- -

Development and validation in a 2D-GIS environment of a 3D shadow cast vector-based model on arbitrarily orientated and tilted surfaces

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Development and validation in a 2D-GIS environment of a 3D shadow cast vector-based model on arbitrarily orientated and tilted surfaces

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Viana-Fons, Joan Dídac es_ES
dc.contributor.author Gonzálvez-Maciá, José es_ES
dc.contributor.author Payá-Herrero, Jorge es_ES
dc.date.accessioned 2021-05-22T03:31:59Z
dc.date.available 2021-05-22T03:31:59Z
dc.date.issued 2020-10-01 es_ES
dc.identifier.issn 0378-7788 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166652
dc.description.abstract [EN] This paper presents a systematic GIS-based methodology to obtain the shadow cast profile of a group of buildings on arbitrarily orientated and tilted surfaces. The model is integrated in the widely-employed 2D-GIS environment. Given its scalability, the methodology can be easily applied from a local level up to a district, city or even regional level. This work is of interest for a wide range of applications such as for instance in Solar Resource Assessments (SRA) in urban environments. The starting point is to use cadastral cartography and LiDAR altimetric data to obtain a 3D vector-based model of the buildings using high robust mode estimators. Once the geometry of the buildings is defined, analytical models are applied to calculate the shadow cast profile on any arbitrarily orientated and tilted surface of the surroundings. The model has been implemented in the R programming language. An extensive validation has been carried out for several buildings of Valencia (Spain) using CAD elevation views of the buildings and the SketchUp's shadow tool. The error of the vector-based city model is lower than 1% in all LiDAR datasets. The maximum error of the overall methodology, including both height and shadow models, is lower than 2%. (c) 2020 Elsevier B.V. All rights reserved. es_ES
dc.description.sponsorship This work has been supported by the Generalitat Valenciana inside the program "Subvencions per a la contractacio de personal investigador de caracter predoctoral (ACIF/2019/239)". es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Energy and Buildings es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Urban shadow model es_ES
dc.subject Urban solar irradiation es_ES
dc.subject 3D city model es_ES
dc.subject Daylight simulation es_ES
dc.subject Model validation es_ES
dc.subject GIS es_ES
dc.subject.classification MAQUINAS Y MOTORES TERMICOS es_ES
dc.title Development and validation in a 2D-GIS environment of a 3D shadow cast vector-based model on arbitrarily orientated and tilted surfaces es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.enbuild.2020.110258 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//ACIF%2F2019%2F239/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Termodinámica Aplicada - Departament de Termodinàmica Aplicada es_ES
dc.description.bibliographicCitation Viana-Fons, JD.; Gonzálvez-Maciá, J.; Payá-Herrero, J. (2020). Development and validation in a 2D-GIS environment of a 3D shadow cast vector-based model on arbitrarily orientated and tilted surfaces. Energy and Buildings. 224:1-10. https://doi.org/10.1016/j.enbuild.2020.110258 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.enbuild.2020.110258 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 10 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 224 es_ES
dc.relation.pasarela S\427355 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.description.references U. Nations, D. of Economic, S. Affairs, P. Division, World Urbanization Prospects: The 2018 Revision, New York, 2019. https://doi.org/10.18356/b9e995fe-en. es_ES
dc.description.references Coalition for Urban Transitions, Climate Emergency, Urban Opportunity, London and Washington, DC, 2019. https://urbantransitions.global/urban-opportunity/. es_ES
dc.description.references R. Billen, A.-F. Cutting-Decelle, O. Marina, J.-P. de Almeida, C. M., G. Falquet, T. Leduc, C. Métral, G. Moreau, J. Perret, G. Rabin, R. San Jose, I. Yatskiv, S. Zlatanova, 3D City Models and urban information: Current issues and perspectives, in: EDP Sciences, 2014: pp. I–118. https://doi.org/10.1051/tu0801/201400001. es_ES
dc.description.references Biljecki, F., Stoter, J., Ledoux, H., Zlatanova, S., & Çöltekin, A. (2015). Applications of 3D City Models: State of the Art Review. ISPRS International Journal of Geo-Information, 4(4), 2842-2889. doi:10.3390/ijgi4042842 es_ES
dc.description.references Oregi, X., Hermoso, N., Prieto, I., Izkara, J. L., Mabe, L., & Sismanidis, P. (2018). Automatised and georeferenced energy assessment of an Antwerp district based on cadastral data. Energy and Buildings, 173, 176-194. doi:10.1016/j.enbuild.2018.05.018 es_ES
