Loading thesis research...
Geospatial Foundation Models
AI-Powered Earth Observation and Satellite Imagery Analysis
Thesis at a Glance
Key Insights
- 93.75% accuracy from frozen embeddings — no fine-tuning needed
- Agrees with expert interpreters more often than its own training labels (95.3% vs 91.7%)
- Transfers across years 2018–2023 with no retraining (92.5–94.3%)
- 140× fewer labels at a cost of 1.3 points of overall accuracy
Research Timeline
Literature Review Started
June 2025
TerraMind & AlphaEarth Studies
September 2025
Experiments & Blind Human Validation
2026
Thesis Approved
2 July 2026
Thesis Defended
22 July 2026
arXiv Preprint & SpringerBriefs Chapter
Planned
Future Work
TESSERA Comparison
TESSERA, from Cambridge researchers, provides precomputed FAIR global pixel embeddings for Earth representation and analysis — a natural comparison point for AlphaEarth. Comparison notebooks (12–15) were built during the thesis but deliberately kept outside the submitted scope, making this the first candidate for follow-up work.
Research Potential:
- • Comparative analysis with AlphaEarth and TerraMind
- • Architecture differences and performance benchmarks
- • FAIR (Findable, Accessible, Interoperable, Reusable) approach impact
- • Real-world application scenarios and limitations
TerraMind GFM
Comprehensive Analysis
Deep dive into TerraMind's geospatial foundation model architecture, capabilities, and applications in satellite imagery analysis.
AlphaEarth
Google DeepMind Embeddings
Analysis of Google's AlphaEarth foundation model for global mapping from sparse label data and its breakthrough innovations.
Methodology
Research Approach
Detailed research methodology, comparative analysis framework, and evaluation metrics for geospatial foundation models.