To Our Readers

If you are here wondering how to teach AI to "read maps," then welcome—you are in the right place. Through this treasure map we have put together, we hope to embark on a journey with you into the world of GeoAI, equipping AI with a mind capable of understanding the physical world.

The authors of this book are not my fellow colleagues, but the master's students, PhD candidates, and postdoctoral researchers from my lab. Why did we write it? Because I firmly believe in Richard Feynman's famous maxim:

"What I cannot create, I do not understand."

Here, accessible technical writing serves as a form of deep learning—from digesting the technical nuances of top-tier conference papers and iteratively refining drafts with AI tools, to debating and polishing ideas during our group meetings. I believe that through this writing process, our students have truly waded into the river of AI.

Therefore, this is not meant to be an "authoritative guide," but rather a living notebook that captures their journey of learning and reflection.

We are hosting this on a Wiki so that every reader has the opportunity to become the next co-author. Feel free to leave a comment at the bottom of the page to point out errors, contribute new case studies, or even share fresh experimental results you have run yourself.

We will integrate valuable insights directly into the main text, allowing this book to continuously evolve alongside the technology and benefit a wider community of learners. After all, we believe that true knowledge comes alive through open collaboration.