3D Shadow Analysis in Urban Areas: Artvin Coruh University Example

Yıl 2025, Cilt: 6 Sayı: 1, 73 - 81, 26.03.2025

Ziya Usta

Öz

The shadow effect created by buildings in urban areas plays a critical role in applications such as solar panel installation, urban microclimate, and energy efficiency optimization. Due to the inadequacy of traditional 2D analyses, 3D shadow analyses provide a more accurate prediction of the shading interactions between buildings. In the literature, shadow analyses have generally been conducted using 2.5D models. However, these models neglect the effects of vertical surfaces. In this study, a 3D shadow analysis was conducted using 3D models. Procedural modeling was employed for 3D modeling, and buildings were modeled at the LOD1 level based on the number of floors. Shadow analyses were performed using the Ray-Tracing algorithm, considering the daily and hourly positions of the sun. The shadow effect between buildings in Artvin Çoruh University Seyitler Campus and Center Campus was found to be low. However, high-rise buildings in close proximity create a significant shading effect. It was determined that, in addition to rooftops, southern facades receive a significant amount of sunlight, revealing the potential for solar panel installation on building facades as well. This study demonstrates that 3D shadow analysis is an important tool in urban planning processes. Another key finding is that analyses should not be limited to rooftops but should also include building facades. This approach ensures the maximum utilization of building surfaces, facilitating sustainable urban development and optimal site selection.

Anahtar Kelimeler

3D GIS, Shadow analysis, Spatial analysis, Urban analytics

Kentsel Alanlarda 3B Gölge Analizi: Artvin Çoruh Üniversitesi Örneği

Öz

Kentsel alanlarda binaların oluşturduğu gölge etkisi özellikle güneş paneli kurulumu gibi uygulamalarda, kentsel mikroklima ve enerji verimliliği optimizasyonunda kritik bir role sahiptir. Geleneksel 2B analizlerin yetersizliği nedeniyle 3B gölge analizleri, binaların birbirine gölge oluşturma durumunun daha doğru tahmin edilmesini sağlar. Literatürde genellikle 2.5B modeller kullanılarak gölge analizleri yapılmıştır. Ancak bu modeller dikey yüzeylerin etkisini göz ardı etmektedir. Bu çalışmada 3B modeller kullanarak 3B gölge analizi yapılmıştır. 3B modelleme için prosedürel modelleme yöntemi kullanılmış, kat sayıları baz alınarak binalar LOD1 düzeyinde modellenmiştir. Işın İzleme (Ray-Tracing) algoritmasıyla güneşin günlük ve saatlik konumları dikkate alınarak gölge analizleri yapılmıştır. Artvin Çoruh Üniversitesi Seyitler ve Merkez Yerleşkelerinde binalar arası gölge etkisi düşük bulunmuştur. Ancak yakın mesafedeki yüksek binalar gölgeleme etkisi yaratmaktadır. Çatılardan sonra özellikle güney cephelerinin anlamlı düzeyde güneş ışığı aldığı belirlenmiş olup, bu da dış cephelerin de güneş paneli kurulum potansiyeli olduğunu ortaya koymaktadır. Bu çalışma, 3B gölge analizinin kentsel planlama süreçlerinde önemli bir araç olduğunu göstermektedir. Çalışmada elde edilen diğer önemli bir sonuç, analizlerin sadece çatıları değil dış cepheleri de kapsaması gerektiğidir. Bu sayede bina yüzeylerinden maksimum oranda yararlanılarak sürdürülebilir kentsel gelişim ve doğru yer seçimi sağlanabilir.

Anahtar Kelimeler

3B CBS, Gölge analizi, Konumsal analiz, Kentsel analitik

Destekleyen Kurum

Bu çalışma Artvin Çoruh Üniversitesi Bilimsel Araştırma Projeleri koordinatörlüğü tarafından desteklenmiştir

