Data · dataset · 2026
Multitemporal Sentinel-2 Patches (64×64) for Flood Mapping - Training and Validation Set, Jacuí River Basin, Rio Grande do Sul, Brazil
Listed in ScienceDB
Flood Mapping Dataset – Training and Validation (Jacuí River Basin, Rio Grande do Sul, Brazil)OverviewThis dataset contains 1,598 patches of shape (64, 64) of Sentinel-2 satellite imagery and corresponding flood masks, extracted from the Jacuí River Basin, Brazil.
Description
The acquisitions used are: three pre-disaster images (December 17, 2023; January 10, 2024; February 21, 2024) and one post-disaster image (May 6, 2024).It is designed for training and validation of deep learning models for flood semantic segmentation. 4-time setup: 3 pre-flood + 1 post-flood acquisitionIn this dataset, the post-flood acquisition and flood mask correspond to the same reference date (May 6, 2024).Data Structure images/: 1,598 patches, shape (64, 64), each with 12 channels (4 time steps × 3 bands: Green, Red, NIR) masks/: 1,598 corresponding flood masks, shape (64, 64), values: 0 = non-flooded, 1 = floodedAll filenames are synchronized between images and masks.Patch counts:| Total | 1,598 |Spatial Reference CRS: EPSG:32722 (WGS 84 / UTM zone 22S) Resolution: 10 m/pixel Location: Jacuí River Basin, Rio Grande do Sul, Brazil
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.41571 ↗
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
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Genome sequencing 65% · Image 75% · Satellite remote sensing 65%
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.41571 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| 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:genome-sequencing | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[modality].local:modality:image | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| 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 |