AI for Community Science Biodiversity Monitoring
AI4Biodiversity project aims to fuse AI, human computation and other technological means in
processing biodiversity data that is based on unstructured monitoring protocols. We are proposing a hybrid human-computational system that combines artificial intelligence and human input to collect, improve, and aggregate
biodiversity data.
Vision
With the use of advanced technology, crowdsourcing, and community-based knowledge, we’re aiming to build a social computing system that provides timely species information for effective conservation efforts. We aim to increase people’s affinity with nature and create environmentally-conscious communities and collaborate with educators to develop curricular material related to conservation.
Mission
We are using data-science techniques to make sense of unsystematic biodiversity image data, unleashing its scientific potential. The AI and human-computational hybrid system enables dealing with the issues like Internal validity, the accuracy of species’ identification in crowdsourced photos and external validity, the ability to infer from such unsystematic and biased observations.
Striving towards this visions, our mission is to develop scalable tools and methods for biodiversity monitoring and assessment of habitat dynamics, which would inform best practices in the field and will be adopted by nature protection agencies.
In line with the UN’s vision as articulated in the Sustainable Development Goals (SDGs), we plan working towards strengthening communities of, as well as collaborate with educators in developing curricular material related to conservation.
Our multi-disciplinary research team– AI4Biodiversity – consists of experts in the areas of: ecology and biodiversity monitoring (Prof. Malkinson); artificial intelligence (Prof. Shimshoni), specifically machine learning techniques for image recognition; online communities and human computation (Prof. Arazy and Dr. Nagar); and statistics (Dr. Nov). We aims to address the challenge of biodiversity monitoring through the development of novel hybrid human-computational systems. Namely, the primary scientific novelty of the proposed project is in the smart combination of artificial intelligence (AI) machinery and human (or social) technologies. We plan to fuse human and computational approaches in the three stages of the proposed pipeline: data collection, data betterment, and data aggregation, as illustrated below.
For more information about the AI and human-computational hybrid system go to our system
