請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101504完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 韓仁毓 | zh_TW |
| dc.contributor.advisor | Jen-Yu Han | en |
| dc.contributor.author | 蔡孟璇 | zh_TW |
| dc.contributor.author | Meng-Syuan Tsai | en |
| dc.date.accessioned | 2026-02-04T16:18:58Z | - |
| dc.date.available | 2026-02-05 | - |
| dc.date.copyright | 2026-02-04 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2026-01-21 | - |
| dc.identifier.citation | Asciione, F., Bianco, N., and De Stasio, C. (2017). Energy performance optimization of window-to-wall ratio for residential buildings in different climatic zones: A systematic approach. Applied Energy, 186:344–358.
Barbastathis, G., Ozcan, A., and Situ, G. (2019). On the use of deep learning for computational imaging. Optica, 6(8):921–943. Bedra, H., Al-Anbari, H., and Hasan, A. (2021). The influence of window-to-wall ratio on energy performance in buildings: A review. Energy and Buildings, 253:111498. Biljecki, F. and Ito, K. (2021). Street view imagery in urban analytics and GIS: A review. Landscape and Urban Planning, 215:104217. Chen, J. and Ran, X. (2019). Deep learning with edge computing: A review. Proceedings of the IEEE, 107(8):1655–1674. Dai, S., Li, Y., Stein, A., Yang, S., and Jia, P. (2024). Street view imagery-based built environment auditing tools: A systematic review. International Journal of Geographical Information Science, 38(6):1136–1157. Early Access: April 2024. Dai, Y., Liu, L., Wang, K., Li, M., and Yan, X. (2025). Using computer vision and street view images to assess bus stop amenities. Computers, Environment and Urban Systems, 117:102254. Early Access: January 2025. Dargan, S., Kumar, M., Ayyagari, M. R., and Kumar, G. (2020). A survey of deep learning and its applications: A new paradigm to machine learning. Archives of Computational Methods in Engineering, 27(4):1071–1092. De Simone, Z., Biswas, S., and Wu, O. (2024). Window to wall ratio detection using segformer. arXiv preprint. Del Arco, M., Gil, J., and Garcia, A. (2021). Street view imagery and computer vision for urban studies: A systematic review. Cities, 110:103064. Dong, S., Wang, P., and Abbas, K. (2021). A survey on deep learning and its applications. Computer Science Review, 40:100379. Early Access: March 2021. Facebook AI Research (2023). Detectron2 model zoo and baselines. https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md. Accessed: 2025-05-28. Fayaz, R., Muneer, T., and Ayyagari, M. R. (2015). Influence of window-to-wall ratio on daylighting performance in office buildings. Building and Environment, 85:1–13. Feng, G., Chi, D., Xu, X., Dou, B., Sun, Y., and Fu, Y. (2017). Study on the influence of window-wall ratio on the energy consumption of nearly zero energy buildings. In 10th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC2017), Jinan, China. School of Municipal and Environmental Engineering, Shenyang Jian Zhu University. Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P., and Garcia-Rodriguez, J. (2018). A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing, 70:41–65. GhaffarianHoseini, A., Berardi, U., GhaffarianHoseini, A., Alaseim, A., and Omran, A. (2016). The impact of window-to-wall ratio on energy consumption of buildings in different climates: A comprehensive review. Renewable and Sustainable Energy Reviews, 54:1419–1430. Goia, F. (2016). The impact of window-to-wall ratio on daylighting and energy performance of commercial buildings. Solar Energy, 137:337–348. Google Maps Platform (2023). Street view static API documentation. Accessed: 2024-12-01. Grynning, S., Time, B., and Gustavsen, A. (2013). Windows in buildings—performance and influence on energy consumption. Energy and Buildings, 66:249–257. Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187:27–48. