Join the Live Seminar
Research Seminar: Mapping biodiversity at very-high resolution in Europe
Talk Abstract
High-resolution biodiversity monitoring is important for research, conservation, and land management, yet fine-scale, up-to-date maps of species and habitats are still largely missing at the continental scale. To address this gap, we developed GeoPl@ntNet, an open-access web platform that makes European plant biodiversity explorable and actionable at a 50×50 meter resolution. The GeoPl@ntNet is built on top of the new GeoPlant dataset, the largest resource of its kind, including around 5M presence-only (PO) records and 100K standardized presence-absence (PA) surveys, together with multi-year satellite imagery, climate time series, and detailed environmental layers covering more than 10,000 plant species across Europe.
To transform this heterogeneous data into actionable information, GeoPl@ntNet uses a multimodal deep learning pipeline that combines remote sensing, climate, and observation data. A specialized deep species distribution model (deep-SDM) predicts species assemblages for each location, while a transformer-based model infers EUNIS habitat types from predicted species lists. The platform dynamically generates high-resolution maps and region-specific biodiversity "reports", including threatened and invasive species, habitat types, and other key indicators. Overall, GeoPl@ntNet bridges the gap between modern ecological modeling and practical conservation, providing an interactive, user-friendly gateway for researchers, decision-makers, and land managers to access, explore, and utilize biodiversity insights at a continental scale.
Get Your Invite
Not you? Click here to reset
Talk Abstract
High-resolution biodiversity monitoring is important for research, conservation, and land management, yet fine-scale, up-to-date maps of species and habitats are still largely missing at the continental scale. To address this gap, we developed GeoPl@ntNet, an open-access web platform that makes European plant biodiversity explorable and actionable at a 50×50 meter resolution. The GeoPl@ntNet is built on top of the new GeoPlant dataset, the largest resource of its kind, including around 5M presence-only (PO) records and 100K standardized presence-absence (PA) surveys, together with multi-year satellite imagery, climate time series, and detailed environmental layers covering more than 10,000 plant species across Europe.
To transform this heterogeneous data into actionable information, GeoPl@ntNet uses a multimodal deep learning pipeline that combines remote sensing, climate, and observation data. A specialized deep species distribution model (deep-SDM) predicts species assemblages for each location, while a transformer-based model infers EUNIS habitat types from predicted species lists. The platform dynamically generates high-resolution maps and region-specific biodiversity "reports", including threatened and invasive species, habitat types, and other key indicators. Overall, GeoPl@ntNet bridges the gap between modern ecological modeling and practical conservation, providing an interactive, user-friendly gateway for researchers, decision-makers, and land managers to access, explore, and utilize biodiversity insights at a continental scale.
Key takeaways
What you will learn:
Why and how to use AI for personalized content and product recommendations
How to organize content by tagging and enriching data and leverage AI models to create personalized content strategies
How to create a common language and framework for AI development to power marketing strategies
Meet the speaker
Lukáš Picek is a postdoctoral researcher at Inria (France) and an incoming Fulbright Visiting Scholar at MIT. His research focuses on the application of machine learning and computer vision to biodiversity and conservation, with a particular emphasis on large-scale species and individual animal recognition, distribution modeling, and remote sensing. Lukáš develops and evaluates deep learning methods for high-resolution biodiversity mapping and automated species identification, collaborating closely with ecologists and environmental scientists to create practical AI tools for real-world applications such as invasive species detection, habitat monitoring, conservation planning, and early warning systems for biodiversity loss. His work has supported a range of stakeholders, including field biologists, conservation NGOs, land managers, and policy-makers by making advanced ecological insights accessible, actionable, and scalable.
Want to stay up to date with all the AI tends
Clarifai was built to simplify how developers and teams create, share, and run AI at scale
Accelerate data labeling 100X
Establish an AI Operating Model and get out of prototype and into production
Build your next AI app, test and tune popular LLMs models, and much more.

