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Publication details

Document type
Journal articles

Document subtype
Full paper

Title
A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images

Participants in the publication
Miguel M. Pinto (Author)
IDL
 Renata Libonati (Author)
IDL
 Ricardo M. Trigo (Author)
Dep. Engenharia Geográfica, Geofísica e Energia
 IDL
 Isabel F. Trigo (Author)
Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749–016, Lisboa, Portugal
 Carlos C. DaCamara (Author)
Dep. Engenharia Geográfica, Geofísica e Energia
 IDL
Date of Publication
2020-02

Institution
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA

Where published
ISPRS Journal of Photogrammetry and Remote Sensing

Publication Identifiers
ISSN - 0924-2716

Publisher
Elsevier BV

Volume
160

Number of pages
14
Starting page
260
Last page
274

Document Identifiers
DOI - https://doi.org/10.1016/j.isprsjprs.2019.12.014
URL - http://dx.doi.org/10.1016/j.isprsjprs.2019.12.014

Rankings
SCOPUS Q1 (2019) - 11.7 - Computers in Earth Sciences


Export

APA
Miguel M. Pinto, Renata Libonati, Ricardo M. Trigo, Isabel F. Trigo, Carlos C. DaCamara, (2020). A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 160, 260-274. ISSN 0924-2716. eISSN . http://dx.doi.org/10.1016/j.isprsjprs.2019.12.014

IEEE
Miguel M. Pinto, Renata Libonati, Ricardo M. Trigo, Isabel F. Trigo, Carlos C. DaCamara, "A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images" in ISPRS Journal of Photogrammetry and Remote Sensing, vol. 160, pp. 260-274, 2020. 10.1016/j.isprsjprs.2019.12.014

BIBTEX
@article{45811, author = {Miguel M. Pinto and Renata Libonati and Ricardo M. Trigo and Isabel F. Trigo and Carlos C. DaCamara}, title = {A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, year = 2020, pages = {260-274}, volume = 160 }