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Les modèles numériques de terrain, cas particulier de modèles numériques de surfaces, n'ont pas la même erreur quadratique moyenne en planimétrie qu'en altimétrie. Différentes solutions ont été envisagées pour déterminer séparément l'erreur en altimétrie et l'erreur planimétrique, disposant, bien entendu, d'un modèle numérique plus précis comme référence. La démarche envisagée consiste à déterminer les paramètres des ellipsoïdes d'erreur, centrées dans la surface de référence. Dans un premier temps, l'étude a été limitée aux profils de référence avec l'ellipse d'erreur correspondante. Les paramètres de cette ellipse sont déterminés à partir des distances qui séparent les tangentes à l'ellipse du centre de cette même ellipse. Remarquons que cette distance est la moyenne quadratique des distances qui séparent le profil de référence des points du modèle numérique à évaluer, c'est à dire la racine de la variance marginale dans la direction normale à la tangente. Nous généralisons à l'ellipsoïde de révolution. C'est le cas ou l'erreur planimétrique est la même dans toutes les directions du plan horizontal (ce n'est pas le cas des MNT obtenus, par exemple, par interférométrie radar). Dans ce cas nous montrons que le problème de simulation se réduit à l'ellipse génératrice et la pente du profil correspondant à la droite de pente maximale du plan appartenant à la surface de référence. Finalement, pour évaluer les trois paramètres d'un ellipsoïde, cas où les erreurs dans les directions des trois axes sont différentes (MNT obtenus par Interférométrie SAR), la quantité des points nécessaires pour la simulation doit être importante et la surface tr ès accidentée. Le cas échéant, il est difficile d'estimer les erreurs en x et en y. Néanmoins, nous avons remarqué, qu'il s'agisse de l'ellipsoïde de révolution ou non, que dans tous les cas, l'estimation de l'erreur en z (altimétrie) donne des résultats tout à fait satisfaisants. / A Digital Surface Model (DSM) is a numerical surface model which is formed by a set of points, arranged as a grid, to study some physical surface, Digital Elevation Models (DEM), or other possible applications, such as a face, or some anatomical organ, etc. The study of the precision of these models, which is of particular interest for DEMs, has been the object of several studies in the last decades. The measurement of the precision of a DSM model, in relation to another model of the same physical surface, consists in estimating the expectancy of the squares of differences between pairs of points, called homologous points, one in each model which corresponds to the same feature of the physical surface. But these pairs are not easily discernable, the grids may not be coincident, and the differences between the homologous points, corresponding to benchmarks in the physical surface, might be subject to special conditions such as more careful measurements than on ordinary points, which imply a different precision. The generally used procedure to avoid these inconveniences has been to use the squares of vertical distances between the models, which only address the vertical component of the error, thus giving a biased estimate when the surface is not horizontal. The Perpendicular Distance Evaluation Method (PDEM) which avoids this bias, provides estimates for vertical and horizontal components of errors, and is thus a useful tool for detection of discrepancies in Digital Surface Models (DSM) like DEMs. The solution includes a special reference to the simplification which arises when the error does not vary in all horizontal directions. The PDEM is also assessed with DEM's obtained by means of the Interferometry SAR Technique

