台灣地區地層下陷問題肇始於六十年代迄今已逾三十餘載，持續下陷面積已達1,194平方公里，最大累積下陷量達到三公尺以上。而目前地層下陷地表監測所採用的傳統水準測量耗費人力、時間較多，且不易獲得連續和全面性之地層下陷資料，目前國內水利單位限於人力時間，無法針對所有監測區域每年皆施測一次。近年來由於空載光達測量技術興起，其具有短時間內獲取大區域高密度、高精度高程資料的特性，因此本研究之目的在探討如何利用空載光達測量技術快速獲取高精度之三維點雲資訊，進行大區域的地層下陷監測及其成效。 研究方法係首先將監測區內掃瞄的光達點雲資料進行網格化分群；接著，計算網格區域內所有光達點擬合平面的中心高程；然後，以人工或自動方法萃取出平坦、穩固的網格區域做為監測面；最後，進行不同時期網格監測面高程差異之統計測試分析，以求出地層下陷量。 實驗結果顯示改善點雲高程精度至5公分以內後，經由網格監測面的精度、坡度、坡向、反射強度、道路範圍等為門檻值，可萃取出80%以上正確率的穩固監測面，且其高差成果與長期監測成果的平均值差異在1.3公分至2.9公分之間，由此成果可以說明本研究成果對建立一套省時省力的監測模式，進而達到地層下陷監測自動化的目的有相當幫助。 / The issue of land subsidence in Taiwan has been concerned for over 30 years since 1970. Land subsidence area has been already over 1194 km2, the maximum amount of accumulative subsidence is more than 3 meters. The conventional leveling for the land subsidence monitoring is labor-intensive and time-consuming, so that the Water Resources Agency could not monitor all the subsidence area every year. Airborne LIDAR technology was developed in recent years, it has the characteristics of collecting 3-D point data at the high density and high elevation accuracy in short time. The purpose of this study, therefore, is to discuss how to utilize the airborne LIDAR technology to monitor the land subsidence. The proposed approach, therefore, is first to divide the collecting DSM points in the monitor area into regular grids. Secondly, all the points in the regular grids are fitted to one set planar parameters by least squares principle and the centric elevation of each grid is calculated. Third, the flatness and well-defined planar grids are selected as the monitoring surfaces with the manual or automatic method. Finally, the difference of centric elevation in each monitoring surfaces at different period is calculated and analyzed with statistical approach. This study shows that after refining the elevation accuracy of point clouds within 5 cm, our approach can extract stable monitoring surfaces by limiting planar fitting accuracy, flatness, slope, intensity, or by using road information. The extracted correct rate can be more than 80%. The discrepancy of elevation difference between this study and long-term monitoring result is between 1.3 cm and 2.9 cm. It proves the proposed approach is helpful on constructing the monitoring model in timesaving and efficient way, and our proposed approach has the potential for developing automatic land subsidence monitoring method.

地面光達系統可以快速獲取大量且高精度之點雲資料，這些點雲資料不但記錄了被掃描物體之三維資訊，還包含其色彩訊息。但因光達點雲資料量過於龐大，若要直接於電腦上展示其三維模型，必須配合有效的資料處理技術，才能迅速且即時地將資料顯示於螢幕上。 我們針對地面光達系統獲取之建物點雲，提出一套處理方法，期盼透過少數關鍵點雲，就足以表示整個建物的模型。研究流程主要分為三階段，首先採用三維網格資料結構，從地面光達系統獲取之建物點雲中，萃取出關鍵點雲，並利用三維不規則三角網建模方式，進行模型建構工作，產生建物大略模型。其次再逐點判斷是否將剩餘之點加入此模型中，持續更新模型細微之部分。最後將點雲中的色彩資訊轉成影像，敷貼在模型表面上，讓整個模型更為逼真。 我們以政大綜合大樓進行實驗，成功地減少大量冗餘的點雲資料，只需要約原始點雲的1％，就足以將綜合大樓模型建構完成。為了達到可以從不同視角即時瀏覽建物模型，我們採用虛擬實境語言(VRML)來描述處理後的三維模型，遠端使用者只需透過一般網頁瀏覽器，即可即時顯示處理過的三維建物模型。 / Ground-based LIDAR system can be used to detect the surface of the buildings on the earth. In general, it produces large amount of high-precision point cloud data. These data include not only the three-dimensional space information, but also the color information. However, the number of point cloud data is huge and is difficult to be displayed efficiently. It’s necessary to use efficient data processing techniques in order to display these point cloud data in real-time. In this research, we construct the three-dimensional building model using the key points selected from a given set of point cloud data. The major works of our scheme consists of three parts. In the first part, we extract the key points from the given point cloud data through the help of a three-dimensional grid. These key points are used to construct a primitive model of the building. Then, we checked all the remaining points and decided whether these points are essential to the final building model. Finally, we transformed the color information into images and then used the transformed images to represent generic surface material of the three-dimensional model of the building. The goal of the final step is to make the model more realistic. In the experiments, we used the twin-tower of our university as our target. We successfully reduced the required data in displaying the building model and only about one percent of the original point cloud data are used in the final model. Hence, one can see the twin-tower from various view points in real-time. In addition, we use VRML to describe our model and the users can browse the results in real-time on internet.

