NDLI: Deep Learning Dataset for Estimating Burned Areas: Case Study, Indonesia

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Deep Learning Dataset for Estimating Burned Areas: Case Study, Indonesia

Content Provider	MDPI
Author	Prabowo, Yudhi Sakti, Anjar Dimara Pradono, Kuncoro Adi Amriyah, Qonita Rasyidy, Fadillah Halim Bengkulah, Irwan Ulfa, Kurnia Candra, Danang Surya Imdad, Muhammad Thufaili Ali, Shadiq
Copyright Year	2022
Description	Wildland fire is one of the most causes of deforestation, and it has an important impact on atmospheric emissions, notably $CO_{2}$. 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 × 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.
Starting Page	78
e-ISSN	23065729
DOI	10.3390/data7060078
Journal	Data
Issue Number	6
Volume Number	7
Language	English
Publisher	MDPI
Publisher Date	2022-06-09
Access Restriction	Open
Subject Keyword	Data Remote Sensing Dataset Burned Area Deep Learning U-net Landsat-8 Satellite Image Indonesia
Content Type	Text
Resource Type	Article

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