dc.description.references Freitas, S., Catita, C., Redweik, P., & Brito, M. C. (2015). Modelling solar potential in the urban environment: State-of-the-art review. Renewable and Sustainable Energy Reviews, 41, 915-931. doi:10.1016/j.rser.2014.08.060 es_ES
dc.description.references Machete, R., Falcão, A. P., Gomes, M. G., & Moret Rodrigues, A. (2018). The use of 3D GIS to analyse the influence of urban context on buildings’ solar energy potential. Energy and Buildings, 177, 290-302. doi:10.1016/j.enbuild.2018.07.064 es_ES
dc.description.references Lima, I., Scalco, V., & Lamberts, R. (2019). Estimating the impact of urban densification on high-rise office building cooling loads in a hot and humid climate. Energy and Buildings, 182, 30-44. doi:10.1016/j.enbuild.2018.10.019 es_ES
dc.description.references G. and S.R. Ward, Rendering with Radiance: The Art and Science of Lighting Visualization, Morgan Kaufman. (1998). es_ES
dc.description.references Teller, J., & Azar, S. (2001). Townscope II—A computer system to support solar access decision-making. Solar Energy, 70(3), 187-200. doi:10.1016/s0038-092x(00)00097-9 es_ES
dc.description.references Miguet, F., & Groleau, D. (2002). A daylight simulation tool for urban and architectural spaces—application to transmitted direct and diffuse light through glazing. Building and Environment, 37(8-9), 833-843. doi:10.1016/s0360-1323(02)00049-5 es_ES
dc.description.references Trimble Inc., SketchUp, (2019). https://www.sketchup.com. es_ES
dc.description.references Robert McNeel & Associates, Rhinoceros, (2019). https://www.rhino3d.com. es_ES
dc.description.references Autodesk Inc., Autodesk Revit, (2020). https://www.autodesk.com/products/revit/overview. es_ES
dc.description.references G. Desthieux, C. Carneiro, R. Camponovo, P. Ineichen, E. Morello, A. Boulmier, N. Abdennadher, S. Dervey, C. Ellert, Solar Energy Potential Assessment on Rooftops and Facades in Large Built Environments Based on LiDAR Data, Image Processing, and Cloud Computing. Methodological Background, Application, and Validation in Geneva (Solar Cadaster), Frontiers in Built Environment. 4 (2018) 14. https://doi.org/10.3389/fbuil.2018.00014. es_ES
dc.description.references Vartholomaios, A. (2019). A machine learning approach to modelling solar irradiation of urban and terrain 3D models. Computers, Environment and Urban Systems, 78, 101387. doi:10.1016/j.compenvurbsys.2019.101387 es_ES
dc.description.references Assouline, D., Mohajeri, N., & Scartezzini, J.-L. (2017). Quantifying rooftop photovoltaic solar energy potential: A machine learning approach. Solar Energy, 141, 278-296. doi:10.1016/j.solener.2016.11.045 es_ES
dc.description.references J. Hofierka, M. Súri, The solar radiation model for Open source GIS: implementation and applications, in: Open Source GIS - GRASS Users Conference, 2002. es_ES
dc.description.references J.G. Corripio, insol: Solar Radiation. R package version 1.2.1, (2019). https://cran.r-project.org/package=insol (accessed January 9, 2020). es_ES
dc.description.references P. Fu, P.M. Rich, Design and Implementation of the Solar Analyst: an ArcView Extension for Modeling Solar Radiation at Landscape Scales, 19th Annual ESRI User Conference. (1999) 1–24. es_ES
dc.description.references Esri, What is raster data? - Help | ArcGIS Desktop, (n.d.). https://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/what-is-raster-data.htm (accessed January 9, 2020). es_ES
dc.description.references Brito, M. C., Freitas, S., Guimarães, S., Catita, C., & Redweik, P. (2017). The importance of facades for the solar PV potential of a Mediterranean city using LiDAR data. Renewable Energy, 111, 85-94. doi:10.1016/j.renene.2017.03.085 es_ES
dc.description.references Catita, C., Redweik, P., Pereira, J., & Brito, M. C. (2014). Extending solar potential analysis in buildings to vertical facades. Computers & Geosciences, 66, 1-12. doi:10.1016/j.cageo.2014.01.002 es_ES
dc.description.references Brito, M. C., Redweik, P., Catita, C., Freitas, S., & Santos, M. (2019). 3D Solar Potential in the Urban Environment: A Case Study in Lisbon. Energies, 12(18), 3457. doi:10.3390/en12183457 es_ES
dc.description.references Kaynak, S., Kaynak, B., & Özmen, A. (2018). A software tool development study for solar energy potential analysis. Energy and Buildings, 162, 134-143. doi:10.1016/j.enbuild.2017.12.033 es_ES
dc.description.references Liang, J., Gong, J., Zhou, J., Ibrahim, A. N., & Li, M. (2015). An open-source 3D solar radiation model integrated with a 3D Geographic Information System. Environmental Modelling & Software, 64, 94-101. doi:10.1016/j.envsoft.2014.11.019 es_ES
dc.description.references Liang, J., & Gong, J. (2017). A Sparse Voxel Octree-Based Framework for Computing Solar Radiation Using 3D City Models. ISPRS International Journal of Geo-Information, 6(4), 106. doi:10.3390/ijgi6040106 es_ES