Modelling solar radiation data at a high spatiotemporal resolution for an urban environment can inform many different applications related to climate action, such as urban agriculture, forest, building, and renewable energy studies. However, the complexity of urban form, vastness of city-wide coverage, and general dearth of climatological information pose unique challenges doing so. To address some climate action goals related to reducing building emissions in the City of Victoria, British Columbia, Canada, applied geomatics and climatology were used to model solar radiation data suitable for informing renewable energy feasibility studies, including photovoltaic system sizing, costing, carbon offsets, and financial payback. The research presents a comprehensive review of solar radiation attenuates, as well as methods of accounting for them, specifically in urban environments. A novel methodology is derived from the review and integrates existing models, data, and tools – those typically available to a local government. Using Light Detection and Ranging (LiDAR), a solar climatology, Esri’s ArcGIS Solar Analyst tool, and Python scripting, daily insolation (kWh/m2) maps are produced for the city of Victoria. Particular attention is paid to the derivation of daily diffuse fraction from atmospheric clearness indices, as well as LiDAR classification and generation of a Digital Surface Model (DSM). Novel and significant improvements in computation time are realized through parallel processing. Model results exhibit strong correlation with empirical data and support the use of Solar Analyst for urban solar assessments when great care is taken to accurately and consistently represent model inputs and outputs integrated in a methodological approach. / Graduate

點雲（point cloud）為以大量三維坐標描述地表實際情形的資料形式，其中包含其三維坐標及相關屬性。通常點雲資料取得方式為光達測量，其以單一波段雷射光束掃描獲取資料，以光達獲取點雲，常面臨掃描時間差、缺乏多波段資訊、可靠邊緣線及角點資訊、大量離散點雲又缺乏語意資訊（semantic information）難以直接判讀及缺乏多餘觀測量等問題。 攝影測量藉由感測反射自太陽光或地物本身放射之能量，可記錄為二維多光譜影像，透過地物在不同光譜範圍表現之特性，可輔助分類，改善分類成果。若匹配多張高重疊率的多波段影像，可以獲取包含多波段資訊且位於明顯特徵點上的點雲，提供光達以外的點雲資料來源。 傳統空中三角測量平差解算地物點坐標及產製數值表面模型（Digital Surface Model, DSM）時，多採用可見光影像為主；而目前常見之高空間解析度數值航照影像，除了記錄可見光波段之外，亦可蒐集近紅外光波段影像。但較少採用近紅外光波段影像，以求解地物點坐標及建立DSM。 因此本研究利用多波段影像所蘊含的豐富光譜資訊，以取像方式簡易及低限制條件的近景攝影測量方式，匹配多張可見光、近紅外光及紅外彩色影像，分別建立可見光、近紅外光及紅外彩色之DSM，其目的在於探討加入近紅外光波段後，所產生的近紅外光及紅外彩色DSM，和可見光DSM之異同；並比較該DSM是否更能突顯植被區。 研究顯示，以可見光點雲為檢核資料，計算近紅外光與紅外彩色點雲的均方根誤差為其距離門檻值之相對檢核方法，可獲得約21%的點雲增加率；然而使用近紅外光或紅外彩色影像，即使能增加點雲資料量，但對於增加可見光影像未能匹配的資料方面，其效果仍屬有限。 / Point cloud represents the surface as mass 3D coordinates and attributes. Generally, these data are usually collected by LIDAR (LIght Detection And Ranging), which acquires data through single band laser scanning. But the data collected by LIDAR could face problems, such as scanning process is not instantaneous, lack of multispectral information, breaklines, corners, semantic information and redundancies. However, photogrammetry record the electromagnetic energy reflected or emitted from the surface as 2D multispectral images, via ground features with different characteristics differ in spectrum, it can be classified more efficiently and precisely. By matching multiple high overlapping multispectral images, point cloud including multispectral information and locating on obvious feature points can be acquired. This provides another point cloud source aparting from LIDAR. In most studies, visible light (VIS) images are used primarily, while calculating ground point coordinates and generating digital surface models （DSM） through aerotriangulation. Although nowadays, high spatial resolution digital aerial images can acquire not only VIS channel, but also near infrared (NIR) channel as well. But there is lack of research doing the former procedures by using NIR images. Therefore, this research focuses on the rich spectral information in multispectral images, by using easy image collection and low restriction close range photogrammetry method. It matches several VIS, NIR and color infrared (CIR) images, and generate DSMs respectively. The purpose is to analyze the difference between VIS, NIR and CIR data sets, and whether it can emphasize the vegetation area, after adding NIR channel in DSM generation. The result shows that by using relative check points between NIR, CIR data with VIS one. First, VIS point cloud was set as check point data, then, the RMSE (Root Mean Square Error) of NIR and CIR point cloud was calculated as distance threshold. Its data increment is 21% ca. However, the point cloud data amount can be increased, by matching NIR and CIR images. But the effect of increasing data, which was not being matched from VIS images are limited.