Kaynakça

• Albraheem, L., & Alabdulkarim, L. (2021). Geospatial analysis of solar energy in Riyadh using a GIS-AHP-based technique. ISPRS International Journal of Geo-Information, 10(5), Article 291. https://doi.org/10.3390/ijgi10050291
• Adjiski, V., Kaplan, G., & Mijalkovski, S. (2023). Assessment of the solar energy potential of rooftops using LiDAR datasets and GIS-based approach. International Journal of Engineering and Geosciences, 8(2), 188–199.
• Alam, N., Coors, V., Zlatanova, S., & Oosterom, P. J. M. (2012). Shadow effect on photovoltaic potentiality analysis using 3D city models. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, 209–214.
• Baghani, A. (2023). Assessment of rooftop solar power potential in rural areas using UAV photogrammetry and GIS. Renewable Energy Research and Applications, 4(2), 251–258.
• Bhattacharya, S., Braun, C., & Leopold, U. (2019, May 3–5). A novel 2.5D shadow calculation algorithm for urban environment [Conference presentation]. 5th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2019), Heraklion, Crete, Greece.
• Bhattacharya, S., Braun, C., & Leopold, U. (2021). An efficient 2.5D shadow detection algorithm for urban planning and design using a tensor-based approach. ISPRS International Journal of Geo-Information, 10(9), Article 583. https://doi.org/10.3390/ijgi10090583
• 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.
• Cenky, M., Bendik, J., & Lazarenko, I. (2024, October 16–18). Rooftop photovoltaic potential estimation using QGIS and simple building shadow analysis [Conference presentation]. 2024 International Conference on Smart Systems and Technologies (SST), Osijek, Croatia.
• Clementi, M., Dessì, V., Podestà, G. M., Chien, S. C., Wei, B. A. T., & Lucchi, E. (2024). GIS-based digital twin model for solar radiation mapping to support sustainable urban agriculture design. Sustainability, 16(15), Article 6590. https://doi.org/10.3390/su16156590
• El-Hosaini, H. (2015). Locating and positioning solar panels in a 3D city model: A case study of Newcastle, UK. Journal for Geographic Information Science, 3, 147-157. https://doi.org/10.1553/giscience2015s147
• Erbil, Z. C., & Altay, B. (2023). The relationship between shadow analysis and sustainability in university campuses: The example of Selcuk University Alaeddin Keykubat Campus. Turkish Journal of Agriculture-Food Science and Technology, 11(2), 343–347.
• Gui, B., Sam, L., & Bhardwaj, A. (2024). From roofs to renewables: Deep learning and geographic information systems insights into a comprehensive urban solar photovoltaic assessment for Stonehaven. Energy 360, 1, Article 100006. https://doi.org/10.1016/j.energ.2024.100006
• Konakoğlu, S. S. K., & Usta, Z. (2019, 23–25 Ekim). Ekolojik sürdürülebilirlik kavramının 3B gölge analizi ile KTÜ Kanuni Kampüsü örneğinde irdelenmesi [Bildiri sunumu]. TMMOB 6. Coğrafi Bilgi Sistemleri Kongresi, Ankara, Türkiye.
• Kuru, A. (2023). Solar power plant site selection modeling for sensitive ecosystems. Clean Technologies and Environmental Policy, 25(8), 2529–2544.
• Massano, M., Macii, E., Lanzini, A., Patti, E., & Bottaccioli, L. (2023). A GIS open-data co-simulation platform for photovoltaic integration in residential urban areas. Engineering, 26, 198–213.
• Ni, H., Wang, D., Zhao, W., Jiang, W., Mingze, E., Huang, C., & Yao, J. (2024). Enhancing rooftop solar energy potential evaluation in high-density cities: A deep learning and GIS-based approach. Energy and Buildings, 309, Article 113743. https://doi.org/10.1016/j.enbuild.2023.113743
• Ninsawat, S., & Hossain, M. D. (2016). Identifying potential areas and financial prospects of rooftop solar photovoltaics (PV). Sustainability, 8(10), Article 1068. https://doi.org/10.3390/su8101068
• Omar, K. S., Moreira, G., Hodczak, D., Hosseini, M., Colaninno, N., Lage, M., & Miranda, F. (2024). Deep Umbra: A generative approach for sunlight access computation in urban spaces. IEEE Transactions on Big Data, 11(2), 388–401.
• Rai, B., & Trivedi, R. (2024, February 21–23). Analysis of effects on solar energy generation due to mountain shadow on Sikkim Manipal Institute of Technology, Sikkim using Shadowmap 3D GIS mapping tool [Conference presentation]. 4th International Conference on Innovative Practices in Technology and Management (ICIPTM), Noida, India.
• Soha, T., Sugár, V., & Hartmann, B. (2024). City-scale analysis of PV potential and visibility in heritage environments using GIS and LiDAR. Energy and Buildings, 311, Article 114124. https://doi.org/10.1016/j.enbuild.2024.114124
• Suprojo, B., Utami, W., Saraswati, L. A., Nabila, D. A., & Salim, M. N. (2022). Digital earth surface model for the estimation of solar panel electric power towards renewable energy. Geoplanning: Journal of Geomatics and Planning, 9(2), 103–120.
• Şenol, H. İ. (2022). Investigation of the shadow effect of urbanization on green areas with shadow impact analysis. Mugla Journal of Science and Technology, 8(1), 26–30.
• Şenyurdusev, G., & Doğru, A. Ö. (2021). Akıllı şehir uygulamaları için prosedürel 3B kent modeli oluşturulması ve fotorealistik 3B görselleştirme. Türk Uzaktan Algılama ve CBS Dergisi, 2(2), 67–75.
• Usta, Z., Akın, A. T., & Cömert, Ç. (2023). Deep learning-aided web-based procedural modeling of LOD2 city models. Earth Science Informatics, 16(3), 2559–2571.
• Usta, Z., & Cömert, Ç. (2015, September 18–20). Deriving solar energy potential of buildings in a 3D city model [Conference presentation]. First International Conference on Sea and Coastal Development in the frame of Sustainability, Trabzon, Turkey.
• Xu, L., León-Sánchez, C., Agugiaro, G., & Stoter, J. (2024). Shadowing calculation on urban areas from semantic 3D city models. In T. H. Kolbe, A. Donaubauer, & C. Beil (Eds.), Recent Advances in 3D Geoinformation Science (pp. 31–47). Springer.
• Vo, A. V., & Laefer, D. F. (2019). A big data approach for comprehensive urban shadow analysis from airborne laser scanning point clouds. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 131–137.
• Zhou, K. L., & Gorte, B. G. H. (2017). Shadow detection from VHR aerial images in urban areas by using 3D city models and a decision fusion approach. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 579–586.