RT Journal Article
T1 Scalable approach for high-resolution land cover: a case study in the Mediterranean Basin.
A1 Burgueño Romero, Antonio Manuel
A1 Aldana Martín, José Francisco
A1 Vázquez-Pendón, María
A1 Barba-González, Cristóbal
A1 Jiménez-Gómez, Yaiza
A1 García Millán, Virginia
A1 Navas-Delgado, Ismael
K1 Aprendizaje automático (Inteligencia artificial)
K1 Árboles de decisión
K1 Suelo - Uso - Proceso de datos
AB The production of land cover maps is an everyday use of image classification applications on remote sensing. However, managing Earth observation satellite data for a large region of interest is challenging in the task of creating land cover maps. Since satellite imagery is getting more precise and extensive, Big Data techniques are becoming essential to handle the rising quantity of data. Furthermore, given the complexity of managing and analysing the data, defining a methodology that reduces the complexity of the process into different smaller steps is vital to data processing. This paper presents a Big Data methodology for creating land cover maps employing artificial intelligence algorithms. Machine Learning algorithms are contemplated for remote sensing and geodata classification, supported by explainable artificial intelligence. Furthermore, the process considers aspects related to downloading data from different satellites, Copernicus and ASTER, executing the pre-processing and processing of the data in a distributed environment, and depicting the visualisation of the result. The methodology is validated in a test case for er map of the Mediterranean Basin.
PB Springer Nature
YR 2023
FD 2023-06-02
LK https://hdl.handle.net/10630/30638
UL https://hdl.handle.net/10630/30638
LA eng
NO Burgueño, A. M., Aldana-Martín, J. F., Vázquez-Pendón, M., Barba-González, C., Jiménez Gómez, Y., García Millán, V., & Navas-Delgado, I. (2023). Scalable approach for high-resolution land cover: a case study in the Mediterranean Basin. Journal of Big Data, 10(1), 1-22.https://doi.org/10.1186/s40537-023-00770-z
NO PID2020-112540RB-C41, MCIN/AEI/10.13039/501100011033,PRE2021-098594, LIFEWATCH-2019-11-UMA-4
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026