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Remote Sens. 2014, 6(4), 2628-2646; doi:10.3390/rs6042628
Article

Semi-Automatic Detection of Swimming Pools from Aerial High-Resolution Images and LIDAR Data

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Received: 19 December 2013 / Revised: 19 February 2014 / Accepted: 24 February 2014 / Published: 25 March 2014
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Abstract

Bodies of water, particularly swimming pools, are land covers of high interest. Their maintenance involves energy costs that authorities must take into consideration. In addition, swimming pools are important water sources for firefighting. However, they also provide a habitat for mosquitoes to breed, potentially posing a serious health threat of mosquito-borne disease. This paper presents a novel semi-automatic method of detecting swimming pools in urban environments from aerial images and LIDAR data. A new index for detecting swimming pools is presented (Normalized Difference Swimming Pools Index) that is combined with three other decision indices using the Dempster–Shafer theory to determine the locations of swimming pools. The proposed method was tested in an urban area of the city of Alcalá de Henares in Madrid, Spain. The method detected all existing swimming pools in the studied area with an overall accuracy of 99.86%, similar to the results obtained by support vector machines (SVM) supervised classification.
Keywords: feature extraction; land cover database; mapping updating; Dempster–Shafer; RAG; NDSPI feature extraction; land cover database; mapping updating; Dempster–Shafer; RAG; NDSPI
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Rodríguez-Cuenca, B.; Alonso, M.C. Semi-Automatic Detection of Swimming Pools from Aerial High-Resolution Images and LIDAR Data. Remote Sens. 2014, 6, 2628-2646.

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