AI4Biodiveristy project

In this talk, we will describe the AI4Biodiveristy project we are undertaking as part of a Vatat supported grant for data science based research projects.


  • We envision a global hybrid human-computational system that provides timely information on a large number of species across the entire globe, allowing policy makers and conservation agencies to respond quickly with effective mitigation plans.
  • Furthermore, we foresee increasing people’s affinity to nature and thriving environmentally-conscious communities. As part of our vision, education systems would engage students with nature, shaping their awareness and attitude towards environmental issues.


  • To develop scalable tools and methods for biodiversity monitoring and estimation of populations of terrestrial vertebrate species, which would inform best practices in the field and will be adopted by nature protection agencies.
  • Furthermore, employing the biodiversity maps that we will produce, we aim to develop state-of-nature reports, which will inform policy recommendations and intervention plans for nature conservation.
  • Additionally, we plan working towards strengthening communities of, as well as collaborate with educators in developing curricular material related to conservation.

Prof. Ilan Shimshoni. Project’s PI. Department of Information Systems, University of Haifa

Ilan Shimshoni is a Professor of Information Systems. His research areas are computer vision, computer graphics and robotics. In recent years he has also been developing algorithms in CV for solving problems in Archeology, Rehabilitation, Geography, and especially ecology and agriculture. He served as the chair of the IS department and was an Associate Editor for IEEE TPAMI, the leading journal in the field of computer vision.

A Social computing platform for Biodiversity monitoring

In this talk, we aim to explore the utilization of human computation and machine learning techniques to expedite and enhance the tedious tasks faced by ecologists in monitoring biodiversity. By working towards the integration of human and machine intelligence, our intermediate goal is to create a crowd-computing platform accessible for citizen scientists, allowing them to augment or even substitute the efforts of a single expert, thereby expediting the process considerably.

Should our system prove successful, it will enable ecologists to harness algorithms and crowdsourced assistance to produce accurate and prompt assessments of nature’s status. Such data will be crucial for conservation organizations and authorities in formulating effective measures. Additionally, this approach is expected to strengthen the connection between citizen scientists and nature, while raising awareness about the importance of wildlife sustainability.