@InProceedings{MuralikrishnaVieiSantAlme:2020:ToSoIr,
author = "Muralikrishna, Amita and Vieira, Luis Eduardo Antunes and Santos,
Rafael Duarte Coelho dos and Almeida, Adriano Pereira",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Total solar irradiance forecasting with keras recurrent neural
networks",
booktitle = "Proceedings...",
year = "2020",
editor = "Gervasi, O. and Murgante, B. and Misra, S. and Garau, C. and
Blecic, I. and Taniar, D. and Apduhan, B. O. and Rocha, A. M. A.
C. and Tarantino, E. and Torre, C. M. and Karaca, Y.",
pages = "255--269",
organization = "International Conference on Computational Science and Its
Applications,20.",
publisher = "Springer",
note = "Lecture Notes in Computer Science, v.12253",
keywords = "Remote sensing · Burned forest classification keyword · Forest
fire survey and monitoring.",
abstract = "Monitoring the large number of active fires and their consequences
in such an extensive area such as the Brazilian territory is an
important task. Machine Learning techniques are a promising
approach to contribute to this area, but the challenge is the
building of rich example datasets, whose previous examples are
unavailable in many areas. Our aim in this article is to move
towards the development of an approach to detect burned areas in
regions for which there is no previously validated samples. We
deal with that by presenting some experiments to classify burned
areas through Machine Learning techniques that combine remote
sensing data from nearby areas and it can distinguish between
burned and non burned polygons with good results.",
conference-location = "Cagliari, Italy",
conference-year = "01-04 July",
doi = "10.1007/978-3-030-58814-4_18",
url = "http://dx.doi.org/10.1007/978-3-030-58814-4_18",
isbn = "978-303058813-7",
issn = "03029743",
language = "en",
targetfile = "amita_total.pdf",
urlaccessdate = "29 mar. 2024"
}