臺灣博碩士論文加值系統

橋樑和涵洞統稱為排水結構（drainage structures, DS）。在全球範圍內，大多數DS在使用期間，會面對極端天氣條件下潛在的故障威脅。因此，廣域排水結構製圖是必不可少的，然而廣域DS位置通常難以取得，這使得對其檢查和按時管理更具挑戰性。與遙感資料結合的地理信息系統(Geographical Information systems, GIS)和全球導航衛星系統(Global Navigational Satellite Systems, GNSS)已被用於手動繪製DS數據。近年來，越來越多的空載光達掃描(airborne laser scanning, ALS)資料使用於DS製圖，如基於空載光達掃描的數字高程模型(DEM)和山體陰影圖。然而，目前仍未開發出統一且自動化的DS製圖方法，在過去的研究中也已展示對此方法的需求。手動方法速度較緩慢，受天氣條件和過去調查的有限研究地點的資金來源的限制。為了解決上述提及的挑戰，目前的研究主要集中在開發自動排水結構製圖算法(DS mapping algorithm, DSMA)以繪製廣域容量的DS。由於過去DS製圖成功應用於空載光達三維地形描繪，因此已分類的ALS點雲以及道路中心線被用於DSMA的開發。在使用ALS數據進行DS製圖的背景下，發現道路較相鄰的ALS地面點高，這分別對排水網絡(drainage network, DN)和隨後的DS製圖構成了潛在挑戰。因此，自動DSMA首先消除道路的ALS地面點，使用ALS地面點估計代表高速公路、私人道路、住宅街道等道路寬度的不同緩衝區值的數量，接著分別使用緩衝區值和道路中心線生成組合道路遮罩，使用組合的道路遮罩去除ALS數據中屬於道路的所有地面點。然後對剩餘的地面點進行插值以創建新開發的空載光達修正DEM(ALS-modified DEM, ALS-mDEM)，分別使用流向、流量和Strahler流序算法自動從ALS-mDEM導出排水網絡(DN)。接著通過將DN與道路中心線相交來測試幾個流序閾值以繪製候選DS。在DSMA開發的最後一步，生成並測試了不同的DS細化緩衝區，以從重複的DS記錄中清除繪製的DS，通過DS細化分析最終選擇15m的細化緩衝區。DSMA的性能在美國佛蒙特州總面積50平方公里的兩種不同地理環境下的廣域容量中進行了評估，分別包括城市站點和農村站點。

分類後的ALS點雲來自美國地質調查局(Unite State Geological Survey, USGS)，而美國聯邦公路管理局(Federal Highway Administration, FHWA)的道路功能分類方案來自佛蒙特州的公共數據平台。非FHWA道路由FHWA管轄範圍之外的私人道路和住宅街道組成，即無法從公共數據平台獲取，因此，使用土地利用土地覆蓋(land use land cover, LULC)的不透水表面(impervious surface, ISA)分別自動繪製地圖。最後，從佛蒙特州交通局收集包括橋樑和涵洞的DS數據集與FHWA道路資料，DS數據集與非FHWA道路分別從Google Earth Street View (GE-SV)影像數化。基準DS數據集用於評估DSMA方法繪製DS的位置和預測精度，根據基準DS和對應的繪製DS之間的對應關係，計算歐幾里得距離以分別評估繪製之DS與基準DS數據集的位置精度。城市站點和農村站點的平均定位精度為13.5m及15.8m。並將繪製的DS在F1 scores方面的預測精度與FHWA和非FHWA道路分別計算，對於FHWA管理的道路，城市站點和農村站點的F1 score分別為0.87和0.94；對於非FHWA道路，城市站點和農村站點的F1 score分別為0.72和0.74。
ALS的地面點非常適合用於創建高解析度數字高程模型(High-resolution digital elevation models, HR-DEMs)。然而，在水文和地貌學的應用環境中，HR-DEM需要經過後處理，通過將繪製的DS合併到HR-DEM中對DEM進行涵洞修正，以分別創建適用於水文和地貌調查的涵洞修正DEM。然而，在缺乏廣域容量的DS數據集的情況下，建立涵洞修正DEM變得具有挑戰性。相反，當DS數據在某種程度上無法解決問題時，破壞算法(breach algorithm, BA)方法是獲取涵洞修正DEM的標準程序。

在第二部分中，本研究評估了來自DSMA的廣域容量中DSs數據的可用性，以處理美國佛蒙特州36平方公里的涵洞修正DEM。基準DS數據集用作評估DSMA方法性能的標準參考。此外，亦比較DSMA方法的性能與破壞算法(BA)方法，這是在DSs數據不可用時獲得涵洞修正DEM的標準程序。並開發了一個新的自動化方法框架，以使用DSMA方法繪製的DS生成涵洞修正DEM，並對DSMA方法與BA方法相比的性能分別在生產涵洞修正DEM的背景下進行評估，分析從DSMA和BA方法的涵洞修正DEM中發現的DS，以量化這兩種方法的性能。為了評估這兩種方法的性能，從涵洞修正DEM中發現的DS被分別分類為代表正確解的真陽性 (true positive, TP)、代表錯誤解的假陽性 (false positive, FP) 和兩者都未找到的假陰性 (false-negative, FN) 解，藉由使用來自DSMA和BA方法的TP、FP和FN，發現了分類錯誤，即假陽性率（false positive rate, FPR）和遺漏錯誤，即假陰性率（false-negative rate, FNR）。由評估矩陣顯示出，新開發的基於DSMA的DS數據的方法與FHWA之FPR分別為0.05，與非FHWA道路的FPR分別為0.12；在FNR方面，DSMA方法與FHWA為0.07，與非FHWA道路為0.38。與DSMA相比，BA方法與FHWA的FPR分別為0.28，與非FHWA道路的FPR分別為0.62；在FNR方面，BA方法與FHWA為0.32，與非FHWA道路為0.61。基於FNR和FPR的評估矩陣，DSMA方法已被證明分別比BA方法更準確，因此，與BA方法相比，使用DSMA生成涵洞修正DEM的公式化框架為相對穩健的。

