博碩士論文 etd-0716121-193218 詳細資訊


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姓名 蔡志明(Chih-Ming Tsai) 電子郵件信箱 E-mail 資料不公開
畢業系所 資訊管理學系研究所(Department of Information Management)
畢業學位 碩士(Master) 畢業時期 109學年第2學期
論文名稱(中) 基於卷積類神經網路的房屋價值估值分析-以高雄市街景影像為例
論文名稱(英) Housing Price Evaluation based on Convolutional Neural Networks—A Case Study of Street View Image Analysis for Kaohsiung City.
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    摘要(中)      深度學習卷積類神經網路(Convolutional Neural Networks ,CNN)可有效提取影像特徵,自監督式學習自動編碼器(AutoEncoder ,AE)可將資訊壓縮成較低維度具代表性的資料,輕量梯度提升機器(Light Gradient Boosting Machine, LightGBM)是一種基於決策樹boosting的機器學習演算法,可快速建立可解釋性的模型及利用梯度權重分類激勵映射(Gradient-weighted Class Activation Mapping, Grad-CAM) 可對基於CNN的模型產生的決策生成,以視覺方式解釋關注的位置及區域。
         本研究提出利用機器學習及深度學習等相關技術,建構一個房屋估價模型,用以協助民眾、相關地政機關及不動產服務業者評估房屋正常合理的價格,透過CNN、AE、LightGBM、Grad-CAM等方法進行房屋估價建模的研究,並包含房屋實價登錄及建物街景影像資料的抓取及預處理、轉換特徵及圖像自動視覺化解釋等一併探討,以達本研究目標。
    摘要(英) Deep learning convolutional neural networks (Convolutional Neural Networks, CNN) can effectively extract image features, self-supervised learning autoencoder (AutoEncoder, AE) can compress information into lower-dimensional representative data, LightGBM (Light Gradient Boosting Machine) is a machine learning algorithm based on decision tree boosting, which can quickly build interpretable models and use Grad-weighted Class Activation Mapping (Grad-CAM) to generate decisions based on CNN-based models and interpret them visually The location and area of interest.
    This paper proposes the use of machine learning and deep learning and other related technologies to construct a house valuation model to assist the public, relevant land administration agencies, and real estate service providers to evaluate the normal and reasonable price of houses through CNN, AE, LightGBM, Grad-CAM and other methods are used to conduct research on building valuation modeling, including real-price registration of houses, capture and preprocessing of building street view image data, conversion features and automatic image interpretation, etc., in order to achieve the goal of this research .
    關鍵字(中)
  • 房屋估價
  • 卷積類神經網路
  • 圖像特徵提取
  • 自動編碼器
  • LightGBM
  • Grad-CAM
  • 關鍵字(英)
  • Housing Price
  • Convolutional Neural Networks
  • Image feature extraction
  • AutoEncoder
  • LightGBM
  • Grad-CAM
  • 論文目次 論文審定書 i
    誌謝 ii
    摘要 iii
    Abstract iv
    圖次 vi
    表次 vii
    第一章、 緒論 1
    第一節 研究背景 1
    第二節 研究動機 2
    第三節 研究目的 2
    第二章、 文獻探討 3
    第一節 實價登錄 3
    第二節 影響房價重要變數 4
    第三節 深度學習 5
    第四節 自動編碼器 7
    第五節 LightGBM框架 8
    第六節 Grad-CAM方法 9
    第三章、 研究方法與步驟 11
    第一節 研究架構 11
    第二節 研究方法 12
    第三節 提取建物暨周遭街景影像特徵 14
    第四節 實價登錄資料之移轉層次資料降維處理 15
    第五節 塑建房價預測模型 16
    第六節 評估建物暨周遭街景影像模型 18
    第七節 建模成果評估標準 18
    第四章、 實驗結果與討論分析 19
    第一節 研究資料 19
    第二節 CNN特徵擷取結果 21
    第三節 AE降維結果 22
    第四節 建模結果及比較分析 23
    第五節 建物街景影像特徵視覺化分析結果 25
    第六節 研究限制 27
    第五章、 研究結論與建議 28
    第六章、 參考文獻 29
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    口試委員
  • 林耕霈 - 召集委員
  • 李珮如 - 委員
  • 康藝晃 - 指導教授
  • 口試日期 2021-07-02 繳交日期 2021-08-16

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