Project Proposal Template

Project Proposal (Due: Week 3)

Student Name: [Your Name]
Project Title: [Descriptive Project Title]
Date: [Submission Date]

Problem Statement and Objectives

Problem Description (3-4 sentences): - [Clearly describe the problem you’re addressing] - [Why is this problem important? What is the broader significance?] - [What are the current limitations or gaps in existing approaches?]

Research Objectives (2-3 bullet points): - [Specific, measurable goals for your project] - [What specific outcomes will you deliver?]

Dataset Description

Primary Dataset(s): - Source: [e.g., Landsat, Sentinel, MODIS, custom dataset] - Spatial Coverage: [Geographic extent] - Temporal Coverage: [Time period] - Resolution: [Spatial and temporal resolution] - Size: [Approximate data volume]

Ground Truth/Labels (if applicable): - [Description of labeled data for training/validation] - [Source and quality of labels]

Data Availability Assessment: - [x] Data is readily accessible - [ ] Data requires special access/permissions - [ ] Data needs to be collected/generated - [ ] Alternative data sources identified

Technical Approach

Foundation Model Selection: - Primary Model: [e.g., Prithvi, SatMAE, custom architecture] - Justification: [Why is this model appropriate for your problem?] - Baseline Comparisons: [What will you compare against?]

Methodology Overview (4-5 sentences): - [High-level description of your approach] - [Key technical components and workflow] - [How will you adapt/fine-tune the foundation model?]

Key Technical Components: - [ ] Data preprocessing pipeline - [ ] Model fine-tuning/adaptation - [ ] Evaluation framework - [ ] Visualization/interpretation tools - [ ] Scalable inference pipeline

Timeline and Milestones

Week 4-5: [Specific goals and deliverables] Week 6: [Specific goals and deliverables]
Week 7 (MVP): [What will your MVP demonstration include?] Week 8-9: [Final implementation goals] Week 10: [Final presentation preparation]

Evaluation Strategy

Performance Metrics: - [How will you measure success? Specific metrics] - [Quantitative measures: accuracy, F1, IoU, etc.] - [Qualitative measures: interpretability, usability, etc.]

Validation Approach: - [Cross-validation strategy] - [Train/validation/test splits] - [Spatial/temporal validation considerations]

Feasibility Analysis

Technical Risks and Mitigation: - Risk 1: [Description] → Mitigation: [How you’ll address it] - Risk 2: [Description] → Mitigation: [How you’ll address it]

Resource Requirements: - Computational: [GPU hours, memory requirements, storage] - Data: [Download time, storage space, preprocessing time] - Time: [Realistic assessment of scope given 10-week timeframe]

Backup Plans: - [What will you do if primary approach doesn’t work?] - [How can you ensure you have a working system for final presentation?]

Expected Outcomes

Primary Deliverables: - [What specific outputs will your project produce?] - [Code repository, trained models, analysis results, etc.]

Potential Impact: - [How could this work be used in practice?] - [What are the broader implications of success?]

Future Work: - [How could this project be extended beyond the course?] - [What are the next logical steps?]

Resources and References

Key Literature (3-5 papers): - [List relevant papers that inform your approach]

Software/Tools: - [List key libraries, platforms, tools you’ll use]

Additional Resources: - [Any other resources, datasets, collaborations, etc.]

--- title: "Project Proposal Template" subtitle: "GEOG 288KC: Geospatial Foundation Models and Applications" format: html --- ## Project Proposal (Due: Week 3) **Student Name**: [Your Name] **Project Title**: [Descriptive Project Title] **Date**: [Submission Date] ### Problem Statement and Objectives **Problem Description** (3-4 sentences): - [Clearly describe the problem you're addressing] - [Why is this problem important? What is the broader significance?] - [What are the current limitations or gaps in existing approaches?] **Research Objectives** (2-3 bullet points): - [Specific, measurable goals for your project] - [What specific outcomes will you deliver?] ### Dataset Description **Primary Dataset(s)**: - **Source**: [e.g., Landsat, Sentinel, MODIS, custom dataset] - **Spatial Coverage**: [Geographic extent] - **Temporal Coverage**: [Time period] - **Resolution**: [Spatial and temporal resolution] - **Size**: [Approximate data volume] **Ground Truth/Labels** (if applicable): - [Description of labeled data for training/validation] - [Source and quality of labels] **Data Availability Assessment**: - [x] Data is readily accessible - [ ] Data requires special access/permissions - [ ] Data needs to be collected/generated - [ ] Alternative data sources identified ### Technical Approach **Foundation Model Selection**: - **Primary Model**: [e.g., Prithvi, SatMAE, custom architecture] - **Justification**: [Why is this model appropriate for your problem?] - **Baseline Comparisons**: [What will you compare against?] **Methodology Overview** (4-5 sentences): - [High-level description of your approach] - [Key technical components and workflow] - [How will you adapt/fine-tune the foundation model?] **Key Technical Components**: - [ ] Data preprocessing pipeline - [ ] Model fine-tuning/adaptation - [ ] Evaluation framework - [ ] Visualization/interpretation tools - [ ] Scalable inference pipeline ### Timeline and Milestones **Week 4-5**: [Specific goals and deliverables] **Week 6**: [Specific goals and deliverables] **Week 7 (MVP)**: [What will your MVP demonstration include?] **Week 8-9**: [Final implementation goals] **Week 10**: [Final presentation preparation] ### Evaluation Strategy **Performance Metrics**: - [How will you measure success? Specific metrics] - [Quantitative measures: accuracy, F1, IoU, etc.] - [Qualitative measures: interpretability, usability, etc.] **Validation Approach**: - [Cross-validation strategy] - [Train/validation/test splits] - [Spatial/temporal validation considerations] ### Feasibility Analysis **Technical Risks and Mitigation**: - **Risk 1**: [Description] → **Mitigation**: [How you'll address it] - **Risk 2**: [Description] → **Mitigation**: [How you'll address it] **Resource Requirements**: - **Computational**: [GPU hours, memory requirements, storage] - **Data**: [Download time, storage space, preprocessing time] - **Time**: [Realistic assessment of scope given 10-week timeframe] **Backup Plans**: - [What will you do if primary approach doesn't work?] - [How can you ensure you have a working system for final presentation?] ### Expected Outcomes **Primary Deliverables**: - [What specific outputs will your project produce?] - [Code repository, trained models, analysis results, etc.] **Potential Impact**: - [How could this work be used in practice?] - [What are the broader implications of success?] **Future Work**: - [How could this project be extended beyond the course?] - [What are the next logical steps?] ### Resources and References **Key Literature** (3-5 papers): - [List relevant papers that inform your approach] **Software/Tools**: - [List key libraries, platforms, tools you'll use] **Additional Resources**: - [Any other resources, datasets, collaborations, etc.]