EnvirometriX Case Study: AI Workstations for Global Environmental Data Analysis

Project Approach

EnvirometriX and OpenGeoHub both observed that there is was a vast amount of legacy data on land use already publicly available, including observations of land cover, land use, soil properties and qualities. However, this data is systematically under-used despite its value. They decided to try to integrate, harmonise and process this underused data using state-of-the-art machine learning algorithms to extract valuable information patterns and trends not immediately recognisable from ground observations and remote sensing data itself. Furthermore, their aim was to automate this process and open it to all interested users, so that its use can be accelerated and empower others to create their own, new open data.

EnvirometriX and OpenGeoHub worked with many partners including GILAB, Land Potential and Spatia Ecology, to start a global predictive vegetation and soil mapping system. This would serve as an international reference for global planning and assessment projects, and to facilitate easy access to, and use of, environmental layers potentially important for spatial modelling, decision making and for raising public awareness about land degradation, land restoration and land potential.