Bridges and culverts are collectively known as drainage structures (DS). Globally, most of the DSs are completing their designed age and posing a potential threat of failure under extreme weather conditions. Therefore, wide-area drainage structure (DS) mapping is essential, though wide-area DS locations are frequently unavailable making their inspection and management on-time more challenging. Remotely sensed data along with Geographical Information systems (GIS), and Global Navigational Satellite Systems (GNSS) have been reported for mapping DS data through manual methods. In recent times, the increased use of airborne laser scanning (ALS) data has been witnessed for DSs mapping using ALS-based digital elevation models (DEMs) and hillshade images, respectively. However, a unified, automated algorithmic-based DS mapping solution is not developed and yet the need for such a method is frequently reported in the past. The manual methods were reported to be slower, constrained by weather conditions and funding sources for limited study sites as investigated in the past. To address the aforementioned challenges in the context of DS mapping, the present research primarily focusing the development of an automated DS mapping algorithm (DSMA) to map DSs in wide-area capacity. The ALS three-dimensional (3D) portrayal of topography owing to the success of DS mapping in the past, therefore, classified ALS point clouds along with road centerlines were used for the development of DSMA. In the context of DS mapping using ALS data, the roads were found to be elevated than neighboring ALS ground points posing a potential challenge in drainage network (DN) and afterward DS mapping, respectively. Thus, automated DSMA initiates by eliminating ALS ground points of roads first. The number of different buffers values representing the road widths of highways, freeways, private roads, residential streets, etc., were estimated using the ALS ground points. Then a combined road mask is generated using buffers values and road centerlines, respectively. Then the combined road mask is used to remove all the ground points belonging to roads in the ALS data. The remaining ground points are then interpolated to create a newly developed ALS-modified DEM (ALS-mDEM). A drainage network (DN) is then derived from the ALS-mDEM automatically using the flow-direction, flow-accumulation, and Strahler stream-order algorithms, respectively. Several stream order thresholds were then tested to map candidate DSs by intersecting the DN with the road centerlines. In the final step of the DSMA development, different DS refinement buffers were generated and tested to clean the mapped DSs from duplicate DS records. A refinement buffer of 15 m is then finally selected through DS refinement analysis. The performance of DSMA was assessed in wide-area capacity under two different geographical settings for a total area of 50 km² in Vermont, USA, including an urban site and a rural site, respectively.
