L D Shen
Biographic Data
| ID | 5159101 |
|---|---|
| NAME | L D Shen |
| GIVEN NAMES | L D |
| FAMILY NAME | Shen |
| SIGNATURE | SHEN L D |
| AFFILIATIONS | Southwest Jiaotong University |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Seeing life in motion
Referencing, Internalizing, or Activating Knowledge
Large language models (LLMs) offer transformative potential for automated cartography, yet their effectiveness depends on their integration with specialized cartographic knowledge. Although strategies like retrieval-augmented generation (RAG), fine-tuning, and prompt engineering are the prevailing approaches for knowledge augmentation, their comparative effectiveness in enhancing LLM performance in cartography remains underexplored. Based on a sp…
Estimating the impact of shelterbelt structure on corn yield at a large scale using Google Earth and Sentinel 2 data
A shelterbelt is an important measure to protect farmland and increase crop yield. However, how a shelterbelt structure affects crop yield is still unclear due to the difficulties accessing sufficient data from traditional field observations. To address this problem, we developed an innovative framework to estimate the shelterbelt structure and crop yield profile at a regional scale based on Google Earth and Sentinel-2 data. Using this method, we…
No prominent works on this page.
Estimating the impact of shelterbelt structure on corn yield at a large scale using Google Earth and Sentinel 2 data
A shelterbelt is an important measure to protect farmland and increase crop yield. However, how a shelterbelt structure affects crop yield is still unclear due to the difficulties accessing sufficient data from traditional field observations. To address this problem, we developed an innovative framework to estimate the shelterbelt structure and crop yield profile at a regional scale based on Google Earth and Sentinel-2 data. Using this method, we…
Seeing life in motion
Referencing, Internalizing, or Activating Knowledge
Large language models (LLMs) offer transformative potential for automated cartography, yet their effectiveness depends on their integration with specialized cartographic knowledge. Although strategies like retrieval-augmented generation (RAG), fine-tuning, and prompt engineering are the prevailing approaches for knowledge augmentation, their comparative effectiveness in enhancing LLM performance in cartography remains underexplored. Based on a sp…
Action Observation and Synchronization (1 work) · Adaptation (eye) (1 work) · Agroforestry (1 work) · Agronomy (1 work) · Animacy (1 work) · Biological motion (1 work) · Biology (1 work) · Categorization (1 work) · Child and Animal Learning Development (1 work) · Context (archaeology (1 work)