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Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2016-12-27T11:27:00Z No. of bitstreams: 2 Dissertação - Avilmar Antonio Rodrigues - 2016.pdf: 13580117 bytes, checksum: 2b78a395b4bd955f8d72e83399bcc578 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-12-27T11:27:20Z (GMT) No. of bitstreams: 2 Dissertação - Avilmar Antonio Rodrigues - 2016.pdf: 13580117 bytes, checksum: 2b78a395b4bd955f8d72e83399bcc578 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-12-27T11:27:20Z (GMT). No. of bitstreams: 2 Dissertação - Avilmar Antonio Rodrigues - 2016.pdf: 13580117 bytes, checksum: 2b78a395b4bd955f8d72e83399bcc578 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-11-25 / This research aimed to evaluate the use of Unmanned Aerial Vehicle (UAV) as a platform for taking aerial photographs for mapping erosion planialtimetric located in urban areas. In addition, we evaluated the need to use or not to field control points for the generation of Digital Surface Model (DSM) and ortomosaico as tools to evaluate the erosive process. Despite the wide variation in attitude of aerial photographs that make up the aerophotogrammetric block arising from the instability of the UAV, it was possible to generate the MDS and ortomosaico with or without control points. This research was conducted in two urban erosions located in Goiania in Sector Fonte Nova in the stream of grass and the other in Silvânia called foot-washing. Whole generation of MDS, Digital Surface Model (MDT) and ortomosaico were performed in Agisoft PhotoScan program in semi-automatic processing, if used control points, or automatic without control points. The ortomosaicos generated without control points presented rotation, translation and scale of different generated with support. In addition, MDS generated without control points showed elevation or lowering of the reference surface with respect to the generated control, it is emphasized that these discrepancies are not constant. When performing automatic conversion of MDS to MDT, it was realized that the program was not able to eliminate the shrub vegetation located within the erosion. The vegetation or tree, shrub or undergrowth (grass) prevents proper limitation of erosion to the volume calculation. But unlike the MDS generated between two distinct epochs identifies the changes in the interval of time in areas without vegetation. The use of control points was essential to ensure the orientation, scale and the reference plane in the products generated from aerial photographs and thus evaluate the changes. Anyway, the UAV can be used as a platform for taking aerial photographs for generating cartographic products that enable the mapping and evaluation of erosions. / Esta pesquisa teve por objetivo avaliar a utilização do Veículo Aéreo Não Tripulado (VANT) como plataforma para a tomada de fotografias aéreas para o mapeamento planialtimétrico de erosão situada em zona urbana. Além disso, analisou-se a necessidade de utilização ou não de pontos de controle de campo para a geração de Modelo Digital de Superfície (MDS) e ortomosaico como instrumentos para examinar o processo erosivo. Apesar da grande variação da atitude da aeronave durante a obtenção das fotografias aéreas que compõem o bloco aerofotogramétrico, foi possível gerar o MDS e o ortomosaico com ou sem pontos de controle. Este estudo foi realizado em duas erosões urbanas, uma situada em Goiânia-GO, no Setor Fonte Nova/Córrego do Capim, e a outra em Silvânia-GO, denominada de Lava-Pés. Toda a geração dos MDS, Modelo Digital de Terreno (MDT) e ortomosaico foram realizados no programa Agisoft PhotoScan, em processamento semiautomático (i.e., com pontos de controle) e automático (i.e., sem pontos de controle). Os ortomosaicos gerados sem pontos de controle apresentaram rotação, translação e escala diferente dos gerados com apoio. Ademais, os MDS gerados sem pontos de controle apresentaram elevação ou rebaixamento da superfície de referência em relação aos gerados com controle. Ressalta-se, ainda, que essas discrepâncias não foram constantes. Ao realizar a conversão automática do MDS para o MDT, percebeu-se que o programa não foi capaz de eliminar a vegetação arbustiva localizada no interior da erosão. As vegetações arbórea, arbustiva ou rasteira (gramíneas) impedem a correta delimitação da erosão para o cálculo do volume. Porém, a diferença dos MDS gerados entre duas épocas distintas propicia identificar as alterações ocorridas nesse intervalo de tempo nas regiões sem cobertura vegetal. O uso de pontos de controle foi essencial para garantir a orientação, a escala e o plano de referência nos produtos gerados a partir das fotografias aéreas e, assim, avaliar as modificações da erosão. Por fim, o VANT pode ser utilizado como plataforma para a tomada de fotografias aéreas para gerar produtos cartográficos que possibilitem o mapeamento e as avaliações das erosões, sobretudo em áreas urbanas.

This thesis is about using airborne laser scanning data for making maps for Orienteering. In this thesis were used altimetry data DMR 4G and DMR 5G. These data are provided by ČUZK. The control measuring was made in choosen area to verify the accuracy. In this thesis is described procedure of data collection and their treatment. The results of this thesis are two illustrations of the orienteering maps.

