Data · dataset · 2022
Deep Learning and Remote Sensing Dataset For Turkey's Wildfire 2021 Multispectral Sentinel-2 Satellite Imagery
Listed in Teesside University Research Data Repository
Wildfire maps are useful in understanding the growth of burned areas, change detection in vegetation, and decision-making.
Description
Wildfire is a complex exercise and is hard to capture using traditional means. The dataset consists of two folders; Train Images and Train Masks.
Each image in train images folder has a size of 128*128 pixels and consists of three multispectral bands. The image is saved using the Universal Transverse Mercator (UTM) as the coordinate system, and GeoTiff as a format file. The image has a spatial resolution of 0.00025 degrees.
Read the rest (2 more)
The values of each pixel are saved in a 16-bit unsigned integer with a range of 0 to 65,535. The dataset has 25,563 images containing the burned area's objects. In train masks folder each image is a binary annotation image that consists of two classes; burned area as the foreground and the non-burned area as the background.
These binary images are saved in 8-bit unsigned integer where the burned area is indicated by the pixel value of 1, whereas the non-burned area is indicated by 0.
Links
Where it is published
- DOI doi.org/10.17632/hgctmx9y6c.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Deep learning · Earth & Environmental Science · Photogrammetry and remote sensing · Satellite remote sensing
- Inferred from text
- Image 75%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/hgctmx9y6c.1 | 15 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:field:401304 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Remote Sensing'] |
| concepts[field].anzsrc:field:461103 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Deep Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:remote-sensing | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Remote Sensing'] |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |