邊坡災害為全球常見之天然災害，造成嚴重人員傷亡和經濟損失，並且亞洲地區受到的影響最明顯。為了因應邊坡災害所帶來之衝擊，國際間已經提出多項防災措施。包含聯合國世界減災會議（World Conference on Natural Disaster Reduction, WCNDR）和永續發展目標（Sustainable Development Goals, SDGs）中的 SDG 11.5 與SDG 13.1 也都和邊坡災害密切相關，顯示國際對相關議題之重視程度逐年提升。
在臺灣地震與颱風為誘發邊坡災害之主要因素，平均每年約發生上千次地震及四次颱風侵襲。加上地形條件複雜，使臺灣地區具有較高之邊坡災害潛勢。因此建立有效之邊坡災害預測模型，在降低災害損失以及支援防災措施中具有重要意義。
本研究選用語意分割（Semantic Segmentation）技術，針對指定的多波段資料集進行訓練與預測。資料集由數值高程模型、斷層分布、地質敏感區、土地利用與土地覆蓋和降水資料所建構而成。模型以 U-Net 為核心架構，為卷積神經網路（Convolutional Neural Network, CNN）的一種。模型評估主要以混淆矩陣進行，並導入空間容忍（Spatial Tolerance）的概念，以更全面地評估預測結果之空間表現。
透過所建立之邊坡災害預測模型，能夠有效辨識臺灣北部地區邊坡災害之空間分布特徵。在以邊坡災害事件為中心、影響範圍設定為 250公尺條件之下，模型成功預測79.2%之邊坡災害事件。同時，本研究亦建立了對Landsat 衛星影像進行分類的LULC分類模型，準確率達到87%。

Landslides are among the most common natural disasters around the world, causing substantial casualties and economic losses.
In Taiwan, earthquakes and typhoons are the primary triggers of landslides. With thousands of earthquakes and average four typhoons occurring annually, together with complex topographic conditions, Taiwan is highly susceptible to landslide hazards. Therefore, a landslide prediction model is needed for reducing disaster losses and supporting disaster risk management.
This study employed semantic segmentation to train and predict landslide using a multi-band dataset, and the model was based on the U-Net. Model performance was evaluated using a confusion matrix with the incorporation of spatial tolerance to provide a more comprehensive assessment of spatial prediction accuracy.
The proposed model could effectively identify the spatial distribution of landslide in northern Taiwan. Using a 250 m spatial tolerance centered on recorded landslide events, the model successfully predicted 79.2% of landslide occurrences. In addition, an LULC classification model based on Landsat imagery was developed, achieving an overall accuracy of 87%.

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