• 期刊

Road Marking Extraction and Classification from Mobile LiDAR Point Clouds Derived Imagery Using Transfer Learning

High Definition (HD) Maps are highly accurate 3D maps that contain features on or nearby the road that assist with navigation in Autonomous Vehicles (AVs). One of the main challenges when making such maps is the automatic extraction and classification of road markings from mobile mapping data. In this paper, a methodology is proposed to use transfer learning to extract and classify road markings from mobile LiDAR. The data procedure includes preprocessing, training, class extraction and accuracy assessment. Initially, point clouds were filtered and converted to intensity-based images using several grid-cell sizes. Then, it was manually annotated and split to create the training and testing datasets. The training dataset has undergone augmentation before serving as input for evaluating multiple openly available pre-trained neural network models. The models were then applied to the testing dataset and assessed based on their precision, recall, and F1 scores for extraction as well as their error rates for classification. Further processing generated classified point clouds and polygonal vector shapefiles. The results indicate that the best model is the pre-trained U-Net model trained from the intensity-based images with a 5 cm resolution among the other models and training sets that were used. It was able to achieve F1 scores that are comparable with recent work and error rates that are below 15%. However, the classification results are still around two to four times greater than those of recent work and as such, it is recommended to separate the extraction and classification procedures, having a step in between to remove misclassifications.

• 期刊

以生物序列演算法進行UAV影像幾何校正控制點匹配新型模式之探索性研究

本研究開發了一種新穎的半自動地面控制點（Ground Control Points, GCPs）匹配模型來解決UAV影像校正問題。我們使用生物序列演算法（Biological sequence algorithms）為概念來進行影像匹配程序，其概念則是透過Needleman-Wunsch algorithm（NWA）的全局特徵對齊技術，匹配兩個影像（基準影像和待校正影像）中的物件對象，在識別成功匹配的物件對象後，再利用Smith-Waterman algorithm（SWA）的局部特徵對齊技術，從匹配成功的物件對象中提取GCPs，最後，再使用多項式模型方法對於所提出GCPs進行幾何校正與價值評估。研究的案例成果顯示，除了可從本研究中所使用的影像中自動提取適當的GCPs之外，而影像進行幾何校正後，經由人工刪去殘差值大於1個單位的控制點後，剩餘控制點的RMSE（均方根誤差）值為0.52418，可證明本研究之方法未來可適用於高解析度影像之GCPs校正問題。

• 期刊

無人機遙測平台於金門野郊地區有形文化資產環境風險測圖－以陳健墓與陳禎墓為例

近年來文化資產維護保存議題已普遍受到重視，然而野郊地區有形文化資產因地形與環境影響，長久下來將對其結構體產生破壞威脅。本研究利用無人機可對小區域範圍監測之機動性，搭載多光譜感測器對金門野郊地區國定古蹟陳健墓與陳禎墓周圍地物進行資料蒐集，並提出一套環境風險評估模式。該模式考量氣象與土壤含水量作為危害度因子，以及地形坡度為脆弱度因子，將監測分析資料以四分位距統計劃分為四級距，以產製不同時期兩墓園環境風險等級圖。研究結果顯示，所產製的多時期環境風險等級圖，可細緻化呈現有形文化資產各部位承受之環境風險等級，除了作為辦理修復計畫時設置減災設施參考外，並有效指出兩墓園有形文化資產不同季節承受環境風險之差異。

• 期刊

基於整體學習演算法於航攝正射影像之物件分類探索－以海岸廢棄物為例

近年臺灣在氣候變遷及海洋垃圾成長的環境下，多樣化的海岸樣貌正遭受威脅，在海岸廢棄物的議題上刻不容緩。有鑑於當今GIS的蓬勃發展，及遙測技術能夠在短時間內擷取大範圍的量化資訊，能看到有越來越多取代傳統調查方式的實際應用。本研究屬遙測技術結合機器學習的應用，以整體學習（Ensemble learning）之模型訓練方式在海岸廢棄物上進行物件分類實作的可行性，使用隨機森林（Random Forest）演算法及極限樹（Extra Trees）演算法所建構之模型，探討其分類成效差異，將機器分類後的數據將資料空間視覺化以繪製出主題圖。研究成果可應用於海岸廢棄物相關環境保育作業上，將為一經濟且有效的解決方案。