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

Yudhi Prabowo, Anjar Dimara Sakti, Kuncoro Adi Pradono, Qonita Amriyah, Fadillah Halim Rasyidy, Irwan Bengkulah, Kurnia Ulfa, Danang Surya Candra, Muhammad Thufaili Imdad and Shadiq Ali
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Yudhi Prabowo: National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia
Anjar Dimara Sakti: Remote Sensing and Geographic Information Science Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia
Kuncoro Adi Pradono: National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia
Qonita Amriyah: National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia
Fadillah Halim Rasyidy: National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia
Irwan Bengkulah: National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia
Kurnia Ulfa: National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia
Danang Surya Candra: National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia
Muhammad Thufaili Imdad: National Research and Innovation Agency (BRIN), Jakarta 13220, Indonesia
Shadiq Ali: Department of Physics, University of Indonesia, Depok City 16424, Indonesia

Data, 2022, vol. 7, issue 6, 1-11

Abstract: 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.

Keywords: dataset; burned area; deep learning; U-Net; Landsat-8; remote sensing; satellite image; Indonesia (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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