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 2961–2969. He, L., Gao, K., Jin, Y., Shen, Z., Li, Y., Chi, F., and Wang, M. (2025). Research on energy efficiency evaluation system for rural houses based on improved mask R-CNN network. Sustainability, 17(3):1132. International Energy Agency (2022). Buildings: Tracking clean energy progress 2022. IEA. IPCC (2021). Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. Jaysawal, R. K., Chakraborty, S., Elangovan, D., and Padmanaban, S. (2017). Concept of net zero energy buildings (NZEB) – a literature review. In 10th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC2017), Jinan, China. Jiang, L., Wu, Y., and Liu, J. (2020). Urban building energy modeling considering facade characteristics: A case study in Shanghai. Sustainable Cities and Society, 54:101983. Karakis, I., Biyik, C., Hepbasli, A., and Aksu, I. (2019). Influence of window-to-wall ratio on energy performance of residential buildings in Turkish climates. Journal of Building Engineering, 22:97–105. Kim, J., Jeong, J., and Kim, J. T. (2017). Optimization of window-to-wall ratio for office buildings in different climates. Sustainability, 9(7):1126. Kirillov, A., He, K., Girshick, R., Rother, C., and Dollár, P. (2019). Panoptic segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9404–9413. Li, D. H., Yang, L., and Lam, J. C. (2009). Zero energy buildings and sustainable development implications—A review. Energy, 54(1):1–10. Li, J., Zheng, B., Bedra, K. B., Li, Z., and Chen, X. (2021). Evaluating the effect of window-to-wall ratios on cooling-energy demand on a typical summer day. PLOS ONE. Li, Y., Peng, L., Wu, C., and Zhang, J. (2022). Street view imagery (SVI) in the built environment: A theoretical and systematic review. Buildings, 12(8):1167. Liu, L. and Sevtsuk, A. (2024). Clarity or confusion: A review of computer vision street attributes in urban studies and planning. Cities, 150:105022. Early Access: April 2024. Ma, R., Ma, R., and Long, E. (2024). Analysis of the rule of window-to-wall ratio on energy demand of residential buildings in different locations in China. Unspecified Journal. Mapillary (2021). Mapillary API documentation. Crowdsourced street-level imagery for computer vision. Mayer, K., Heilborn, G., and Fischer, M. (2023). Classifying building energy efficiency from street view and aerial images in Denmark. Energy and Buildings, 294:113245. Meha, D., Novosel, T., and Duić, N. (2020). Bottom-up and top-down heat demand mapping methods for small municipalities, case Glogoc. Energy, 199:117429. MMSegmentation Contributors (2022). MMSegmentation PIDNet pretrained model with Cityscapes. https://github.com/open-mmlab/mmsegmentation/tree/main/configs/pidnet. Accessed: 2023-09-20. Nordmark, N. and Ayenew, M. (2021). Window detection in facade imagery: A deep learning approach using Mask R-CNN. arXiv preprint arXiv:2107.10006. Pace, G. and Fichera, S. A. (2019). Optimal window-to-wall ratio for office buildings in Mediterranean climates: A comparative analysis of various glazing types. Energy and Buildings, 194:134–148. Rahman, A., Srikumar, V., and Smith, A. D. (2018). Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Applied Energy, 212:372–385. Ramalingam, S. P. and Kumar, V. (2025). Building usage prediction in complex urban scenes by fusing text and facade features from street view images using deep learning. Building and Environment, 267:112174. Early Access: October 2024. Reinhart, C. F. and Cerezo Davila, C. (2016). Urban building energy modeling – a review of a nascent field. Building and Environment, 97:196–202. Ren, S., Hu, W., Bradbury, K., Harrison-Atlas, D., Malaguzzi Valeri, L., Murray, B., and Malof, J. M. (2022). Automated extraction of energy systems information from remotely sensed data: A review and analysis. Applied Energy, 326:119876. Rey, E. (2004). Office building retrofitting strategies: Multi-criteria approach of an architectural and technical issue. Energy and Buildings, 36(4):367–372. Robinson, C., Dilkina, B., Hubbs, J., Zhang, W., Guhathakurta, S., Brown, M. A., and Pendyala, R. M. (2017). Machine learning approaches for estimating commercial building energy consumption. Applied Energy, 208:889–904. Sezen, G., Cakir, M., Atik, M. E., and Duran, Z. (2022). Deep learning-based door and window detection from building façade. