SERVIR Applied Deep Learning Book
Use satellite data and geospatial technology to address critical challenges in weather & climate resilience, agriculture and food security, ecosystem and carbon management, water security, disasters, as well as air quality and health
https://github.com/nasa-earthrise/earthrise-applied-artificial-intelligence-and-deep-learning-book
Category: Sustainable Development
Sub Category: Education
Keywords
deeplearning geoai remotesensing
Last synced: about 3 hours ago
JSON representation
Repository metadata
- Host: GitHub
- URL: https://github.com/nasa-earthrise/earthrise-applied-artificial-intelligence-and-deep-learning-book
- Owner: NASA-EarthRISE
- Created: 2024-06-17T19:29:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-10-01T17:00:58.000Z (4 months ago)
- Last Synced: 2026-01-30T05:32:25.878Z (14 days ago)
- Topics: deeplearning, geoai, remotesensing
- Language: Jupyter Notebook
- Homepage: https://nasa-earthrise.github.io/EarthRISE-Applied-Artificial-Intelligence-and-Deep-Learning-Book/
- Size: 46.3 MB
- Stars: 40
- Watchers: 7
- Forks: 13
- Open Issues: 0
- Releases: 0
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Metadata Files:
- Readme: README.md
README.md
EarthRISE Applied Artificial Intelligence and Deep Learning Book
The NASA EarthRISE program harnesses NASA Earth Action capabilities to deliver trusted solutions for sustained benefits to society. Collabroting with State and Local partners and striving to strengthens the capacities of partners to use satellite data and geospatial technology to address critical challenges. EarthRISE co-develops innovative solutions through a network of partners to improve resilience and sustainable resource management across scales. Additionally, EarthRISE focuses on developing participate in innovative knowledge products such as the SAR Handbook and the GEE book designed to support capacity building in applying Remote Sensing and geospatial approaches to address challenges.
The focus of the EarthRISE Applied Artificial Intelligence and Deep Learning Book is to provide practitioners with a wide variety of applied examples of Remote Sensing Artificial Intelligence and Deep Learning approaches. With each chapter focusing on a specific problem set such as object detection of downscaling using Deep Learning. Additionally, throughout the books chapters various examples are provided spanning the aforementioned NASA Earth Action thematic areas. Thereby providing a wide variety of thematic applications to complement reader’s domain specific practical knowledge such as agronomy or forestry etc.
We suspect readers are coming to this virtual book with preexisting geospatial expertise. However, limited Artificial Intelligence and Deep Learning knowledge
Each chapter contains both the theoretical background as well as a practical hand-on section facilitated through virtual notebooks. Finally, this book spans a variety of platforms such as TensorFlow and PyTorch to provide readers with a wide set of examples.
Applied Deep Learning Book Outline
- Introduction
- Data Preparation
- Semantic Segmentation
- Object Detection
- Ecological Processes Simulation
- Transfer Learning
- Fusion
- Downscaling
- Future of Deep Learning and Foundational Models
- Ethics of Artificial Intelligence
- Conclusions
License and Distribution
SERVIR-Applied-Deep-Learning-Book is distributed by SERVIR under the terms of the MIT License. See
LICENSE in this directory for more information.
Privacy & Terms of Use
EarthRISE Applied Artificial Intelligence and Deep Learning Book abides to all of EarthRISE's privacy and terms of use
Owner metadata
- Name: NASA EarthRISE
- Login: NASA-EarthRISE
- Email:
- Kind: organization
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- Icon url: https://avatars.githubusercontent.com/u/220915817?v=4
- Repositories: 1
- Last ynced at: 2025-09-18T18:35:45.826Z
- Profile URL: https://github.com/NASA-EarthRISE
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- Fork event: 1
Last Year
- Watch event: 1
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Last synced: about 18 hours ago
Total Commits: 101
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Avg Commits per committer: 33.667
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Avg Commits per committer in past year: 9.0
Development Distribution Score (DDS) in past year: 0.167
| Name | Commits | |
|---|---|---|
| Tim Mayer | 4****1 | 96 |
| Biplov Bhandari | b****5@g****m | 4 |
| Meryl | 8****f | 1 |
Issue and Pull Request metadata
Last synced: about 1 month ago
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Total pull requests: 7
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Average time to close pull requests: about 3 hours
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Score: 4.787491742782046