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Analiza możliwości wykorzystania danych z lotniczego skaningu laserowego do opracowywania trójwymiarowych modeli miast

Fryśkowska A.

Biuletyn Wojskowej Akademii Technicznej

2011

Vol. 60, nr 3

261-276

Obecnie ponad połowa populacji ludzi na świecie żyje na terenach zurbanizowanych. Problem wizualizacji obszarów zurbanizowanych za pomocą danych fotogrametrycznych pojawił się już na początku tego wieku. Trójwymiarowe modele miast są opracowywane dla większych miast już od kilku lat i obecnie znajdują szerokie zastosowanie w wielu dziedzinach nauki i gospodarki. Między innymi: w planowaniu przestrzennym i urbanistycznym, nawigacji samochodowej oraz systemach informacji geograficznej. Istnieje wiele metod wykonywania trójwymiarowych modeli miast. Najważniejsze z nich to opracowanie modeli ze zdjęć lotniczych i satelitarnych na drodze cyfrowej korelacji obrazów, a w ostatnich latach równie popularną metodą stało się generowanie Numerycznych Modeli Pokrycia Terenu (NMPT) z danych z lotniczego skaningu laserowego. Dane takie umożliwiają wykonanie NMPT, a następnie na drodze filtracji tych danych opracowuje się modele kolejno najpierw wykrywając budynek, ekstrahując jego krawędzie i rekonstruując geometrię. W artykule przedstawiona zostanie analiza możliwości wykorzystania lotniczego skaningu laserowego do opracowywania modeli 3D miast oraz metody filtracji chmury punktów. Poruszone zostaną kwestie związane z charakterem danych z Lotniczego Skaningu Laserowego (LSL) oraz przeanalizowane szczegółowe wytyczne dotyczące modelowania terenów zurbanizowanych - czyli standard CityGML opracowany przez Open Geospatial Consortium, uwzględniając generalizację tych modeli.

Nowadays, more than 50% of human population lives in the urban areas, therefore there is an increasing demand for 3D urban modelling. The issue of visualization of cities have appeared at the beginning of this century. The three-dimensional city models exist since a few years and currently this building information is extremely important for many applications such as urban planning, telecommunication, navigation, geographic information systems or environment monitoring etc. There are many methods of 3D city models generation. The most important are: models generation on the basis of aerial and satellite imagery (automatic image correlation) and in the past a few years also very popular method was aerial laser scanning, commonly named LIDAR. It enables acquiring data to generate Digital Surface Models. This DSMs have to be filtrated and then from this data we detect buildings, extract them and as the last phase - there is a building reconstruction by boundary extraction. This paper presents the analysis of using LIDAR data to accurate building detection and extraction for the use of 3D city modelling and it also reviews methods of LIDAR point cloud filtration and methods of 3D city modelling from aerial laser scanning systems. I will describe also some analysis connected with CityGML - the standard created by Open Geospatial Consortium. CityGML is a common semantic information model for the representation of 3D urban objects that can be shared over different applications, characterized by generalization - different accuracies and minimal dimensions of objects.

Wymagania wobec danych źródłowych dla generowania true-ortho

Kurczyński Z., Preuss R.

Archiwum Fotogrametrii, Kartografii i Teledetekcji

2009

Vol. 19

229--239

Ortofotomapa jest obecnie najbardziej popularnym produktem kartograficznym. W obszarze zabudowanym obrazy budynków są jednak przesunięte zgodnie z rzutem środkowym, a część terenu jest zakryta (tzw. „martwe pola”). Tej wady nie ma „prawdziwe” orto (true-ortho). Do jego wykonania konieczny jest jednak numeryczny model pokrycia terenu (NMPT) z przestrzennymi modelami budynków. W artykule podjęto dyskusję uwarunkowań technicznych generowania trueortho. Rozważane są szczególne wymagania do wykonawstwa zdjęć lotniczych, generowania brył budynków z ręcznej stereodigitalizacji modelu zbudowanego ze zdjęć, z automatycznego dopasowania obrazów, oraz danych skaningu laserowego (LIDAR). Badany jest wpływ danych źródłowych na jakość wynikowego true-ortho, oraz koszt jego wytworzenia. Prezentowane są wstępne wyniki. Prace są kontynuowane.