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Archives), XXIV ISPRS Congress, XLIII-B4, pages 315–320. Shi, W., Cao, J., Zhang, Q., Li, Y., and Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5):637–646. Smith, P. and Taylor, G. (2019). Improving energy efficiency in urban buildings with optimized window-to-wall ratios. Sustainable Cities and Society, 45:102362. Sun, C., Hsu, W.-C., and Lin, M. S. (2019). HorizonNet: Learning room layout with 1D representation and pano stretch data augmentation. arXiv preprint arXiv:1901.03861. Sun, X., Nie, X., Wang, L., Huang, Z., and Tian, R. (2024). Spatial sense of safety for seniors in living streets based on street view image data. Buildings, 14(12):3973. Sun, Y., Malihi, S., Li, H., and Maboudi, M. (2022). DeepWindows: Windows instance segmentation through an improved Mask R-CNN using spatial attention and relation modules. ISPRS International Journal of Geo-Information, 11(3):162. Unknown (2017). Impact of glazing to wall ratio in various climatic regions: A case study. Journal of King Saud University – Engineering Sciences, 31(1). Viso.ai (2023). Panoptic segmentation in computer vision: An overview. https://viso.ai/deep-learning/panoptic-segmentation/. Accessed: 2025-05-21. Voulodimos, A., Doulamis, N., Doulamis, A., and Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018:7068349. Wang, C., Zhang, Q., and Li, B. (2022a). Automatic estimation of window-to-wall ratio from street view images using deep learning. Automation in Construction, 139:104261. Wang, S., Kang, Q., She, R., Tay, W. P., Navarro, D., and Hartmannsgruber, A. (2022b). Building facade parsing R-CNN. arXiv preprint arXiv:2205.05912. Wang, Y., Xu, Y., Zhou, Y., and Tai, Y. (2023). PIDNet: Proportional-integral-derivative network for real-time semantic segmentation. https://github.com/open-mmlab/mmsegmentation/tree/main/configs/pidnet. Accessed: 2023-09-15. Wu, J., Liu, W., and Maruyama, Y. (2024). Street view image-based road marking inspection system using computer vision and deep learning techniques. Sensors, 24(23):7724. Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., and Girshick, R. (2019). Detectron2. Facebook AI Research. Yue, H. (2025). Investigating streetscape environmental characteristics associated with road traffic crashes using street view imagery and computer vision. Accident Analysis and Prevention, 210:107851. Early Access: November 2024. Zamir, S. W., Arora, A., Khan, S. H., Hayat, M., and Yang, M.-H. (2019). iSAID: A large-scale dataset for instance segmentation in aerial images. Remote Sensing, 11(1):47. Zhong, D., He, L., and Lin, Y. (2023). An improved Mask R-CNN: Extraction of door and window instances on village building façade images. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, volume X-1/W1-2023, pages 295–301. Zhou, W., Yang, M., Wang, H., and Tang, J. (2019). CV4Building: A computer vision benchmark dataset for building segmentation in street view images. In 2019 IEEE International Conference on Image Processing (ICIP), pages 1570–1574. IEEE. 瑞竣科技 (2022). 初探臺北市建物樓高分布. https://www.richitech.com.tw/6818/%E5%88%9D%E6%8E%A2%E8%87%BA%E5%8C%97%E5%B8%82%E5%BB%BA%E7%89%A9%E6%A8%93%E9%AB%98%E5%88%86%E5%B8%83/. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101504 | - |
