As environmental change sweeps our planet, capturing and predicting threats to communities and ecosystems becomes increasingly critical. Satellite remote sensing enables us to analyze key climate threats from the hyper-local to the global scale, with applications ranging from temperature and drought monitoring to capturing urgent hotspots of deforestation and biodiversity stress. 

Machine learning now provides the opportunity to capture landscape changes and predict environmental stressors with unprecedented accuracy and scale. In this talk, participants will learn about key case studies in leveraging remote sensing and machine learning to better capture critical environmental changes in their communities and other critically vulnerable areas around the world. We will complete an end-to-end demonstration of ingesting publicly available satellite data from Google Earth Engine, an open-source geospatial analysis platform, and completing a machine learning classification of forest loss in the Amazon Rainforest. 

Liza Goldberg will be completing a live demonstration with Google Earth Engine during this talk. To enhance your experience and make the talk more interactive, we encourage you to join Google Earth Engine. This powerful platform will provide you with a hands-on way to explore satellite imagery and data, which will significantly deepen your understanding of the topics discussed.

Please visit the following URL to learn how to join Google Earth Engine:

https://courses.spatialthoughts.com/gee-sign-up.html

Ultimately, this session will guide participants in understanding how Earth observation technology can increasingly create transparency around critical indicators of global change, enabling more informed policy decision-making to protect our planet.