Smart-Biodiv_Ukraine

Marine environments undergo rapid changes under the influence of various pressures (human footprint, climate change) and the monitoring of their ecosystem status becomes critical. Such a monitoring requires gathering data, to process them and to extract indicators summarizing the status of the environment that is otherwise too highly dimensional to be grasped by a human being. In recent years, the massive availability of data combined with powerful machine learning algorithms and the associated hardware led to significant advances in domains that were not even dreamed about in the last few years (image classification, automatic translation, text to speech, action selection, ...). Marine ecosystems, where progress has been made in collecting large amounts of data, could also benefit from these AI advances. However, the data in environmental sciences are often sparse either in time, space or relative to the measured variables, and imbalanced which constitute challenges for AI algorithms. This leads to the two directions followed in the SMART-BIODIV proposal: 1) harnessing the power of machine learning algorithms to complete and process sparse and imbalanced data that we often encounter in environmental sciences and 2) designing indicators to qualify the ecological status of the considered environments. Even if the data are scattered, there are several heterogeneous databases that constitute as many points of view that can be combined to build a coherent and complete state of the ecosystem. We will study the potential of interpolation algorithms in time and space as well as predictive models based on co-occurrences. We will also exploit the large image databases collected by the partners on marine plankton and make them available to the challenge participants. More prospectively, we will study the feasibility of including symbolic data, such as food webs, to constrain the evolution of the state of the ecosystem and inject this knowledge of the interdependencies between the dimensions of the state to improve its estimation. These data, grouped, merged and completed, will then serve as a basis for the calculation of taxonomic and trait-based indicators, which will be designed on the basis of our expertise in freshwater bioindication. To reach the challenge’s objectives, our consortium gathers complementary expertises in deep learning, computer vision, oceanography, plankton imaging, and freshwater bioindication. In addition, our experts in AI (GeorgiaTech, CentraleSupelec) and biodiversity (LOV, LIEC) have a strong record of fruitful interdisciplinary collaborations (co-supervised PhD, co-authored articles).