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Burnt Forest Estima...
Burnt Forest Estimation from Sentinel-2 Imagery of Australia using Unsupervised Deep Learning
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- Abid, Nosheen, 1993- (författare)
- Luleå tekniska universitet,EISLAB,Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan; School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan
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- Malik, Muhammad Imran (författare)
- Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan; School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan
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- Shahzad, Muhammad (författare)
- Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan; School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan; Technical University of Munich (TUM), Munich, Germany
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- Shafait, Faisal (författare)
- Deep Learning Lab, National Center of Artificial Intelligence, National University of Sciences and Technology, Pakistan; School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Pakistan
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- Ali, Haider (författare)
- Engineering, TU, Kaiserslautern, Germany
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- Ghaffar, Muhammad Mohsin (författare)
- Johns Hopkins University, USA
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- Weis, Christian (författare)
- Johns Hopkins University, USA
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- Wehn, Norbert (författare)
- Johns Hopkins University, USA
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- Liwicki, Marcus (författare)
- Luleå tekniska universitet,EISLAB
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(creator_code:org_t)
- IEEE, 2021
- 2021
- Engelska.
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Ingår i: Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA). - : IEEE. ; , s. 74-81
- Relaterad länk:
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https://ltu.diva-por...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Massive wildfires not only in Australia, but also worldwide are burning millions of hectares of forests and green land affecting the social, ecological, and economical situation. Widely used indices-based threshold methods like Normalized Burned Ratio (NBR) require a huge amount of data preprocessing and are specific to the data capturing source. State-of-the-art deep learning models, on the other hand, are supervised and require domain experts knowledge for labeling the data in huge quantity. These limitations make the existing models difficult to be adaptable to new variations in the data and capturing sources. In this work, we have proposed an unsupervised deep learning based architecture to map the burnt regions of forests by learning features progressively. The model considers small patches of satellite imagery and classifies them into burnt and not burnt. These small patches are concatenated into binary masks to segment out the burnt region of the forests. The proposed system is composed of two modules: 1) a state-of-the-art deep learning architecture for feature extraction and 2) a clustering algorithm for the generation of pseudo labels to train the deep learning architecture. The proposed method is capable of learning the features progressively in an unsupervised fashion from the data with pseudo labels, reducing the exhausting efforts of data labeling that requires expert knowledge. We have used the realtime data of Sentinel-2 for training the model and mapping the burnt regions. The obtained F1-Score of 0.87 demonstrates the effectiveness of the proposed model.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Unsupervised
- Deep Learning
- Australia
- Forest Fire
- Wildfire
- Sentinel-2
- Aerial Imagery
- Machine Learning
- Maskininlärning
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
- Av författaren/redakt...
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Abid, Nosheen, 1 ...
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Malik, Muhammad ...
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Shahzad, Muhamma ...
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Shafait, Faisal
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Ali, Haider
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Ghaffar, Muhamma ...
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visa fler...
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Weis, Christian
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Wehn, Norbert
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Liwicki, Marcus
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visa färre...
- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Data och informa ...
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och Datavetenskap
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Luleå tekniska universitet