AI in the fields: Challenges and insights in detecting agrarian footprints from Satellite and LiDAR data

Deep learning plays a key role to detects patterns in large datasets, Real-world AI validation needs accurate ground-truthing, Data quality and access pose technical challenges, Ethical concerns arise in remote sensing and heritage data use

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Publication Details

Journal
ArchéoSciences
Published
2025-01-01
DOI
https://doi.org/10.4000/14nua
Primary Topic
Food Supply Chain Traceability
Type
article
Field-Weighted Citation Impact
0.00
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article

AI in the fields: Challenges and insights in detecting agrarian footprints from Satellite and LiDAR data

Melda Küçükdemirci
ArchéoSciences
Food Supply Chain Traceability
article

AI in the fields: Challenges and insights in detecting agrarian footprints from Satellite and LiDAR data

Melda Küçükdemirci
article en

Abstract

Deep learning plays a key role to detects patterns in large datasets, Real-world AI validation needs accurate ground-truthing, Data quality and access pose technical challenges, Ethical concerns arise in remote sensing and heritage data use

ArchéoSciencesVol. 49-1
Istanbul University-Cerrahpaşa (TR), Istanbul University (TR)
Openalex Percentile: Top 71%
Food Supply Chain Traceability
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AI in the fields: Challenges and insights in detecting agrarian footprints from Satellite and LiDAR data — Melda Küçükdemirci · ArchéoSciences (2025) | TGRS Research Map | TGRS