| dc.description.abstract | 隨著氣候變遷持續加劇，實現「淨零碳排放」（Net Zero Emissions）已成為全球各國政府與產業的重要目標。建築部門約佔全球能源消耗的36%，並產生相當比例的碳排放，成為能源效率政策中最關鍵的施力點之一。其中，窗牆比（Window-to-Wall Ratio, WWR）作為建築設計中的重要參數，不僅影響自然採光與通風條件，更直接關係到建築物的熱舒適性與能源使用表現。適當的WWR設計有助於平衡日照利用與空調（Heating, Ventilation, and Air Conditioning, HVAC）需求，進而提升建築能效與室內環境品質，對於推動永續都市發展具有重要意義。然而，現行WWR資料多仰賴人工丈量或建築圖面擷取，不僅成本高、效率低，亦難以應用於都市尺度之建築能源分析。本研究以臺北市為實證場域，提出一套結合地理資訊系統（GIS）、街景影像與深度學習之自動化WWR資料擷取流程。方法首先透過Google Street View靜態API自動取得建築立面影像，並以Detectron2進行實例分割，準確辨識建築牆面與窗戶區域，再以遮罩像素統計方式計算WWR數值，並整合至空間屬性資料中。此流程可大幅減少人工介入，並具備高效率與擴展性，適用於大規模都市環境下的建築資料分析需求。本研究實際應用於建築樣式多元且街廓密集之臺北市區，驗證其可行性與自動化程度，並針對模型在標註品質、資料地區化與遮蔽物干擾等面向提出改進方向。研究成果顯示，本方法具備實用性與推廣潛力，能有效支援都市建築節能改造、碳排盤查與永續城市規劃等應用場景，為未來建築資訊擷取技術提供具體貢獻。 | zh_TW |
| dc.description.abstract | As climate change accelerates, achieving Net Zero Emissions has become a global priority. The building sector, responsible for about 36% of global energy use, plays a key role in carbon reduction. Among design factors, the Window-to-Wall Ratio (WWR) significantly impacts natural lighting, ventilation, and energy performance. However, conventional WWR data collection relies on manual measurement or drawings, limiting scalability.This study proposes an automated WWR extraction workflow combining Google Street View imagery, deep learning, and GIS. Using Detectron2 for instance segmentation, building façades and windows are identified from street images, and WWR is calculated via pixel-based mask statistics. The method is applied to dense urban areas in Taipei, demonstrating its feasibility and scalability.Results show that the approach effectively reduces manual effort and enables large-scale WWR analysis, supporting applications such as energy retrofitting, carbon audits, and sustainable urban planning. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-02-04T16:18:58Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-02-04T16:18:58Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 目次
誌謝 ii 摘要 iii Abstract iv 目次 v 圖次 vii 表次 ix 第一章 緒論 1 1.1 研究背景與目的 1 1.2 論文架構 4 第二章 文獻回顧 5 2.1 窗牆比定義與研究進展 5 2.2 窗牆比對建築能效的影響 6 2.3 傳統窗牆比資料取得方式與限制 7 2.4 街景影像的取得方法 8 2.5 電腦視覺用於街景影像 10 2.6 電腦視覺於建築立面元素擷取 11 2.7 小結 13 第三章 研究方法與理論 14 3.1 建物影像資料蒐集 16 3.1.1 建物點位資料取得 16 3.1.2 街景影像資料取得與建物影像集建立 17 3.2 街景影像之背景與遮蔽物去除 19 3.3 建物立面牆面區域提取 22 3.3.1 自定義資料集的格式轉換與註冊 23 3.3.2 實例分割之模型選擇與訓練 24 3.4 建物立面窗戶區域提取 25 3.5 實例分割之模型評估指標 27 3.6 整合 GIS 進行屬性建構與窗牆比計算 29 3.7 窗牆比精度驗證 31 3.8 小結 33 第四章 研究成果與討論 34 4.1 建物立面資料集建立 34 4.1.1 建築物點位取得成果 34 4.1.2 建物影像集建立成果 36 4.2 背景與遮蔽物去除成果 39 4.3 牆面積提取成果 42 4.3.1 背景剔除對牆面分割的影響 45 4.4 窗戶面積提取成果 46 4.4.1 遮蔽問題探討 51 4.4.2 投影變形問題探討 53 4.5 窗牆比提取精度驗證 55 4.6 建物立面資訊與 GIS 平台整合分析 58 4.6.1 大安區窗牆比與建物空間分佈 58 4.6.2 窗牆比與都市環境影響探討 63 4.6.3 窗牆比可信度分級與應用考量 64 4.7 小結 66 第五章 結論與建議 67 5.1 結論 67 5.2 建議與未來工作 68 參考文獻 70 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 窗牆比 | - |
| dc.subject | 城市建築能源消耗 | - |
| dc.subject | 影像辨識 | - |
| dc.subject | 深度學習 | - |
| dc.subject | 決策支援 | - |
| dc.subject | Window-to-Wall Ratio (WWR) | - |
| dc.subject | Urban Building Energy Consumption | - |
| dc.subject | Image Recognition | - |
| dc.subject | Deep Learning | - |
| dc.subject | Decision Support | - |
| dc.title | 基於街景影像之建築物窗牆比的自動化提取與分析 | zh_TW |
| dc.title | Automated Extraction and Analysis of Building Window-to-Wall Ratios Using Street View Imagery | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 郭重言;葉大綱;黃春嘉 | zh_TW |
| dc.contributor.oralexamcommittee | Chung-Yen Kuo;Da-Gang Ye ;Chun-Jia Huang | en |
| dc.subject.keyword | 窗牆比,城市建築能源消耗影像辨識深度學習決策支援 | zh_TW |
| dc.subject.keyword | Window-to-Wall Ratio (WWR),Urban Building Energy ConsumptionImage RecognitionDeep LearningDecision Support | en |
| dc.relation.page | 76 | - |
| dc.identifier.doi | 10.6342/NTU202501158 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2026-01-21 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2031-01-19 | - |
| 顯示於系所單位： | 土木工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-114-1.pdf 未授權公開取用 | 25.57 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。