dc.creator | García Gutiérrez, Jorge | es |
dc.creator | González Ferreiro, Eduardo | es |
dc.creator | Mateos García, Daniel | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2022-04-25T10:40:26Z | |
dc.date.available | 2022-04-25T10:40:26Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | García Gutiérrez, J., González Ferreiro, E., Mateos García, D. y Riquelme Santos, J.C. (2016). A Preliminary Study of the Suitability of Deep Learning to Improve LiDAR-Derived Biomass Estimation. En HAIS 2016 : 11th International Conference on Hybrid Artificial Intelligence Systems (588-596), Sevilla, España: Springer. | |
dc.identifier.isbn | 978-3-319-32033-5 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/132537 | |
dc.description.abstract | Light Detection and Ranging (LiDAR) is a remote sensor
able to extract three-dimensional information about forest structure. Bio physical models have taken advantage of the use of LiDAR-derived infor mation to improve their accuracy. Multiple Linear Regression (MLR) is
the most common method in the literature regarding biomass estima tion to define the relation between the set of field measurements and
the statistics extracted from a LiDAR flight. Unfortunately, there exist
open issues regarding the generalization of models from one area to
another due to the lack of knowledge about noise distribution, relation ship between statistical features and risk of overfitting. Autoencoders (a
type of deep neural network) has been applied to improve the results
of machine learning techniques in recent times by undoing possible data
corruption process and improving feature selection. This paper presents
a preliminary comparison between the use of MLR with and without
preprocessing by autoencoders on real LiDAR data from two areas in
the province of Lugo (Galizia, Spain). The results show that autoen coders statistically increased the quality of MLR estimations by around
15–30%. | es |
dc.format | application/pdf | es |
dc.format.extent | 9 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | HAIS 2016 : 11th International Conference on Hybrid Artificial Intelligence Systems (2016), pp. 588-596. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Deep learning | es |
dc.subject | LiDAR | es |
dc.subject | Regression | es |
dc.subject | Remote sensing | es |
dc.subject | Soft computing | es |
dc.title | A Preliminary Study of the Suitability of Deep Learning to Improve LiDAR-Derived Biomass Estimation | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-319-32034-2_49 | es |
dc.identifier.doi | 10.1007/978-3-319-32034-2_49 | es |
dc.contributor.group | Universidad de Sevilla. TIC-254: Data Science and Big Data Lab | es |
dc.publication.initialPage | 588 | es |
dc.publication.endPage | 596 | es |
dc.eventtitle | HAIS 2016 : 11th International Conference on Hybrid Artificial Intelligence Systems | es |
dc.eventinstitution | Sevilla, España | es |
dc.relation.publicationplace | Cham, Switzerland | es |
dc.identifier.sisius | 21078236 | es |