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L D Shen

Biographic Data

ID5159101
NAMEL D Shen
GIVEN NAMESL D
FAMILY NAMEShen
SIGNATURESHEN L D
AFFILIATIONSSouthwest Jiaotong University
VERIFIEDNo
TOTAL WORKS3
TOTAL CITATIONS0
AUTHOR COUNT3
EDITOR COUNT0
FIRST PUBLICATION YEAR2022
LATEST PUBLICATION YEAR2026
H-INDEX0
  • Seeing life in motion

    Open Access•Mei Huang, Xinlin Yang et al.•ARTICLE•Cognition•2026

  • Referencing, Internalizing, or Activating Knowledge

    Shuaiqing Wang, Li Shen et al.•ARTICLE•Annals of the American…•2026•References: 28

    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

    Open Access•Yage Liu, Huidong Li et al.•ARTICLE•Environmental Research Letters•2022

    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

    Open Access•Yage Liu, Huidong Li et al.•ARTICLE•Environmental Research Letters•2022

    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

    Open Access•Mei Huang, Xinlin Yang et al.•ARTICLE•Cognition•2026

  • Referencing, Internalizing, or Activating Knowledge

    Shuaiqing Wang, Li Shen et al.•ARTICLE•Annals of the American…•2026•References: 28

    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)

Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae