隨著全球暖化與氣候變遷等問題，都市熱島效應與都市熱環境日漸加劇，不只嚴重影響都市中人們的舒適度，更會對人體健康帶來不同程度的影響，故都市熱島的緩解與調適十分重要。其中都市熱島的緩解與調適策略可大致分為三種，包括增加水綠比例、都市通風廊道與增加都市遮蔭，不只能有效為都市帶來降溫效果，更能改善都市中的熱環境。然而，大部分的都市研究皆高度依賴官方提供的都市模型，或是昂貴儀器監測所得的高精度都市模型，而上述數據集的製作與維護成本皆相對高昂，非所有研究者都能負擔，故本研究旨在結合遙測技術與人工智慧演算法，解決上述問題，並應用於都市熱島之緩解與調適策略，從而降低相關研究的成本。
本研究以三個案例，呈現遙測技術與人工智慧演算法之結合如何協助都市建模與都市熱島緩解調適策略之制定。在各個案例中，本研究皆使用U-net神經網路從SPOT 6衛星影像中萃取建物面積，並後續與標準化數位表面模型(Normalized Digital Surface Model, nDSM)套疊，以生成都市三維模型作後續分析應用。在案例一，本研究透過都市模型與衛星影像探討台北市都市發展強度對都市空氣溫度之影響，並後續利用機器學習模型學習都市發展強度與空氣溫度之關係，以建立一都市空氣溫度之預測模式；在案例二，本研究利用都市模型計算台中市的粗糙長度分布，並後續應用A*啟發式搜尋演算法指認示範區與台中市區的潛在都市風廊，以評估台中市內的都市環境，並提供對應的建議；在案例三，本研究利用都市模型與地理資訊系統計算國立成功大學與其附近區域的天空可視率分布，以評估該區域之遮蔭分布，並後續應用於不同時段之熱環境評估，以指認需改善的區域。
研究成果顯示，利用U-net神經網路從SPOT 6衛星影像萃取的建物面積準確率達75%以上，其後套疊nDSM能有效完成都市建模，供後續研究應用。在台北市，每增加10%的綠覆率可能帶來0.32的降溫，每增加10%的建敞率與容積率可能帶來0.28與0.03的升溫，而機器學習模型也良好地學習上述之關係，以準確預測都市中的空氣溫度，其R2最高達0.98；在台中市，A*演算法指認之風廊大多平均風速皆高於其背景平均風速，確立了上述方法之可行性與適用性。另外，其潛在風廊的指認更能有效反映台中市的通風環境，如台中市區較差的通風狀況、道路網路對都市通風的重要性等；在台南市，本研究確立了利用上述都市模型大範圍天空可視率計算之可行性，其平均誤差與平均絕對誤差最高較官方數據高0.09與0.04，其天空可視率之分布同時能有效協助後續熱環境之評估，指認需改善的區域。綜合而言，本研究確立了遙測技術與人工智慧演算法之結合在都市研究中的適用性，不只能有效降低都市建模之成本，更能有效協助都市熱島緩解與調適策略之制定，其發展潛力無限。

To reduce the impact from urban heat island on people, mitigation and adaptation are required. However, the cost of urban modeling and related analysis limited the development of above mitigation and adaptation. Therefore, this study aimed to resolve the above problem using remote sensing and artificial intelligent. According to the advancements of science and technology, remote sensing and artificial intelligent became the most critical techniques worldwide, which make various works became more efficient and effective. Hence, this study first developed a method for urban modeling based on SPOT 6 satellite images and U-net model. The proposed method was easy-to-use and effective, which achieved an accuracy rate over 75% in building segmentation. According to the above proposed method, the cost of urban modeling can be reduced. After that, methods for urban heat island mitigation and adaptation were further developed using different kinds of artificial intelligent algorithms. Fist of all, Extreme Gradient Boosting (XGB) and Deep Neural Network (DNN) were applied to understand the relationship between urban development intensity and air temperature. The above models achieved a R2 score and MAE over 0.9 and lower than 1 ◦C, respectively. With the above models, the cost of urban climate monitoring can be reduced, and air temperature mapping can be more efficient and effective. Moreover, A* algorithm was applied for urban ventilation corridors identification. The identified ventilation corridors have relatively high ventilation conditions compared to the background, which validated using computational fluid dynamics (CFD) simulation. Based on the above method, urban ventilation conditions can be briefly identified in the early urban planning, such as identifying the regions need urgent improvements. Finally, a new method for tree canopy segmentation was proposed based on SPOT 6 satellite images and K-means clustering. The proposed method can effectively help in tree canopy segmentation, which is helpful to enhance the shading assessment within cities. With the above methods, including urban and tree canopy modeling, the shading level and thermal conditions within cities can be assessed and simulated easily and conveniently.

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