Satellite Data and Conservation: Gracie Ermi on Land Use, AI, and Protecting the Planet

Your AI Roadmap

Gracie Ermi, Machine Learning Scientist at Impact Observatory, highlights how AI and satellite data transform land use mapping and environmental protection. By analyzing satellite imagery with machine learning, she improves data analysis efficiency and accuracy. Gracie discusses AI's impact on wildlife conservation, interdisciplinary challenges, and the importance of trust with environmental experts. She also shares her career journey into satellite data and encourages women to explore computer science and AI in conservation.
Takeaways:
🤯 AI is enabling the geospatial field and environmental protection to expand in ways that weren't possible before.
🌍 Creating land use maps once took years. With AI we can produce global maps in weeks!
🌲 Find datasets and play with them to get involved with conservation and AI!
Resources Mentioned:
🌟 wildlife.ai
🌟 fruitpunch.ai
🌟 opensustain.tech
🌟 Conservation Tech Directory
🌟 AI for Conservation Slack
🌟 Wild Labs
🌟 Zooniverse
Podcast:
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About Gracie:
Gracie Ermi is a machine learning scientist specializing in building machine learning & AI applications to support wildlife research and inform environmental policy. She uses aerial imagery, remote sensing, bioacoustics and more to study marine and terrestrial species, and monitor global land use and land cover. As one of the co-creators of the Conservation Tech Directory, she works to encourage more connection and communication across the conservation technology field. Gracie also prioritizes educating and inspiring the next generation of innovators, and as an AAAS IF/THEN Ambassador and an AGU Voices for Science Fellow, she has spoken to over 150 audiences around the world about the intersection of conservation and technology.
Connect w/ Gracie on LinkedIn

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Kapitel

1. Introduction and Background (00:00:00)

2. Mapping Land Use and Land Cover (00:03:01)

3. Data Sources and Updates (00:05:25)

4. Use Cases and Applications (00:06:37)

5. Frequency of Imagery and Real-Time Data (00:07:39)

6. Satellite Data Sources (00:09:06)

7. Role of Machine Learning in Geospatial Analysis (00:09:37)

8. Labeling and Categorization of Land Features (00:10:40)

9. Improving Labeling and Data Quality (00:11:39)

10. Surprises and Innovations in the Field (00:12:40)

11. Data Size and Storage (00:14:25)

12. Observing Climate Change (00:14:57)

13. Geospatial Analysis vs. Computer Vision (00:17:23)

14. The Future of Geospatial Analysis (00:17:46)

15. Unlocking Hidden Insights in Vast Datasets (00:19:58)

16. Evolution of the Field (00:21:41)

17. Technology Adoption and Anxiety (00:23:10)

18. Gracie's Career Journey (00:24:26)

19. Women in Computer Science (00:26:26)

20. Further Education (00:29:03)

21. The Future of Computer Science (00:30:36)

22. Getting into the Field (00:32:25)

23. Gracie's Recommendations and Resources (00:34:05)

24. AI Conservation Volunteering and Data Sets (00:37:27)

25. Podcast Guest Nomination Form (00:39:25)