The classified ALS point clouds were acquired from Unite State Geological Survey (USGS), while the road functional classification scheme of the Federal Highway Administration (FHWA), and was obtained from a public data portal of Vermont. The non-FHWA roads are comprised of private roads and residential streets that were out of the jurisdiction of FHWA i.e., unavailable from a public data portal, therefore, were automatically mapped using impervious surface (ISA) of land use land cover (LULC), respectively. Finally, the DS dataset comprised of bridges and culverts was gathered from the Vermont agency of transportation along with FHWA roads, and the DS dataset along with non-FHWA roads was digitized from Google Earth Street View (GE-SV) images, respectively. The benchmark DS dataset was used to assess the positional and prediction accuracies of the mapped DS of the DSMA method. Based on the one-to-one correspondence between a benchmark DS and corresponding a mapped DS, the Euclidean distances were computed to assess the positional accuracies of the mapped DS compared to the benchmark DS dataset, respectively. The mean positional accuracy for the urban site and rural sites were 13.5 m and 15.8 m were reported for both geographical settings. The prediction accuracies of the mapped DS in terms of F1 scores were calculated along with FHWA and non-FHWA roads separately. The F1 scores were 0.87 and 0.94 for the urban site and rural site respectively, for the FHWA administrated roads. The F1 scores were found to be 0.72 and 0.74 for the urban site and rural site respectively, for the non-FHWA roads.
ALS ground points are in great use to create High-resolution digital elevation models (HR-DEMs). However, in the application setting of hydrology and geomorphology, HR-DEMs require post-processing to create culvert-modified DEMs suitable for hydrological and geomorphological investigations, respectively. A culvert-modified DEM is processed by incorporating mapped DSs in an HR-DEM at the post-processing stage. Nevertheless, in the absence of a DS dataset in wide-area capacity, the creation of culvert-modified DEMs becomes challenging. Instead, the breach algorithm (BA) method is a standard procedure to obtain culvert-modified DEM when DSs data is not available to solve the problem to some extent.
In the second part, the present research assesses the availability of DSs data in wide-area capacity originating from the DSMA to process a culvert-modified DEM comprised of an area of 36 km² in Vermont, USA. Benchmark DS dataset is used as a standard reference to assess the performance of the DSMA method. Furthermore, the research is extended to compare the DSMA method performance with the Breaching Algorithm (BA) method, which is a standard procedure to obtain a culvert-modified DEM when DSs data is unavailable. A new and automated methodological framework is then developed to generate culvert-modified DEM using mapped DS of the DSMA method. Furthermore, the performance of the DSMA method compared to the BA method is assessed in the context of producing culvert-modified DEMs, respectively. The DS found from the culvert-modified DEMs of the DSMA and BA methods were analyzed to quantify the performance of both methods. To assess the performance of both methods, DS found from culvert-modified DEM were classified as true positive (TP) for the correct solution, false positive (FP) for the wrong solution, and false-negative (FN) no solution found by both methods, respectively. Using TP, FP, and FN originating from the DSMA and BA method, the commission error i.e. false positive rate (FPR), and omission error i.e., false-negative rate (FNR) were found. The reported evaluation matrices indicate that the newly developed DSMA-based DS data showed an FPR of 0.05 along with FHWA, and 0.12 with non-FHWA roads, respectively. In terms of FNR, The DSMA showed 0.07 along with FHWA, and 0.38 along with non-FHWA roads respectively. Compared to the DSMA, The BA method showed an FPR of 0.28 along with FHWA, and 0.62 along with non-FHWA roads, respectively. In terms of FNR, the FNR the BA method scores 0.32 along with FHWA, and 0.61 with non-FHWA roads, respectively. Based on the evaluation matrices of FNR, and FPR, the DSMA method has proven to be more accurate than the BA method, respectively. Thus, the formulated framework for producing culvert-modified DEM using DSMA was robust compared to the BA method, respectively.

