AI-RemoteSensing
Overview
Note: This work is currently in progress and is still in its early stages of development. I have plans to incorporate a larger volume of Jupyter notebooks soon and enhance the existing ones. I appreciate your patience and understanding.
Welcome to AI Remote Sensing, a collection of Jupyter and Google Colaboratory notebooks dedicated to leveraging Artificial Intelligence (AI) in Remote Sensing applications. This repository serves as an open-source platform for researchers, educators, and professionals to explore, learn, and expand their knowledge in this exciting intersection of technologies.
These notebooks range from introductory concepts to advanced applications, encompassing a wide variety of techniques such as classification, segmentation, object detection, spectroscopy analysis, and optical and multispectral image processing.
Whether you're a student seeking educational materials, a researcher eager to delve into the frontier of AI developments, or a professional exploring pragmatic applications, I hope you find these resources valuable. These materials are meticulously curated and designed to serve as a comprehensive guide, assisting you in exploring and harnessing the power of AI in remote sensing, spectroscopy, and image processing.
Content
The notebooks are organized into different categories based on the complexity and the application of the AI techniques:
- Beginner: These notebooks introduce basic concepts and techniques, perfect for those new to AI or remote sensing.
- Intermediate: Here you'll find notebooks that delve into more complex topics and applications.
- Advanced: These notebooks tackle cutting-edge research topics and complex real-world applications.
The repository is further divided based on the type of data processing:
- Spectroscopy Data Processing: Here you'll find notebooks and resources related to spectroscopy data processing. Ideal for those interested in studying and analyzing the interaction between matter and electromagnetic radiation.
- Image Data Processing: In this section, you'll find notebooks and resources focused on image data processing. Perfect for those interested in extracting valuable information from digital images.
Usage
All notebooks are ready to run. You can execute them in your local Jupyter environment or directly in Google Colaboratory. Please ensure that you have the necessary libraries and dependencies installed.
Contributions
Contributions to this repository are more than welcome. If you have a notebook you'd like to share, feel free to make a pull request. We appreciate any contributions that help to enrich this learning resource.
License
This project is licensed under the MIT License - see the LICENSE file for details. This means you're free to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the software, as long as you provide appropriate credit.
Contact
If you have any questions or suggestions, please open an issue on this repository. We'd love to hear your feedback and ideas to make this resource even better!
How to Cite
@online{AIRemoteSensing2023_GitHub,
author = {Lucas Prado Osco},
title = {AI-RemoteSensing},
year = {2023},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/LucasOsco/AI-RemoteSensing}},
commit = {May}
}@misc{AIRemoteSensing2023_Zenodo,
author = {Lucas Osco},
title = {{AI-RemoteSensing: a collection of Jupyter and
Google Colaboratory notebooks dedicated to
leveraging Artificial Intelligence (AI) in Remote
Sensing applications}},
month = jun,
year = 2023,
publisher = {Zenodo},
version = {educational},
doi = {10.5281/zenodo.8092269},
url = {https://doi.org/10.5281/zenodo.8092269}
}