本研究以空載(ALS)、地載(TLS)及車載(MLS)三種光達於不同道路做資料自動化篩選的分析與探討，利用光達對物表所產生的三維坐標數據-點雲資料做道路輪廓萃取。主要以結合點雲中的三個特徵值為自動化篩選要件：(1)高程特徵，利用道路高程呈現連續平緩且與其它地物有明顯高度上之差別來篩選，其中以高程極值±15cm做為篩選門檻值；(2)強度特徵，藉由道路與標線之反射強度成對比來篩選道路點雲，其中強度閥值分別為：車載30～60、空載4～15及地載58～78。此外掃瞄同一物表時因掃瞄角度與距離上的不同，所呈現的強度值也會有所差異，故必須做強度正規化處理；(3)回波特徵，以道路地表皆為單一回波的特性來篩選。最後將所得的結果以點雲密度方式轉換匯出xyz檔，再藉 ArcGis轉成圖檔以 Canny演算法搭配閥值做邊緣萃取。

研究結果顯示，車載與地面光達因點雲密度較大，故採用0.1與0.5m的網格做分析，0.1m所能呈現的物表較明顯且提供較完整的道路屬性，在市區的5～6線道道路經單次測繪可得3～4線道；而空載光達點雲密度較小，以0.5與1m的網格做探討，1m網格較清晰但道路輪廓較簡略，主要能快速掃瞄大範圍山區並提供道路邊界。

山區道路因地形起伏大、範圍廣闊且道路組成較單純，故可採用解析力較差之空載光達，但其效率較高；然而在市區道路中新開闢道路或道路屬性測繪需要較高之分辨率與精確度，則以車載光達或地面光達較為適合。

由於光達資料昂貴，若以街道圖繪製為目標則不符經濟效益，應以其它地形或地貌監測為目的，將所得之測繪成果作為二次利用。此外本研究提出之適用時機和技術門檻，可作為類似使用之先行參考與依據。

The purpose of this study is to perform an automatic filtering schema to obtain the special features regarding road boundary by using three different kinds of LiDAR systems, ALS、TLS and MLS. The major characters utilized in LiDAR data are the three dimensional coordinate, laser intensity and character features of the return signals. The resulting LiDAR point cloud data is used as fundamental figure for the feature extraction. The combining criteria mainly to choice these three features from point cloud automatically is the technique of filtering： (1) elevation feature, the use of the road elevation showing a continuous flat surface and obviously different from the other objects in elevation, which extremes are within ± 15cm and used as the filtering threshold; (2) intensity feature, by mean of intensity contrast between which reflected from the road and from the marker to extract out the point cloud that corresponding to the road, where the intensity threshold are listed as following： MLS 30 ～ 60, ALS 4 to 15 and TLS 58 to 78 . The reflected laser intensity is influenced by the different scanning angles and distances, a normalization process is required to adjust such phenomena; (3) echo feature, the road surface are filtering by the characteristic of single echo. Finally, the chosen result been converted into point cloud density and export the xyz file, then to utilize the ArcGIS in converting image then the edge is extracted by the canny algorithms.

As the results show, the point cloud density of ALS and TLS to larger to show the object. Therefore, the data grid is degraded for analysis purpose to the interval of 0.1 and 0.5m, respectively. The 0.1 m grid could presented object more clearly and provide much more complete road attributes. It could provide 2/3 of the lanes information for urban road by single campaign. Airborne Lidar point cloud density is sparse, so the chosen data grid is 0.5 and 1m. Testing results show 1m grid provide much more clear outline but the edge of road is blurred, it is suitable in quickly scan a wider range of mountain area.

The road in the mountain is hypsography and simple in shape, so it is easier to extract the road boundary than inside the urban area. This character is suitable to use ALS with less resolution but to achieve better efficiency. Therefore in the new road detection within the urban or while the attribute of road need higher demand for resolution and accuracy of mapping, then it is better to use MLS or TLS.

As the data of LiDAR is expensive, constructing street map based on LiDAR data is not economical. It should be applied to monitor topographic or landscape for reuse. In addition, the applications of the timing and technological thresholds are suggested in this study, which can be employed or referenced for similar researches primarily.

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