Dataset for "Remote Sensing-Based Approaches for Automatic Vineyard Area Identification: A Systematic Review"
Description
This dataset provides the full data-extraction table supporting the systematic review “Remote Sensing-Based Approaches for Automatic Vineyard Area Identification: A Systematic Review”. It contains study-level extracted information for the empirical studies included in the synthesis (n=80), organised by application-scale groups: (A) vineyard parcel/field, (B) vine rows, (C) individual vines (plant-level), and (R) regional assessments. In addition to bibliographic metadata and study context, the table records mapping applications/outputs and task flags (Detection, Delineation, Segmentation, Classification, Estimation), remote-sensing system and sensor/platform, spectral modality and spatial resolution, input information types, method category and specific methods, and the best-reported evaluation metrics (as reported by the original authors). Missing or unreported fields are preserved as not reported (no assumptions or imputation). Publication-venue metadata are included for descriptive analysis: Publisher, Journal/venue, Type, and SCImago Journal Rank (SJR). Journal articles are recorded by SJR quartile for the publication year (Q1–Q3 when available). For proceedings (conference papers and book chapters), the venue SJR score is stored as a numeric value and categorised into three levels (Top/Medium/Low) using the thresholds defined in the associated manuscript. Risk of bias/applicability is reported per domain (D1 coverage; D2 sensor & pre-processing; D3 ground-truth; D4 validation; D5 portability) plus an overall rating, following the decision rule described in the manuscript. A reproducibility tag summarises whether data/code availability enables independent reuse, as defined in the accompanying data dictionary. This dataset is intended to increase transparency and enable verification, re-analysis, and evidence mapping of the vineyard identification literature. Please cite the associated article and this dataset DOI when reusing the data.
Files
Institutions
- Universidade Nova de Lisboa Instituto Superior de Estatistica e Gestao de InformacaoLisboa, Lisboa