Remote Sensing Data Scientist

[Code block moved to code-block-1.md]

Decision Framework

Thinking Patterns

§ 10 · Common Pitfalls & Anti-Patterns

→ See references/code-block-1.md for spatial cross-validation code. → See references/code-block-2.md for uncertainty estimation code.

Key Anti-Patterns:

• Random pixel split inflates accuracy by 10-20% — use spatial blocking
• Sensor mixing without cross-calibration causes silent errors — use HLS data
• SAR speckle violates statistical assumptions — use multilooking and zonal stats
• Phenological change creates false positives — compare same-season composites
• No uncertainty prevents risk-calibrated decisions — export confidence maps

§ 11 · Integration with Other Skills

§ 12 · Scope & Limitations

Use when:

• Processing Sentinel-1/2, Landsat-8/9, Planet, or COSMO-SkyMed satellite imagery for land cover, change detection, or biophysical parameter retrieval.
• Designing geospatial deep learning training pipelines with torchgeo, SegFormer, or U-Net for semantic segmentation of satellite imagery.
• Building operational change detection systems for deforestation monitoring, flood mapping, or agricultural crop monitoring.
• Developing Google Earth Engine scripts for cloud-scale geospatial time series analysis.
• Validating and reporting remote sensing product accuracy with Kappa, mIoU, and F1 metrics using proper spatial methodology.

Do NOT use when:

• Real-time satellite tasking and constellation management — requires satellite operations engineering expertise.
• InSAR ground deformation monitoring at millimeter precision — requires specialized geodetic processing with StaMPS or MintPy.
• Hyperspectral unmixing for mineral mapping (400+ bands) — requires spectroscopic expertise beyond this skill scope.
• Sub-daily operational numerical weather prediction from satellite radiances — use meteorological satellite specialist.

Alternatives:

• For SAR interferometry (InSAR deformation): geodetic InSAR specialist with MintPy focus.
• For satellite constellation operations and link budget: satellite communication engineer skill.

§ 14 · Quality Verification

→ See references/standards.md §7.10 for full checklist

§ 16 · Domain Deep Dive

Specialized Knowledge Areas

Knowledge Maturity Model

§ 17 · Risk Management Deep Dive

🔴 Critical Risk Register

🟠 Risk Response Strategies

🟡 Early Warning Indicators

• Stakeholder engagement dropping
• Requirement changes increasing
• Team velocity declining
• Defect rates rising

§ 18 · Excellence Framework

World-Class Execution Standards

Excellence Cycle

ASSESS → PLAN → EXECUTE → REVIEW → IMPROVE
   ↑                              ↓
   └────────── MEASURE ←──────────┘

§ 19 · Best Practices Library

Industry Best Practices

§ 21 · Resources & References

Performance Metrics

Additional Resources

• Industry standards
• Best practice guides
• Training materials

References

Detailed content:

• ## § 2 · What This Skill Does
• ## § 3 · Risk Disclaimer
• ## § 4 · Core Philosophy
• ## § 6 · Professional Toolkit
• ## § 7 · Standards & Reference
• ## § 8 · Workflow
• ## § 9 · Scenario Examples
• ## § 20 · Case Studies

Examples

Example 1: Standard Scenario

Input: Handle standard remote sensing data scientist request with standard procedures Output: Process Overview:

1. Gather requirements
2. Analyze current state
3. Develop solution approach
4. Implement and verify
5. Document and handoff

Standard timeline: 2-5 business days

Example 2: Edge Case

Input: Manage complex remote sensing data scientist scenario with multiple stakeholders Output: Stakeholder Management:

• Identified 4 key stakeholders
• Requirements workshop completed
• Consensus reached on priorities

Solution: Integrated approach addressing all stakeholder concerns

Error Handling & Recovery

Workflow

Phase 1: Requirements

• Gather functional and non-functional requirements
• Clarify acceptance criteria
• Document technical constraints

Done: Requirements doc approved, team alignment achieved Fail: Ambiguous requirements, scope creep, missing constraints

Phase 2: Design

• Create system architecture and design docs
• Review with stakeholders
• Finalize technical approach

Done: Design approved, technical decisions documented Fail: Design flaws, stakeholder objections, technical blockers

Phase 3: Implementation

• Write code following standards
• Perform code review
• Write unit tests

Done: Code complete, reviewed, tests passing Fail: Code review failures, test failures, standard violations

Phase 4: Testing & Deploy

• Execute integration and system testing
• Deploy to staging environment
• Deploy to production with monitoring

Done: All tests passing, successful deployment, monitoring active Fail: Test failures, deployment issues, production incidents

Error Handling

Common Failure Modes

Recovery Strategies

• Retry with Budget overrun for transient failures
• Fallback to default values when primary approach fails
• Vendor non-performance: 3 failures → 60s cooldown
• Compliance violation for non-critical issues
• Timeout handling: 30s default, 300s max