ecosystems

Explore the transformative potential of the Population Dynamics Foundation Model (PDFM), a cutting-edge AI model designed to capture complex, multidimensional interactions among human behaviors, environmental factors, and local contexts. This workshop provides an in-depth introduction to PDFM Embeddings and their applications in geospatial analysis, public health, and socioeconomic modeling. 

Participants will gain hands-on experience with PDFM Embeddings to perform advanced geospatial predictions and analyses while ensuring privacy through the use of aggregated data. Key components of the workshop include: 

• Introduction to PDFM Embeddings: Delve into the model architecture of PDFM and discover how aggregated data (such as search trends, busyness levels, and weather conditions) generates location-specific embeddings.
• Data Preparation: Learn to integrate ground truth data, including health statistics and socioeconomic indicators, with PDFM Embeddings at the postal code or county level.
• Hands-On Exercises: Engage with interactive Colab notebooks to explore real-world applications, such as predicting housing prices using Zillow data and nighttime light predictions with Google Earth Engine data.
• Visualization and Interpretation: Analyze and visualize geospatial predictions and PDFM features in 3D, enhancing your ability to interpret complex datasets. 

By the end of this workshop, participants will have a strong foundation in utilizing PDFM Embeddings to address real-world geospatial challenges. 

As the planet faces accelerating environmental degradation and biodiversity loss, emerging technologies have an increasingly vital role to play in enhancing conservation efforts. Artificial intelligence (AI), in particular, is emerging as a powerful tool to address complex environmental challenges, from monitoring ecosystems and predicting habitat changes to combatting illegal wildlife trafficking. 

In this webinar, Professor Tshilidzi Marwala, United Nations Under-Secretary-General and Rector of the United Nations University (UNU), will outline the transformative potential of AI for biodiversity and conservation. He will examine how AI can support the ambitious targets of the Kunming-Montreal Global Biodiversity Framework by enabling scalable, data-driven solutions that enhance our ability to understand and protect our ecosystems. 

Drawing on UNU’s global work at the intersection of AI and sustainability, Prof. Marwala will also reflect on the ethical, environmental, and equity-related challenges associated with AI, such as algorithmic bias, the digital divide, and its carbon footprint. Realising the promise of AI for biodiversity conservation will depend on our ability to govern it wisely, ensuring it serves as a force for equity, sustainability, and environmental stewardship.

Learning Objectives: 

By the end of this webinar, participants will have developed their ability to:

• Identify key facing global biodiversity and conservation efforts.
• Describe the potential applications and limitations of artificial intelligence in addressing these challenges.
• Analyse real-world case studies illustrating how AI is being used to support biodiversity monitoring and conservation.
• Evaluate the role of the United Nations University in advancing research and innovation at the intersection of AI, sustainability, and biodiversity.

Estimating the geographic range of a species from sparse observations is a challenging and important geospatial prediction problem. Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. This problem has a long history in ecology, but traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets which can include tens of millions of observations of hundreds of thousands of species in addition to multi-modal image and text data. In this talk, I will present recent work from my group on deep learning-based solutions for estimating species’ ranges from incomplete data. I will also discuss some of the open challenges that exist in this space.  

Learning Objectives:  

1. Understand the capabilities of current deep learning methods for species range estimation.
2. Recognise the limitations of these models in the context of current open challenges in this space