Building information extraction and reconstruction from satellite images is an essential task for many applications related to 3D city modeling, planning, disaster management, navigation, and decision-making. Building information can be obtained and interpreted from several data, like terrestrial measurements, airplane surveys, and space-borne imagery. However, the latter acquisition method outperforms the others in terms of cost and worldwide coverage: Space-borne platforms can provide imagery of remote places, which are inaccessible to other missions, at any time. Because the manual interpretation of high-resolution satellite image is tedious and time consuming, its automatic analysis continues to be an intense field of research. At times however, it is difficult to understand complex scenes with dense placement of buildings, where parts of buildings may be occluded by vegetation or other surrounding constructions, making their extraction or reconstruction even more difficult. Incorporation of several data sources representing different modalities may facilitate the problem. The goal of this dissertation is to integrate multiple high-resolution remote sensing data sources for automatic satellite imagery interpretation with emphasis on building information extraction and refinement, which challenges are addressed in the following: Building footprint extraction from Very High-Resolution (VHR) satellite images is an important but highly challenging task, due to the large diversity of building appearances and relatively low spatial resolution of satellite data compared to airborne data. Many algorithms are built on spectral-based or appearance-based criteria from single or fused data sources, to perform the building footprint extraction. The input features for these algorithms are usually manually extracted, which limits their accuracy. Based on the advantages of recently developed Fully Convolutional Networks (FCNs), i.e., the automatic extraction of relevant features and dense classification of images, an end-to-end framework is proposed which effectively combines the spectral and height information from red, green, and blue (RGB), pan-chromatic (PAN), and normalized Digital Surface Model (nDSM) image data and automatically generates a full resolution binary building mask. The proposed architecture consists of three parallel networks merged at a late stage, which helps in propagating fine detailed information from earlier layers to higher levels, in order to produce an output with high-quality building outlines. The performance of the model is examined on new unseen data to demonstrate its generalization capacity. The availability of detailed Digital Surface Models (DSMs) generated by dense matching and representing the elevation surface of the Earth can improve the analysis and interpretation of complex urban scenarios. The generation of DSMs from VHR optical stereo satellite imagery leads to high-resolution DSMs which often suffer from mismatches, missing values, or blunders, resulting in coarse building shape representation. To overcome these problems, a methodology based on conditional Generative Adversarial Network (cGAN) is developed for generating a good-quality Level of Detail (LoD) 2 like DSM with enhanced 3D object shapes directly from the low-quality photogrammetric half-meter resolution satellite DSM input. Various deep learning applications benefit from multi-task learning with multiple regression and classification objectives by taking advantage of the similarities between individual tasks. Therefore, an observation of such influences for important remote sensing applications such as realistic elevation model generation and roof type classification from stereo half-meter resolution satellite DSMs, is demonstrated in this work. Recently published deep learning architectures for both tasks are investigated and a new end-to-end cGAN-based network is developed, which combines different models that provide the best results for their individual tasks. To benefit from information provided by multiple data sources, a different cGAN-based work-flow is proposed where the generative part consists of two encoders and a common decoder which blends the intensity and height information within one network for the DSM refinement task. The inputs to the introduced network are single-channel photogrammetric DSMs with continuous values and pan-chromatic half-meter resolution satellite images. Information fusion from different modalities helps in propagating fine details, completes inaccurate or missing 3D information about building forms, and improves the building boundaries, making them more rectilinear. Lastly, additional comparison between the proposed methodologies for DSM enhancements is made to discuss and verify the most beneficial work-flow and applicability of the resulting DSMs for different remote sensing approaches.