空載光達系統於建置國土測繪基本資料扮演關鍵角色，依國土測繪法，為確保測繪成果品質，應依測量計畫目的及作業精度需求辦理儀器校正。國土測繪中心已於102年度建置航遙測感應器系統校正作業中，提出矩形建物之平屋頂面做為空載光達系統校正之可行性，而其所稱之校正，是以點雲精度評估待校件空載光達系統所得最終成果品質，並不對儀器做任何參數改正，但其校正成果可能因不同人員操作而有差異，因此本研究嘗試建立一套空載光達點雲半自動化精度評估程序，此外探討以山形屋脊線執行點雲精度評估之可行性。 由於光達點雲為離散的三維資訊，不論是以山形屋脊線或矩形建物之平屋頂面作為標物執行點雲精度評估，均須先萃取屋頂面上之點，為避免萃取成果受雜訊影響，本研究引入粗差偵測理論，發展最小一乘法結合李德仁以後驗變方估計原理導出的選擇權迭代法(李德仁法)將非屋頂點視為粗差排除。研究中分別對矩形建物之平屋頂面及山形屋脊線進行模擬及真實資料實驗，其中山形屋脊線作為點雲精度評估之可行性實驗中發現不適合用於評估點雲精度，因此後續實驗僅以萃取矩形建物之平屋頂面點雲過程探討粗差比率對半自動化點雲精度評估程序之影響。模擬實驗成果顯示最小一乘法有助於提升李德仁法偵測粗差數量5%至10%；真實資料實驗，以含有牆面點雲的狀況為例，則有助提升5%的偵測粗差數量。本研究由逐步測試結果提出能夠適用於真實狀況的半自動化之點雲精度評估程序，即使由不同人員操作，仍能獲得一致的成果，顯示本研究半自動化精度評估程序之可信度。 / The airborne LiDAR system plays a crucial role in building land surveying data. Based on the Land Surveying and Mapping Act, to ensure the quality of surveying, instrument calibration is required. The approach proposed by National Land Surveying and Mapping Center (NLSC) in 2013 was confirmed the feasibility for airborne LiDAR system calibration using rectangular horizontal roof plane. The calibration mean to assess the final quality of airborne LiDAR system based on the assessment of the accuracy of the point cloud, and do not adjust the instrument. But the results may vary according to different operators. This study attempts to establish a semi-automatic procedure for the accuracy assessment of point clouds from airborne LiDAR system. In addition, the gable roof ridge lines is discussed for its feasibility for the accuracy assessment of point cloud. No matter that calibration is performed using rectangular horizontal roof plane or gable roof ridge line, point clouds located on roof planes need to be extracted at first. Therefore, Least Absolute Deviation (LAD) combined with the Iteration using Selected Weights (Deren Li method) is developed to exclude the non-roof points which regarded as gross errors and eliminate their influences. The simulated test and actual data test found that gable roof ridge lines are not suitable for accuracy assessment. As for the simulated test using horizontal roof planes, LAD combined with Deren Li method prompts the rate of gross error detection about 5% to 10% than that only by Deren Li method. In actual test, data contains wall points, LAD combined with Deren Li method can prompt about 5%. Meanwhile, a semi-automatic procedure for real operations is proposed by the step-by-step test. Even different operators employ this semi-automatic procedure, consistent results will be obtained and the reliability can achieve.