An open-source platform to manage natural resources.
Historically, the information we use to manage our planet’s resources has been expensive and time-consuming to gather. Satellites and remote sensors have reduced that burden, and sophisticated methods of analysing data, such as Machine Learning, are offering new opportunities to improve the way we make decisions that affect our environment and society.
We want to help people make the most of these opportunities. So we decided to develop an open-source platform that can be used to manage natural resources and detect environmental risks. Using Deep Learning techniques, we’ll be investigating new, flexible and scalable approaches to analyzing remotely sensed data. To support our own investment in this project, we sought additional funding and have been awarded a grant by Spain’s Ministry of Energy, Tourism and Digital Agenda.
As we test our hypotheses, we’ll identify potential uses of this technology, such as monitoring water and crop health, or predicting where deforestation or drought is about to occur. Informed by this knowledge, people will be in a position to take action.
Skydipper is a project of Simbiotica S.L. It has been co-financed by the Ministry of Energy, Tourism and Digital Agenda, within the National Plan for Scientific Research, Development and Technological Innovation 2013-2016 with file number TSI-100504-2017-1.
With masses of data being produced every second of the day, a new approach to processing data is needed. We’ll be exploring how Deep Learning techniques can reduce the time between data collection and insights, so people have access to useful, timely data when they need it most. Deep Learning is part of the Machine Learning family, where a computer can be trained to classify characteristics within an image.
Our aim is to develop a platform that endures and evolves with the needs of the communities who use remotely sensed data for environmental decision-making. We’ll achieve this by selecting technology and open source software that can be adapted, updated and maintained.