syllabus

GEOG 288KC: Geospatial Foundation Models and Applications

Fall 2025 Fridays 9am-12pm + optional lab office hours Fridays 2pm-5pm

Course Overview

This accelerated, hands-on seminar provides practical skills for working with state-of-the-art geospatial foundation models. Students learn to access, process, and analyze satellite imagery using modern tools, apply foundation models to real-world problems, and implement independent projects in environmental monitoring and analysis. The course emphasizes immediately applicable skills rather than theoretical foundations.

Prerequisites

Students should have some experience with remote sensing, geospatial data, or ML (e.g., Python, Earth Engine, PyTorch)
Access to UCSB AI Sandbox for computational resources
Basic familiarity with satellite imagery and environmental applications

Applications

To apply, students should submit a paragraph at the form link below describing their past experience with remote sensing, geospatial data, and ML, as well as their interest in applying Geospatial Foundation Models to practical problems. They should describe a specific geospatial application area they want to explore. The more clearly defined the target application and any existing datasets the better.

https://forms.gle/Q1iDp2kuZuX1avMPA

—

Course Structure: 10-Week Format

📚 Phase 1: Structured Learning (Weeks 1-5)

Week 1: Core Tools and Data Access (STAC APIs, satellite data visualization)
Week 2: Remote Sensing Preprocessing (Cloud masking, reprojection, compositing)
Week 3: Machine Learning on Remote Sensing (CNN training, land cover classification)
Week 4: Foundation Models in Practice (Loading pretrained models, feature extraction)
Week 5: Fine-Tuning & Transfer Learning (Linear probing vs. full fine-tuning, adaptation strategies)

🎯 Phase 2: Independent Project Development (Weeks 6-10)

Week 6: Project Proposals & Planning (Define scope, methodology, expected outcomes)
Week 7: Initial Implementation (Develop minimum viable product, early results)
Week 8: Development & Refinement (Expand functionality, optimize performance)
Week 9: Analysis & Results (Generate final results, prepare visualizations)
Week 10: Final Presentations (Present completed projects, peer review, submission)

Deliverables

Phase 1: Structured Learning (Weeks 1-5)

Week 1: Working data access pipeline using STAC APIs
Week 2: Complete preprocessing workflow for satellite imagery
Week 3: Trained CNN model for land cover classification
Week 4: Working foundation model integration and feature extraction
Week 5: Fine-tuning implementation and project proposal

Phase 2: Independent Project Development (Weeks 6-10)

Week 6: Detailed project proposal with methodology and timeline
Week 7: Minimum viable product (MVP) with initial results
Week 8-9: Iterative development, analysis, and documentation
Week 10: Final presentation, complete project code, and written report

Optional: Submit project results to GitHub, Hugging Face, or present at student showcase

Grading

This course will be assessed on a pass/fail basis. Passing requires:

Consistent attendance and participation (Weeks 1-5)
Submission of all structured learning deliverables (Weeks 1-5)
Project proposal submission (Week 6)
MVP demonstration (Week 7)
Final project presentation and submission (Week 10)

## **GEOG 288KC: Geospatial Foundation Models and Applications** **Fall 2025** **Fridays 9am-12pm \+ optional lab office hours Fridays 2pm-5pm** --- ### **Course Overview** This accelerated, hands-on seminar provides practical skills for working with state-of-the-art geospatial foundation models. Students learn to access, process, and analyze satellite imagery using modern tools, apply foundation models to real-world problems, and implement independent projects in environmental monitoring and analysis. The course emphasizes immediately applicable skills rather than theoretical foundations. --- ### **Prerequisites** * Students should have some experience with remote sensing, geospatial data, or ML (e.g., Python, Earth Engine, PyTorch) * Access to UCSB AI Sandbox for computational resources * Basic familiarity with satellite imagery and environmental applications --- ### **Applications** To apply, students should submit a paragraph at the [form link](https://forms.gle/Q1iDp2kuZuX1avMPA) below describing their past experience with remote sensing, geospatial data, and ML, as well as their interest in applying Geospatial Foundation Models to practical problems. They should describe a specific geospatial application area they want to explore. The more clearly defined the target application and any existing datasets the better. [https://forms.gle/Q1iDp2kuZuX1avMPA](https://forms.gle/Q1iDp2kuZuX1avMPA) ### --- ### **Course Structure: 10-Week Format** #### **📚 Phase 1: Structured Learning (Weeks 1-5)** * **Week 1**: Core Tools and Data Access *(STAC APIs, satellite data visualization)* * **Week 2**: Remote Sensing Preprocessing *(Cloud masking, reprojection, compositing)* * **Week 3**: Machine Learning on Remote Sensing *(CNN training, land cover classification)* * **Week 4**: Foundation Models in Practice *(Loading pretrained models, feature extraction)* * **Week 5**: Fine-Tuning & Transfer Learning *(Linear probing vs. full fine-tuning, adaptation strategies)* #### **🎯 Phase 2: Independent Project Development (Weeks 6-10)** * **Week 6**: Project Proposals & Planning *(Define scope, methodology, expected outcomes)* * **Week 7**: Initial Implementation *(Develop minimum viable product, early results)* * **Week 8**: Development & Refinement *(Expand functionality, optimize performance)* * **Week 9**: Analysis & Results *(Generate final results, prepare visualizations)* * **Week 10**: Final Presentations *(Present completed projects, peer review, submission)* --- ### **Deliverables** **Phase 1: Structured Learning (Weeks 1-5)** * **Week 1**: Working data access pipeline using STAC APIs * **Week 2**: Complete preprocessing workflow for satellite imagery * **Week 3**: Trained CNN model for land cover classification * **Week 4**: Working foundation model integration and feature extraction * **Week 5**: Fine-tuning implementation and project proposal **Phase 2: Independent Project Development (Weeks 6-10)** * **Week 6**: Detailed project proposal with methodology and timeline * **Week 7**: Minimum viable product (MVP) with initial results * **Week 8-9**: Iterative development, analysis, and documentation * **Week 10**: Final presentation, complete project code, and written report **Optional**: Submit project results to GitHub, Hugging Face, or present at student showcase --- ### **Grading** This course will be assessed on a pass/fail basis. Passing requires: * Consistent attendance and participation (Weeks 1-5) * Submission of all structured learning deliverables (Weeks 1-5) * Project proposal submission (Week 6) * MVP demonstration (Week 7) * Final project presentation and submission (Week 10)