Data · dataset · 2026
A Multi-Temporal UAV Multispectral Dataset for Land Cover Classification in a Heterogeneous Agricultural Landscape
Listed in ScienceDB
This dataset presents a multi-temporal unmanned aerial vehicle (UAV) remote sensing dataset covering a typical farmland–wetland composite landscape in Jurong City, Zhenjiang, Jiangsu Province, China.
Description
The dataset comprises seven monthly observation periods from June to December 2025, each including RGB orthophotos, four-band multispectral imagery (green, red, red edge, near-infrared) and a digital surface model (DSM).
Field surveys and remote sensing interpretation, conducted in parallel with the UAV aerial photography, provided ground-truth labels for 17 land cover classes, covering rice, dryland crops (maize, groundnuts, sweet potatoes), melons and vegetables (watermelons, loofahs, small vegetable plots), woodland, scrubland, nurseries, natural grassland, cultivated grassland, reed wetlands, ponds and reservoirs, residential areas, roads and greenhouse facilities.
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The seven-period time-series data provide comprehensive and in-depth coverage of the phenological cycle during the growing season, supporting the development and validation of phenology-aware classification algorithms. It is suitable for research into deep learning-based semantic segmentation, time-series classification and the fusion of spectral and structural data.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.ecodb.00334 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Agriculture, land and farm management 74% · Satellite remote sensing 65%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.ecodb.00334 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:3002 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (74%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:remote-sensing | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |