The rapid advancements in artificial intelligence (AI) and geospatial technologies have paved the way for the development of Geospatial AI (GeoAI) strategies, enabling organizations to harness AI for complex geospatial analysis. This repository is a one-stop shop that brings together the resources for practical GeoAI applications for students, practitioners, and scientists. This initivative is founded on open science principles and building capacity to make this exciting technology available to everyone.
This GeoAI framework aims to transform how we process, analyze, and interpret geospatial data by integrating AI-driven models into collaborative efforts. The goal is to enhance environmental sustainability by leveraging Earth Observation Foundation Models (EOFMs), Large Language Models (LLMs), and prompt engineering. Our vision is to create an open, modular, and scalable platform that seamlessly integrates these technologies, ensuring that our organization stays at the forefront of innovation in environmental research and decision-making. By keeping the framework open and welcoming contributions from the broader community, we promote transparency, collaboration, and shared progress in GeoAI.
1. Integrating GeoAI Technologies:
- Leverage AI foundation models to improve the accuracy and efficiency of geospatial data analysis.
- Apply Large Language Models (LLMs) to extract insights from textual data and adapt them for specific environmental tasks.
- Use prompt engineering to refine interactions with models, generating more precise and relevant outputs.
2. Fostering Open Science and Collaboration:
- Promote continuous learning and innovation through collaborative research and open science initiatives.
- Partner with technology providers and community groups to ensure a human-centered approach in GeoAI applications.
3. Developing User-Inspired Platforms:
- Design platforms that simplify interaction with complex geospatial data, making insights accessible and actionable for diverse users.
- Ensure these platforms are adaptable, user-friendly, and capable of evolving with new technologies and user needs.
4. Promoting Sustainable Practices
- Advocate for the use of shared, well-optimized GeoAI models to reduce resource consumption and redundancy.
- Promote the adoption of high-quality, pre-trained models to conserve computational resources and minimize the carbon footprint associated with model training.
| Abbreviation | Title | Publication | Paper/Website | Code & Weights |
|---|---|---|---|---|
| Clay | Clay Foundation Model | null | null | link |
| Privthi | NASA IBM AI Foundation Model for Earth Observations | arXiv | Privthi | link |
| earthPT | ASPIASPACE’s earthPT | arXiv | earthPT | link |
| SAM2 | Meta's Segment Anything Model 2 | TGRS2021 | SAM2 | link |
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Spatial Informatics Group https://sig-gis.com/