Contents
Abstract i
Acknowledgement iv
Contents v
List of Tables viii
List of Figures ix
List of Acronyms xiv
Chapter 1 Introduction 1
1.1. Global Context 4
1.2. Towards DS Mapping. 5
1.2.1. Global Navigational Satellite Systems (GNSS). 5
2.2.2. Optical Remote Sensing. 5
1.3. Major Contributions. 7
1.3.1. DS Mapping using ALS Data. 7
1.3.2. Processing culvert Modified DEM using ALS Data 9
1.4. Organization 9
Chapter 2 Development of Drainage Structure Mapping Algorithm (DSMA) based on ALS Point Clouds and Road Centerline Data 11
2.1. Introduction 11
2.1.1. Assumptions 13
2.2. Study Sites and Datasets. 14
2.2.1. Study Sites. 14
2.2.2. ALS Point Clouds. 15
2.2.3. Centerlines of FHWA Roads. 17
2.2.4. Centerlines of Non-FHWA Roads. 18
2.2.5. Benchmark DS Dataset 18
2.2.6. Orthophotos 21
2.3. Methodology 22
2.3.1. Data Preparation 23
2.3.1.1. ALS Data Tiling 23
2.3.1.2. Creating Combined Mask of FHWA and Non-FHWA Roads 24
2.3.2. ALS-mDEM Production 26
2.3.3. Candidate DS Mapping 28
2.3.4. Candidate DS Refinement 29
2.3.5. Evaluation Metrics 33
2.3.5.1. Positional Accuracy 33
2.3.5.2. Prediction Accuracy 34
2.4. Results 34
2.4.1. Positional Accuracy 34
2.4.2. Prediction Accuracy 36
2.4.2.1. False Negative Cases 39
2.4.2.2. False Positive Cases 43
2.5. Discussions 46
2.5.1. DSMA Standard Operating Procedure (SOP) 49
Chapter 3 Culvert-Modified Digital Elevation Models Obtained using DS Mapping Algorithm Compared to Breaching Algorithm: A Comparative Assessment based on ALS Data 51
3.1. Introduction 51
3.2. Study Area and Datasets 56
3.2.1. Study Area 56
3.2.2. ALS Dataset 57
3.2.3. Benchmark DS Dataset Along with FHWA and Non-FHWA Roads 58
3.3. Methods 58
3.3.1. Preprocessing 60
3.3.2. Breaching Algorithm (BA) Method 60
3.3.3. DS Mapping Algorithm (DSMA) Method 63
3.3.4. Evaluation Metrics 67
3.4. Results 68
3.4.1. Evaluation of the BA Method 68
3.4.2. The Evaluation of the DSMA Method Based on HR-DEM and Point Clouds 69
3.4.3. Comparative Assessment of BA and DSMA Methods 70
3.4.3.1. Case-1: Missing Source of Candidate Sink 71
3.4.3.2. Case-2: Source and Candidate Sink on the Same Side of Road 73
3.4.3.3. Case-3: Wrong Solutions within the Source and Candidate Sink 74
3.5. Discussion 76
Chapter 4 79
4.1. Conclusion 79
4.2. Future Work 81
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