Automatic three-dimensional (3D) building model reconstruction using remote sensing data is crucial in applications which require large-scale and frequent building model updates, such as disaster monitoring and urban management, to avoid huge manual efforts and costs. Recent advances in the availability of very high-resolution satellite data together with efficient data acquisition and large area coverage have led to an upward trend in their applications for 3D building model reconstructions. In this dissertation, a novel multistage hybrid automatic 3D building model reconstruction approach is proposed which reconstructs building models in level of details 2 (LOD2) based on digital surface model (DSM) data generated from the very high-resolution stereo imagery of the WorldView-2 satellite. This approach uses DSM data in combination with orthorectified panchromatic (PAN) and pan-sharpened data of multispectral satellite imagery to overcome the drawbacks of DSM data, such as blurred building boundaries, rough building shapes unwanted failures in the roof geometries. In the first stage, the rough building boundaries in the DSM-based building masks are refined by classifying the geometrical features of the corresponding PAN images. The refined boundaries are then simplified in the second stage through a parameterization procedure which represents the boundaries by a set of line segments. The main orientations of buildings are then determined, and the line segments are regularized accordingly. The regularized line segments are then connected to each other based on a rule-based method to form polygonal building boundaries. In the third stage, a novel technique is proposed to decompose the building polygons into a number of rectangles under the assumption that buildings are usually composed of rectangular structures. In the fourth stage, a roof model library is defined, which includes flat, gable, half-hip, hip, pyramid and mansard roofs. These primitive roof types are then assigned to the rectangles based on a deep learning-based classification method. In the fifth stage, a novel approach is developed to reconstruct watertight parameterized 3D building models based on the results of the previous stages and normalized DSM (nDSM) of satellite imagery. In the final stage, a novel approach is proposed to optimize building parameters based on an exhaustive search, so that the two-dimensional (2D) distance between the 3D building models and the building boundaries (obtained from building masks and PAN image) as well as the 3D normal distance between the 3D building models and the 3D point clouds (obtained from nDSM) are minimized. Different parts of the building blocks are then merged through a newly proposed intersection and merging process. All corresponding experiments were conducted on four areas of the city of Munich including 208 buildings and the results were evaluated qualitatively and quantitatively. According to the results, the proposed approach could accurately reconstruct 3D models of buildings, even the complex ones with several inner yards and multiple orientations. Furthermore, the proposed approach provided a high level of automation by the limited number of primitive roof model types required and by performing automatic parameter initialization. In addition, the proposed boundary refinement method improved the DSM-based building masks specified by 8 % in area accuracy. Furthermore, the ridge line directions and roof types were detected accurately for most of the buildings. The combination of the first three stages improved the accuracy of the building boundaries by 70 % in comparison to using line segments extracted from building masks without refinement. Moreover, the proposed optimization approach could achieve in most cases the best combinations of 2D and 3D geometrical parameters of roof models. Finally, the intersection and merging process could successfully merge different parts of the complex building models.

<p>Emerging mobile mapping systems include a wide range of platforms, for instance, manned aircraft, unmanned aerial vehicles (UAV), terrestrial systems like trucks, tractors, robots, and backpacks, that can carry multiple sensors including LiDAR scanners, cameras, and georeferencing units. Such systems can maneuver in the field to quickly collect high-resolution data, capturing detailed information over an area of interest. With the increased volume and distinct characteristics of the data collected, practical quality control procedures that assess the agreement within/among datasets acquired by various sensors/systems at different times are crucial for accurate, robust interpretation. Moreover, the ability to derive semantic information from acquired data is the key to leveraging the complementary information captured by mobile mapping systems for diverse applications. This dissertation addresses these challenges for different systems (airborne and terrestrial), environments (urban and rural), and applications (agriculture, archaeology, hydraulics/hydrology, and transportation).</p> <p>In this dissertation, quality control procedures that utilize features automatically identified and extracted from acquired data are developed to evaluate the relative accuracy between multiple datasets. The proposed procedures do not rely on manually deployed ground control points or targets and can handle challenging environments such as coastal areas or agricultural fields. Moreover, considering the varying characteristics of acquired data, this dissertation improves several data processing/analysis techniques essential for meeting the needs of various applications. An existing ground filtering algorithm is modified to deal with variation in point density; digital surface model (DSM) smoothing and seamline control techniques are proposed for improving the orthophoto quality in agricultural fields. Finally, this dissertation derives semantic information for diverse applications, including 1) shoreline retreat quantification, 2) automated row/alley detection for plant phenotyping, 3) enhancement of orthophoto quality for tassel/panicle detection, and 4) point cloud semantic segmentation for mapping transportation corridors. The proposed approaches are tested using multiple datasets from UAV and wheel-based mobile mapping systems. Experimental results verify that the proposed approaches can effectively assess the data quality and provide reliable interpretation. This dissertation highlights the potential of modern mobile mapping systems to map challenging environments for a variety of applications.</p>