dc.description.references Hofierka, J., & Zlocha, M. (2012). A New 3-D Solar Radiation Model for 3-D City Models. Transactions in GIS, 16(5), 681-690. doi:10.1111/j.1467-9671.2012.01337.x es_ES
dc.description.references QGIS Development Team, Vector Data - Documentation for QGIS 3.4, (n.d.). https://docs.qgis.org/3.4/en/docs/gentle_gis_introduction/vector_data.html (accessed January 9, 2020). es_ES
dc.description.references Q.Y. Zhou, U. Neumann, Fast and extensible building modeling from airborne LiDAR data, in: GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 2008: pp. 43–50. https://doi.org/10.1145/1463434.1463444. es_ES
dc.description.references Jianhua Yan, Keqi Zhang, Chengcui Zhang, Shu-Ching Chen, & Narasimhan, G. (2015). Automatic Construction of 3-D Building Model From Airborne LIDAR Data Through 2-D Snake Algorithm. IEEE Transactions on Geoscience and Remote Sensing, 53(1), 3-14. doi:10.1109/tgrs.2014.2312393 es_ES
dc.description.references Gröger, G., & Plümer, L. (2012). CityGML – Interoperable semantic 3D city models. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 12-33. doi:10.1016/j.isprsjprs.2012.04.004 es_ES
dc.description.references Wang, R., Peethambaran, J., & Chen, D. (2018). LiDAR Point Clouds to 3-D Urban Models$:$ A Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(2), 606-627. doi:10.1109/jstars.2017.2781132 es_ES
dc.description.references Biljecki, F., Ledoux, H., Stoter, J., & Vosselman, G. (2016). The variants of an LOD of a 3D building model and their influence on spatial analyses. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 42-54. doi:10.1016/j.isprsjprs.2016.03.003 es_ES
dc.description.references Biljecki, F., Ledoux, H., & Stoter, J. (2016). An improved LOD specification for 3D building models. Computers, Environment and Urban Systems, 59, 25-37. doi:10.1016/j.compenvurbsys.2016.04.005 es_ES
dc.description.references Weiler, K., & Atherton, P. (1977). Hidden surface removal using polygon area sorting. ACM SIGGRAPH Computer Graphics, 11(2), 214-222. doi:10.1145/965141.563896 es_ES
dc.description.references Strzalka, A., Alam, N., Duminil, E., Coors, V., & Eicker, U. (2012). Large scale integration of photovoltaics in cities. Applied Energy, 93, 413-421. doi:10.1016/j.apenergy.2011.12.033 es_ES
dc.description.references M. Dorman, E. Erell, A. Vulkan, I. Kloog, shadow: R Package for Geometric Shadow Calculations in an Urban Environment, The R J. 11 (2019) 287. https://doi.org/10.32614/rj-2019-024. es_ES
dc.description.references Ledoux, H., & Meijers, M. (2011). Topologically consistent 3D city models obtained by extrusion. International Journal of Geographical Information Science, 25(4), 557-574. doi:10.1080/13658811003623277 es_ES
dc.description.references M. Bartels, H. Wei, D.C. Mason, DTM generation from LIDAR data using skewness balancing, in: Proceedings - International Conference on Pattern Recognition, 2006: pp. 566–569. https://doi.org/10.1109/ICPR.2006.463. es_ES
dc.description.references Bickel, D. R. (2003). Robust and efficient estimation of the mode of continuous data: the mode as a viable measure of central tendency. Journal of Statistical Computation and Simulation, 73(12), 899-912. doi:10.1080/0094965031000097809 es_ES
dc.description.references Bickel, D. R., & Frühwirth, R. (2006). On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications. Computational Statistics & Data Analysis, 50(12), 3500-3530. doi:10.1016/j.csda.2005.07.011 es_ES
dc.description.references Esri, Obtaining elevation information for building footprints - Help | ArcGIS Desktop, (n.d.). https://desktop.arcgis.com/en/arcmap/latest/extensions/3d-analyst/3d-buildings-obtaining-elevation-information-for-building-footprints.htm (accessed January 9, 2020). es_ES
dc.description.references S.J. Sheather, M.C. Jones, A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation, Journal of the Royal Statistical Society. Series B (Methodological). 53 (1991) 683–690. http://www.jstor.org/stable/2345597. es_ES
dc.description.references R Core Team, R: A Language and Environment for Statistical Computing, (2019). https://www.r-project.org/. es_ES
dc.description.references Cheng, H., & Gupta, K. C. (1989). An Historical Note on Finite Rotations. Journal of Applied Mechanics, 56(1), 139-145. doi:10.1115/1.3176034 es_ES
dc.description.references Instituto Geográfico Nacional, Plan Nacional de Ortofotografía Aérea - Especificaciones técnicas, (n.d.). https://pnoa.ign.es/especificaciones-tecnicas (accessed January 9, 2020). es_ES
dc.description.references Yezioro, A., & Shaviv, E. (1994). Shading: A design tool for analyzing mutual shading between buildings. Solar Energy, 52(1), 27-37. doi:10.1016/0038-092x(94)90078-g es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem