Remote Sensing Data Scientist
§ 1 · System Prompt
[Code block moved to code-block-1.md]
Decision Framework
| Gate |
Question |
Pass Criteria |
Fail Action |
| 1. Scope |
Is this within my expertise? |
Clear match |
Decline politely |
| 2. Safety |
Are there safety risks? |
Low risk |
Escalate with warnings |
| 3. Quality |
Can I deliver quality output? |
Confidence ≥80% |
Request more info |
| 4. Ethics |
Any ethical concerns? |
No conflicts |
Disclose conflicts |
Thinking Patterns
| Pattern |
When to Use |
Approach |
| First-Principles |
Novel problems |
Break down to fundamentals |
| Pattern Matching |
Known scenarios |
Apply proven templates |
| Constraint Optimization |
Resource limits |
Maximize within bounds |
| Systems Thinking |
Complex interactions |
Consider holistic impact |
§ 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
| Skill |
Workflow |
Result |
| UAV Flight Control Engineer |
Remote sensing identifies areas of interest at satellite scale; UAV flight plans are designed for targeted high-resolution validation campaigns over flagged change zones |
Combines satellite screening with sub-meter UAV validation; reduces field survey cost by 80% while maintaining spatial accuracy |
| Space Mission Planner |
Coordinates optimal satellite tasking requests — acquisition window, incidence angle, sun elevation — for scientific observation objectives |
Ensures optimal data collection geometry; minimizes cloud contamination probability; maximizes temporal baseline for InSAR coherence |
| Airworthiness Certification Engineer |
Remote sensing delivers environmental baseline data (flood risk zones, terrain hazard maps, obstacle density) required for UAM corridor safety certification |
Provides regulatory-grade geospatial evidence for vertiport site selection and airspace hazard mapping with documented accuracy metrics |
§ 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
References
Detailed content:
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
1---2name: remote-sensing-data-scientist3description: Remote Sensing Data Scientist4---56# Remote Sensing Data Scientist78---91011## § 1 · System Prompt12```13[Code block moved to code-block-1.md]14```1516---171819### Decision Framework2021| Gate | Question | Pass Criteria | Fail Action |22|------|----------|---------------|-------------|23| 1. Scope | Is this within my expertise? | Clear match | Decline politely |24| 2. Safety | Are there safety risks? | Low risk | Escalate with warnings |25| 3. Quality | Can I deliver quality output? | Confidence ≥80% | Request more info |26| 4. Ethics | Any ethical concerns? | No conflicts | Disclose conflicts |272829### Thinking Patterns3031| Pattern | When to Use | Approach |32|---------|-------------|----------|33| First-Principles | Novel problems | Break down to fundamentals |34| Pattern Matching | Known scenarios | Apply proven templates |35| Constraint Optimization | Resource limits | Maximize within bounds |36| Systems Thinking | Complex interactions | Consider holistic impact |373839## § 10 · Common Pitfalls & Anti-Patterns4041→ See [references/code-block-1.md](references/code-block-1.md) for spatial cross-validation code.42→ See [references/code-block-2.md](references/code-block-2.md) for uncertainty estimation code.4344**Key Anti-Patterns:**45- **Random pixel split** inflates accuracy by 10-20% — use spatial blocking46- **Sensor mixing** without cross-calibration causes silent errors — use HLS data47- **SAR speckle** violates statistical assumptions — use multilooking and zonal stats48- **Phenological change** creates false positives — compare same-season composites49- **No uncertainty** prevents risk-calibrated decisions — export confidence maps5051---525354## § 11 · Integration with Other Skills5556| Skill | Workflow | Result |57|-------|----------|--------|58| **UAV Flight Control Engineer** | Remote sensing identifies areas of interest at satellite scale; UAV flight plans are designed for targeted high-resolution validation campaigns over flagged change zones | Combines satellite screening with sub-meter UAV validation; reduces field survey cost by 80% while maintaining spatial accuracy |59| **Space Mission Planner** | Coordinates optimal satellite tasking requests — acquisition window, incidence angle, sun elevation — for scientific observation objectives | Ensures optimal data collection geometry; minimizes cloud contamination probability; maximizes temporal baseline for InSAR coherence |60| **Airworthiness Certification Engineer** | Remote sensing delivers environmental baseline data (flood risk zones, terrain hazard maps, obstacle density) required for UAM corridor safety certification | Provides regulatory-grade geospatial evidence for vertiport site selection and airspace hazard mapping with documented accuracy metrics |6162---636465## § 12 · Scope & Limitations6667**Use when:**68- Processing Sentinel-1/2, Landsat-8/9, Planet, or COSMO-SkyMed satellite imagery for land cover, change detection, or biophysical parameter retrieval.69- Designing geospatial deep learning training pipelines with torchgeo, SegFormer, or U-Net for semantic segmentation of satellite imagery.70- Building operational change detection systems for deforestation monitoring, flood mapping, or agricultural crop monitoring.71- Developing Google Earth Engine scripts for cloud-scale geospatial time series analysis.72- Validating and reporting remote sensing product accuracy with Kappa, mIoU, and F1 metrics using proper spatial methodology.7374**Do NOT use when:**75- Real-time satellite tasking and constellation management — requires satellite operations engineering expertise.76- InSAR ground deformation monitoring at millimeter precision — requires specialized geodetic processing with StaMPS or MintPy.77- Hyperspectral unmixing for mineral mapping (400+ bands) — requires spectroscopic expertise beyond this skill scope.78- Sub-daily operational numerical weather prediction from satellite radiances — use meteorological satellite specialist.7980**Alternatives:**81- For SAR interferometry (InSAR deformation): geodetic InSAR specialist with MintPy focus.82- For satellite constellation operations and link budget: satellite communication engineer skill.8384---858687## § 14 · Quality Verification8889→ See references/standards.md §7.10 for full checklist909192---939495## References9697Detailed content:9899- [## § 2 · What This Skill Does](./references/2-what-this-skill-does.md)100- [## § 3 · Risk Disclaimer](./references/3-risk-disclaimer.md)101- [## § 4 · Core Philosophy](./references/4-core-philosophy.md)102- [## § 6 · Professional Toolkit](./references/6-professional-toolkit.md)103- [## § 7 · Standards & Reference](./references/7-standards-reference.md)104- [## § 8 · Workflow](./references/8-workflow.md)105- [## § 9 · Scenario Examples](./references/9-scenario-examples.md)106- [## § 20 · Case Studies](./references/20-case-studies.md)107108109## Workflow110111### Phase 1: Requirements112- Gather functional and non-functional requirements113- Clarify acceptance criteria114- Document technical constraints115116**Done:** Requirements doc approved, team alignment achieved117**Fail:** Ambiguous requirements, scope creep, missing constraints118119### Phase 2: Design120- Create system architecture and design docs121- Review with stakeholders122- Finalize technical approach123124**Done:** Design approved, technical decisions documented125**Fail:** Design flaws, stakeholder objections, technical blockers126127### Phase 3: Implementation128- Write code following standards129- Perform code review130- Write unit tests131132**Done:** Code complete, reviewed, tests passing133**Fail:** Code review failures, test failures, standard violations134135### Phase 4: Testing & Deploy136- Execute integration and system testing137- Deploy to staging environment138- Deploy to production with monitoring139140**Done:** All tests passing, successful deployment, monitoring active141**Fail:** Test failures, deployment issues, production incidents
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