| Empirical |
Dataset Scarcity |
Requires billion-scale datasets vs. current RS datasets (e.g., Million-AID: 1M images only) |
🔴 High Priority |
Vision-Language Models |
Limits model generalization across geographies |
| Synthetic Data Reliance |
Diffusion models require quality text prompts (e.g., unstable for rare land cover classes) |
🟡 Medium Priority |
Vision-Language Models |
Introduces artifacts in training data |
| Theoretical |
Domain Knowledge Integration |
No physics-based constraints for SAR imagery (e.g., speckle noise modeling in VLMs) |
🟡 Medium Priority |
Vision-Language Models |
Reduces model interpretability |
| Ethical Frameworks |
No discussion of biases in prompt design (e.g., CLIPs Western-centric object recognition) |
🔴 High Priority |
Prompt Engineering |
Risks deployment in sensitive applications |
| Methodological |
Spatiotemporal Reasoning |
Poor handling of Landsat time-series (8-day revisit cycles not leveraged) |
🔴 High Priority |
Vision-Language Models |
Limits climate change analysis |
| Prompt Generalization |
CoOP overfits to textual patterns (e.g., fails on non-English region descriptions) |
🟡 Medium Priority |
Prompt Engineering |
Reduces cross-cultural applicability |
| Computational |
Resource Demands |
GPT-3s 175B parameters vs. edge devices (e.g., impossible for drone-based deployment) |
🔴 High Priority |
Both Papers |
Hinders real-time disaster response |
| Edge Deployment |
SAM requires 3.2GB RAM vs. field robotics constraints (typically less than 1GB) |
🟡 Medium Priority |
Prompt Engineering |
Limits IoT integration |
| Evaluation |
Cross-Domain Validation |
Limited testing on medical images (e.g., SAM's failure on low-contrast tumor boundaries) |
🟡 Medium Priority |
Prompt Engineering |
Obscures healthcare applicability |
| Hardware-Aware Benchmarking |
No metrics for memory constraints (e.g., FPS not reported for agricultural robots) |
🔴 High Priority |
Both Papers |
Misguides practical system design |