Data · dataset · 2025
A Diffusion Model-Generated Forest Fire Dataset
Listed in Teesside University Research Data Repository
This dataset consists of 2,204 AI-generated images, created using a Stable Diffusion model.
Description
The images are organized into two distinct folders: Fire: Contains 1,102 images depicting synthetic representations of forest fires. Non-Fire: Contains 1,102 images showing various forest scenes without any fire activity.
Each image has a resolution of 150x150 pixels, optimized for use in machine learning tasks such as classification, detection, and environmental monitoring. Purpose The dataset is intended to aid research and development in areas such as: Forest fire detection and prevention using AI. Benchmarking deep learning models for image classification tasks.
Read the rest (3 more)
Studying the environmental impact of forest fires. Key Features Generated with Stable Diffusion, ensuring high-quality and varied synthetic visuals. Balanced dataset structure (equal representation of fire and non-fire images).
Small image size (150x150), suitable for lightweight model training and testing. When using this dataset, please cite the following paper: Alam, G. M. I., Tasnia, N., Biswas, T., Hossen, M. J., Tanim, S. A., & Miah, M. S. U. (2025). Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI Techniques.
IEEE Access.
Links
Where it is published
- DOI doi.org/10.17632/nwzcm9ckrt.2 ↗
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
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Ecological applications 73% · Image 75%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/nwzcm9ckrt.2 | 12 d 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:group:4102 | enrichment · researchdata tees ac uk | taxonomy-embedding@1.0.0 | title+keywords+description (73%) |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | 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%) |
| 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 |