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TitleDevelopment of a machine learning model and a user interface to detect illegal swimming pools
Author(s)Coelho, C.
Costa, M. Fernanda P.
Ferrás, Luís Jorge Lima
Soares, A. J.
KeywordsComputer vision
Deep learning
Object detection
Issue date26-Mar-2021
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 2021
Abstract(s)Portuguese 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.
TypeConference paper
Publisher version
AccessOpen access
Appears in Collections: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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