文章基本信息

标题：Deep Learning Dataset for Estimating Burned Areas: Case Study, Indonesia
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作者：Yudhi Prabowo ; Anjar Dimara Sakti ; Kuncoro Adi Pradono 等
期刊名称：Data
印刷版ISSN：2306-5729
出版年度：2022
卷号：7
期号：6
页码：1-11
DOI：10.3390/data7060078
语种：English
出版社：MDPI Publishing
摘要：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 formapping 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 X 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.Dataset: https://data.mendeley.com/datasets/fs7mtkg2wk/4Dataset License: CC-BY.
关键词：dataset;burned area;deep learning;U-Net;Landsat-8;remote sensing;satellite image;Indonesia