Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/75102

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dc.contributor.authorCoelho, C.por
dc.contributor.authorCosta, M. Fernanda P.por
dc.contributor.authorFerrás, Luís Jorge Limapor
dc.contributor.authorSoares, A. J.por
dc.date.accessioned2021-12-23T09:40:16Z-
dc.date.available2021-12-23T09:40:16Z-
dc.date.issued2021-03-26-
dc.date.submitted2021-02-
dc.identifier.citationC. Coelho, M. Fernanda P. Costa, L.L. Ferrás, A.J. Soares, Development of a Machine Learning Model and a User Interface to Detect Illegal Swimming Pools, SYMCOMP 2021, 5th International Conference on Numerical and Symbolic Computation: Developments and Applications, Évora, Portugal, 25-26 March 2021por
dc.identifier.isbn978-989-99410-6-9por
dc.identifier.urihttps://hdl.handle.net/1822/75102-
dc.description.abstractPortuguese legislation states the compulsory reporting of the addition of amenities, such as swimming pools, to the Portuguese tax authority. The purpose is to update the property tax value, to be charged annually to the owner of each real estate. According to MarketWatch, this decade will bring a global rise to the number of swimming pools due to certain factors such as: cost reduction, increasing health consciousness, and others. The need for inspections to ensure that all new constructions are communicated to the competent authorities is therefore rapidly increasing and new solutions are needed to address this problem. Typically, supervision is done by sending human resources to the field, involving huge time and resource consumption, and preventing the catalogue from updating at a rate close to the speed of construction. Automation is rapidly becoming an absolute requirement to improve task efficiency and affordability. Recently, Deep Learn- ing algorithms have shown incredible performance results when used for object detection tasks. Based on the above, this work presents a study on the various existing object detec- tion algorithms and the implementation of a Deep Learning model capable of recognizing swimming pools from satellite images. To achieve the best results for this specific task, the RetinaNet algorithm was chosen. To provide a smooth user experience with the developed model, a simple graphical user interface was also created.por
dc.language.isoengpor
dc.relationUIDB/00013/2020 and UIDP/00013/2020por
dc.rightsopenAccesspor
dc.subjectComputer visionpor
dc.subjectDeep learningpor
dc.subjectObject detectionpor
dc.subjectResNetpor
dc.subjectRetinaNetpor
dc.subjectMatlabpor
dc.titleDevelopment of a machine learning model and a user interface to detect illegal swimming poolspor
dc.typeconferencePaperpor
dc.peerreviewedyespor
dc.relation.publisherversionhttps://www.eccomas.org/wp-content/uploads/sites/15/2021/04/symcomp2021_proceedings.pdfpor
oaire.citationConferenceDate25-26 March 2021por
sdum.event.titleSYMCOMP 2021 - 5th International Conference on Numerical and Symbolic Computation Developments and Applicationspor
sdum.event.typeconferencepor
oaire.citationConferencePlaceÉvora, Portugalpor
dc.subject.fosCiências Naturais::Matemáticaspor
sdum.conferencePublicationSYMCOMP 2021por
oaire.versionVoRpor
Aparece nas coleções:CMAT - Artigos em atas de conferências e capítulos de livros com arbitragem / Papers in proceedings of conferences and book chapters with peer review

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