The objective of the study is to assess the potential of new information technologies such as Artificial Intelligence (machine learning, deep learning and related algorithms) for processing environmental data in the international context in order to extract appropriate answers to policy- relevant questions for decision-making. These data, which may be in large quantities (Big Data), come from different Earth Observation platforms (EO) and international environmental monitoring (water, soil, forests, climate, etc.), but also as results from models, projections and scenarios presenting the future evolution of the international environment.
The study will indicate the IT tools to search and identify appropriate data on the Internet to answer policy-relevant questions. These tools may be either in the public domain, developed by academic entities or user communities, or commercial enterprises. They can address a wide range of relevant technical aspects such as geolocation. The study will provide concrete examples of current projects in the international context using these tools for environmental protection.
The study will also address important aspects for the user (or "customer") of these tools, such as the transparency of the algorithms, medium-term control (or "ownership") over these IT tools, their versatility, technological lock-in with a supplier or technology, and costs. After a comprehensive review of existing tools, the study will test one of these tools to assess the user friendliness, usability and costs.