{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T12:33:29Z","timestamp":1784637209501,"version":"3.55.0"},"reference-count":17,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research and Innovation Agency (BRIN)"},{"name":"Capacity Building Research Program for ITB Young Scientists by the Institute of Research and Community Service, Institut Teknologi Bandung"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Wildland fire is one of the most causes of deforestation, and it has an important impact on atmospheric emissions, notably CO2. It occurs almost every year in Indonesia, especially during the dry season. Therefore, it is necessary to identify the burned areas from remote sensing images to establish the zoning map of areas prone to wildland fires. Many methods have been developed for mapping burned areas from low-resolution to medium-resolution satellite images. One of the popular approaches for mapping tasks is a deep learning approach using U-Net architecture. However, it needs a large amount of representative training data to develop the model. In this paper, we present a new dataset of burned areas in Indonesia for training or evaluating the U-Net model. We delineate burned areas manually by visual interpretation on Landsat-8 satellite images. The dataset is collected from some regions in Indonesia, and it consists of 227 images with a size of 512 \u00d7 512 pixels. It contains one or more burned scars or only the background and its labeled masks. The dataset can be used to train and evaluate the deep learning model for image detection, segmentation, and classification tasks related to burned area mapping.<\/jats:p>","DOI":"10.3390\/data7060078","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:22:39Z","timestamp":1654820559000},"page":"78","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Deep Learning Dataset for Estimating Burned Areas: Case Study, Indonesia"],"prefix":"10.3390","volume":"7","author":[{"given":"Yudhi","family":"Prabowo","sequence":"first","affiliation":[{"name":"National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anjar Dimara","family":"Sakti","sequence":"additional","affiliation":[{"name":"Remote Sensing and Geographic Information Science Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuncoro Adi","family":"Pradono","sequence":"additional","affiliation":[{"name":"National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qonita","family":"Amriyah","sequence":"additional","affiliation":[{"name":"National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fadillah Halim","family":"Rasyidy","sequence":"additional","affiliation":[{"name":"National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irwan","family":"Bengkulah","sequence":"additional","affiliation":[{"name":"National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kurnia","family":"Ulfa","sequence":"additional","affiliation":[{"name":"National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danang Surya","family":"Candra","sequence":"additional","affiliation":[{"name":"National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Thufaili","family":"Imdad","sequence":"additional","affiliation":[{"name":"National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shadiq","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Indonesia, Depok City 16424, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"key":"ref_1","unstructured":"Ministry of Environment and Forestry Republic of Indonesia (KLHK) (2021, October 01). Siaran Pers: Hutan dan Deforestasi Indonesia Tahun 2019, Available online: http:\/\/ppid.menlhk.go.id\/siaran_pers\/browse\/2435."},{"key":"ref_2","unstructured":"FAO, and UNEP (2020). The State of the World\u2019s Forests (SOFO), FAO and UNEP."},{"key":"ref_3","unstructured":"Nurofiq, H.F., Prihatno, K.B., Margono, B.A., Sudijanto, A., Primiantoro, E.T., Saputro, T., Parisy, Y., Nugroho, D., Ramdhany, D., and Kumar, K. (2020). The State of Indonesia\u2019s Forest 2020."},{"key":"ref_4","unstructured":"Ministry of Environment and Forestry Republic of Indonesia (KLHK) (2021, October 04). SiPongi Karhutla Monitoring Sistem, Available online: http:\/\/sipongi.menlhk.go.id\/hotspot\/luas_kebakaran."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"131","DOI":"10.5194\/isprs-archives-XLIII-B5-2020-131-2020","article-title":"Burnt area detection using medium resolution sentinel 2 and landsat 8 satellites","volume":"43","author":"Ongeri","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_6","unstructured":"Birendra, B., and Rajesh, B.T. (2021). Forest Fire Detection and Monitoring. Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region, Springer Nature."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1071\/WF20168","article-title":"Bureaucratic inertia in dealing with annual forest fires in Indonesia","volume":"30","author":"Purnomo","year":"2021","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.12.011","article-title":"Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa","volume":"222","author":"Roteta","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sari, I.L., Weston, C.J., Newnham, G.J., and Volkova, L. (2021). 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