A Diffusion Model-Generated Forest Fire Dataset
This dataset consists of 2,204 AI-generated images, created using a Stable Diffusion model. 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 f...
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| creator | Imdadul Alam, Gazi Mohammad Tasnia, Naima Biswas, Tapu Hossen, Md. Jakir Tanim, Sharia Arfin Ullah Miah, Md Saef |
| description | This dataset consists of 2,204 AI-generated images, created using a Stable Diffusion model. 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.
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. |
| doi_str_mv | 10.17632/nwzcm9ckrt.1 |
| format | Dataset |
| fullrecord | <record><control><sourceid>datacite_PQ8</sourceid><recordid>TN_cdi_datacite_primary_10_17632_nwzcm9ckrt_1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_17632_nwzcm9ckrt_1</sourcerecordid><originalsourceid>FETCH-datacite_primary_10_17632_nwzcm9ckrt_13</originalsourceid><addsrcrecordid>eNpjYBA1NNAzNDczNtLPK69KzrVMzi4q0TPkZNB3VHDJTEsrLc7Mz1PwzU9JzdF1T81LLUosSU1RcMsvSi0uUXDLLEpVcEksSSxOLeFhYE1LzClO5YXS3Ay6bq4hzh66KUD55MyS1PiCoszcxKLKeEODeLCF8QgL4w2NSVUPAOtkO1k</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>dataset</recordtype></control><display><type>dataset</type><title>A Diffusion Model-Generated Forest Fire Dataset</title><source>DataCite</source><creator>Imdadul Alam, Gazi Mohammad ; Tasnia, Naima ; Biswas, Tapu ; Hossen, Md. Jakir ; Tanim, Sharia Arfin ; Ullah Miah, Md Saef</creator><creatorcontrib>Imdadul Alam, Gazi Mohammad ; Tasnia, Naima ; Biswas, Tapu ; Hossen, Md. Jakir ; Tanim, Sharia Arfin ; Ullah Miah, Md Saef</creatorcontrib><description>This dataset consists of 2,204 AI-generated images, created using a Stable Diffusion model. 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.
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.</description><identifier>DOI: 10.17632/nwzcm9ckrt.1</identifier><language>eng</language><publisher>Mendeley Data</publisher><subject>Image Classification ; Object Detection</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0009-0001-7636-990X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>776,1888</link.rule.ids><linktorsrc>$$Uhttps://commons.datacite.org/doi.org/10.17632/nwzcm9ckrt.1$$EView_record_in_DataCite.org$$FView_record_in_$$GDataCite.org$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Imdadul Alam, Gazi Mohammad</creatorcontrib><creatorcontrib>Tasnia, Naima</creatorcontrib><creatorcontrib>Biswas, Tapu</creatorcontrib><creatorcontrib>Hossen, Md. Jakir</creatorcontrib><creatorcontrib>Tanim, Sharia Arfin</creatorcontrib><creatorcontrib>Ullah Miah, Md Saef</creatorcontrib><title>A Diffusion Model-Generated Forest Fire Dataset</title><description>This dataset consists of 2,204 AI-generated images, created using a Stable Diffusion model. 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.
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.</description><subject>Image Classification</subject><subject>Object Detection</subject><fulltext>true</fulltext><rsrctype>dataset</rsrctype><creationdate>2024</creationdate><recordtype>dataset</recordtype><sourceid>PQ8</sourceid><recordid>eNpjYBA1NNAzNDczNtLPK69KzrVMzi4q0TPkZNB3VHDJTEsrLc7Mz1PwzU9JzdF1T81LLUosSU1RcMsvSi0uUXDLLEpVcEksSSxOLeFhYE1LzClO5YXS3Ay6bq4hzh66KUD55MyS1PiCoszcxKLKeEODeLCF8QgL4w2NSVUPAOtkO1k</recordid><startdate>20241204</startdate><enddate>20241204</enddate><creator>Imdadul Alam, Gazi Mohammad</creator><creator>Tasnia, Naima</creator><creator>Biswas, Tapu</creator><creator>Hossen, Md. Jakir</creator><creator>Tanim, Sharia Arfin</creator><creator>Ullah Miah, Md Saef</creator><general>Mendeley Data</general><scope>DYCCY</scope><scope>PQ8</scope><orcidid>https://orcid.org/0009-0001-7636-990X</orcidid></search><sort><creationdate>20241204</creationdate><title>A Diffusion Model-Generated Forest Fire Dataset</title><author>Imdadul Alam, Gazi Mohammad ; Tasnia, Naima ; Biswas, Tapu ; Hossen, Md. Jakir ; Tanim, Sharia Arfin ; Ullah Miah, Md Saef</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-datacite_primary_10_17632_nwzcm9ckrt_13</frbrgroupid><rsrctype>datasets</rsrctype><prefilter>datasets</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Image Classification</topic><topic>Object Detection</topic><toplevel>online_resources</toplevel><creatorcontrib>Imdadul Alam, Gazi Mohammad</creatorcontrib><creatorcontrib>Tasnia, Naima</creatorcontrib><creatorcontrib>Biswas, Tapu</creatorcontrib><creatorcontrib>Hossen, Md. Jakir</creatorcontrib><creatorcontrib>Tanim, Sharia Arfin</creatorcontrib><creatorcontrib>Ullah Miah, Md Saef</creatorcontrib><collection>DataCite (Open Access)</collection><collection>DataCite</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Imdadul Alam, Gazi Mohammad</au><au>Tasnia, Naima</au><au>Biswas, Tapu</au><au>Hossen, Md. Jakir</au><au>Tanim, Sharia Arfin</au><au>Ullah Miah, Md Saef</au><format>book</format><genre>unknown</genre><ristype>DATA</ristype><title>A Diffusion Model-Generated Forest Fire Dataset</title><date>2024-12-04</date><risdate>2024</risdate><abstract>This dataset consists of 2,204 AI-generated images, created using a Stable Diffusion model. 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.
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.</abstract><pub>Mendeley Data</pub><doi>10.17632/nwzcm9ckrt.1</doi><orcidid>https://orcid.org/0009-0001-7636-990X</orcidid><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.17632/nwzcm9ckrt.1 |
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| language | eng |
| recordid | cdi_datacite_primary_10_17632_nwzcm9ckrt_1 |
| source | DataCite |
| subjects | Image Classification Object Detection |
| title | A Diffusion Model-Generated Forest Fire Dataset |
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