, rather than only the competitive.

  • 1 cohesive team
  • The project will be constrained to solve a predetermined need, separate of those provided to the competitive teams
  • Like the competitive teams, the collaborative team will also be focused on providing a solution based on ol-kit and the provided geospatial stack

One can register their interest here.