ecosystems

Events

images/02_events/adapting%20ML.png#joomlaImage://local-images/02_events/adapting ML.png?width=800&height=300
Wednesday, February 26th 2025 | 17:00 - 18:00 p.m.

Remote sensing and machine learning for environmental monitoring: Opportunities and challenges

online

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. 

Institution

  • AI for Good

Universität Hamburg
Adeline Scharfenberg
Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein. 

Universität Hamburg
Adeline Scharfenberg
Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein. 

Universität Hamburg
Adeline Scharfenberg
Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein.