The application of lidar data for the solar potential analysis based on urban 3D model
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2019
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SPIE
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This paper describes the methodology proposed for 1) calculate solar potential; 2) generate an urban 3D model, both based on LIDAR data; 3) semantize the urban 3D model with different data sources and calculations data for finally, 4) visualize the urban 3D model in a 3D web visualization tool. As a first step, digital surface model data of the case study is preprocessed selecting only building and ground points in order to later calculate the solar potential in a GIS tool. A workflow is presented describing the followed steps. In the second step, an urban 3D model is generated (in CityGML format) using cadastral data and LIDAR data, both digital surface and digital terrain model. Then, in the third step, the urban 3D model is semantized with a) buildings data (that comes from cadastral and statistical office), b) geometrical data such as main building orientation, number of adjoining walls, etc. (that comes from a geometric processing tool), c) key performance indicators data (that are calculated based on the urban 3D model data) and d) solar potential data, which have been calculated in the previous step. In the fourth step, all the gathered data is presented and can be filtered/selected in a 3D web visualization tool. This paper shows the potential of the usage of LIDAR data in different domains that can be connected using different technologies and in different scales.
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Prieto , I , Izkara , J L & Usobiaga , E 2019 , The application of lidar data for the solar potential analysis based on urban 3D model . in K Themistocleous , G Papadavid , S Michaelides , V Ambrosia & D G Hadjimitsis (eds) , Seventh International Conference on Remote Sensing and Geoinformation of the Environment, RSCy 2019 . , 1117418 , Proceedings of SPIE - The International Society for Optical Engineering , vol. 11174 , SPIE , 7th International Conference on Remote Sensing and Geoinformation of the Environment, RSCy 2019 , Paphos , Cyprus , 18/03/19 . https://doi.org/10.1117/12.2531938
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