Digital orthophotomap is at present the most popular cartographic product. However, in built-up areas, images of buildings are displaced according to the central projection, and part of the terrain is invisible (the so-called "occluded area"”). A true orthomap does not have such defects. The digital surface model (DSM) is however necessary, with spatial models of buildings to make it. The paper discusses technical aspects of the true-ortho generation. Special requirements relating to the execution of air photos are considered, along with the analysis of generating the building models based on the manual stereo digitalisation of the terrain model built on the basis of photos, automatic image matching, and laser data (LIDAR). The influence of source data on the quality of the outcome true-ortho, and the costs of its producing are reviewed. Preliminary results are presented. Works are continued.

Wykrywanie budynków na podstawie lotniczego skanowania laserowego

Bucior M., Borowiec N.;, Jędrychowski I., Pyka K.

Roczniki Geomatyki

2006

T. 4, z. 3

57-70

This paper discusses automatic detection of buildings from airborne laser scanner data. Beside introduction and conclusions there are three main parts in this paper. In part one basic technical parameters of airborne laser scanning are reminded. Part two presents literature review of various methods that have been applied in the detection and modeling of buildings. Part three describes a research experiment carried out by the authors. This part includes a comparison between two methods of detection: the one offered by specialist software and the alternative method proposed by the authors of this paper. The technique of laser scanning, often referred to as LIDAR, continues to develop very dynamically. It is characterized by a high level of efficiency and accuracy. It is most often used to create 3D models of cities. Until now, LIDAR was mostly used in national studies to determine digital terrain models (DTM), which is done by separating certain points (those which result from laser reflections of trees, buildings and other above-ground surfaces) from disorganized .clouds of points.. Meanwhile, the most useful contribution of this technique is that it enables numeric calculation of the digital surface model (DSM). The authors. experiment attempted to analyze the effectiveness of automatic detection of buildings using two different methods. The first method used original data and applied specialist software which detects and models buildings. In the second, the .cloud of points. was replaced by a regular grid, which had been determined through interpolation. Then, using the typical tool of GIS, the authors carried out a series of experiments. In this paper, the authors present their concept of detection of buildings. This concept is based on an analysis of three surface layers: map of heights, map of slopes and map of texture. The final stage consisted of spatial analysis which showed all the places which meet certain conditions that are adequate for buildings, such as heights, slopes and texture. The methods were implemented on two test areas. One area contained independently standing apartment buildings in which the sides and rooftops of buildings were perpendicular and at right angles to each other. The second test area was made up of various buildings of differentiated heights with steep, multidirectional roofs. For both these areas, reference data was obtained through the vectorization of photogrammetric stereoscopic models. Both methods of detection showed comparable effectiveness. The method using .cloud of points. and specialist software showed slightly straighter roof edges, however a slightly worse balance of surface in relation to the reference data, than the method based on GIS analyses which presents the authors. concepts of detections of buildings. However, the differences were negligible and both methods had a similar level of effectiveness in the detection of buildings: approximately 90% for the easy area and about 60% for difficult area. These results are similar to those presented in literature. During the study, all cases in which detection of buildings was ineffective were also analyzed. Tall trees rising above rooftops often presented a significant obstacle. Moreover, the scanning data contained several places, where LIDAR provided measurements with very low density, much smaller than the average density of 1,5 points per m2. These .holes. lowered the effectiveness of the first method. However, the weakness of the raster method was weak representation of the grid in places where trees were located as the applied interpolation smoothed out the original data. The results of this research lead to the conclusion that an optimal method would entail a .combined. approach. First, the raster analysis should be applied to determine the probable location of buildings. Then, for certain atypical spaces one should return to the source data (cloud of points) and vertically assign cross sections in predefined directions. What is still needed is a method of automatic recognition of buildings on the basis of cross sections as well as dimensions of buildings which aim to obtain a 3D model. This paper confirms a huge potential of the laser scanning technique to create 3D models. The proposed method of detection of buildings proved promising and it can be applied even without expensive specialized software.