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Early Detection Of Wildfires With Goes-R Time-Series And Deep Gru Network
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0002-4230-2467
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-1369-3216
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0001-9692-8636
2021 (English)In: International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 3765-3768Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, wildfires have become major devastating hazards that affect both public safety and the environment. Thus, agile detection of the wildfires is desirable to suppress wildfires in the early stage. Owing to the high temporal resolution, GOES-R satellites offer capabilities to obtain images every 15 minutes enabling a near real-time monitoring of wildfires. In this research, a time-series-based deep learning framework, composed of Gated Recurrent Units(GRU), is proposed to capture the emerging of the wildfire at early stage. By feeding the embedding of the coarse satellite imagery to Deep GRU network, the active fires are segmented out from the remote sensing imagery. The preliminary results show that proposed network can detect the wildfires earlier than the state-of-the-art fire product for 2020 wildfires in California and British Columbia, at the same time provide sufficiently high accuracy on the burned areas.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2021. p. 3765-3768
Keywords [en]
Deep Learning, GOES-R, GRU, Wildfire monitoring, Remote sensing, Satellite imagery, Time series, Embeddings, Gated recurrent unit, High temporal resolution, Learning frameworks, Near-real-time monitoring, Public safety, Times series, Fires
National Category
Medical Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-316195DOI: 10.1109/IGARSS47720.2021.9554225Scopus ID: 2-s2.0-85129790445OAI: oai:DiVA.org:kth-316195DiVA, id: diva2:1699039
Conference
2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, 12 July 2021 through 16 July 2021
Note

QC 20220926

Available from: 2022-09-26 Created: 2022-09-26 Last updated: 2022-09-26Bibliographically approved

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Zhao, YuBan, YifangNascetti, Andrea

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  • apa
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