ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

Extracting Buildings from True Color Stereo Aerial Images Using a Decision Making Strategy

The automatic extraction of buildings from true color stereo aerial imagery in a dense built-up area is the main focus of this paper. Our approach strategy aimed at reducing the complexity of the image content by means of a three-step procedure combining reliable geospatial image analysis techniques. Even if it is a rudimentary first step towards a more general approach, the method presented proved useful in urban sprawl studies for rapid map production in flat area by retrieving indispensable information on buildings from scanned historic aerial photography. After the preliminary creation of a photogrammetric model to manage Digital Surface Model and orthophotos, five intermediate mask-layers data (Elevation, Slope, Vegetation, Shadow, Canny, Shadow, Edges) were processed through the combined use of remote sensing image processing and GIS software environments. Lastly, a rectangular building block model without roof structures (Level of Detail, LoD1) was automatically generated. System performance was evaluated with objective criteria, showing good results in a complex urban area featuring various types of building objects

CityGML rakennusmallien tuottaminen ilmalaserkeilauksesta

3D city models have become an important tool in many applications across different fields. Usually these 3D city models only represent the geometrical attributes of the city, which enables easy visualization of cities. Yet, different thematic queries, analysis tasks, and spatial data mining are out of the reach of models that only offer us information about their geometry. CityGML 3D city models bring an addition of semantic information to the models. In this thesis, the process and different techniques of building reconstruction from airborne laser scanning are explained. CityGML standard will also be explained and what has to be done in order to go from 3D building models to CityGML. The main focus of this thesis was to study how well it is possible to automatically create CityGML 2.0 3D city models from data collected only by airborne laser scanning. CityGML has five different levels-of-detail indicating the level of precision of the building. LOD1 and LOD2 were the most important levels for this thesis, and so it was tested how well different software were able to export reconstructed building models in the CityGML format with these precision levels. These exports were checked against the official specification of CityGML to see how well they met the requirements. It was also explained what more would be needed for the process and data, in order to produce higher quality models in LOD3. Two different test areas were chosen with different building and roof types. One area included detached houses, some partly covered with vegetation, and another area included mainly apartment houses. The thesis shows that as of now, it is still quite challenging to automatically produce city models that are in line with the CityGML 2.0 standard. The model driven methods had problems when it came to building installations, such as chimneys. These could not be modelled with software that used model driven methods. Data driven methods on the other hand had problems when it came to the conversion from the building models to the CityGML format. Terrain and terrain intersection curve also turned out to be more difficult to model than anticipated. Most of the software used in this thesis were not able to automatically handle the addition of these elements. The elements were possible to add later on to the CityGML file but only with use of additional software tools.3D kaupunkimalleista on tullut tärkeä työkalu eri alojen käyttämissä sovelluksissa. Yleensä näitä 3D kaupunkimalleja käytetään vain kaupunkien geometristen attribuuttien mallintamiseen visualisointitarkoituksiin. Kuitenkin erilaiset temaattiset kyselyt, analyysitehtävät ja spatiaalinen tiedonlouhinta ovat pelkästään geometriaa esittävien mallien ulottumattomissa. CityGML 3D kaupunkimallit ottavat huomioon lisäksi myös semanttisen tiedon. Tässä työssä selitetään rakennusten rekonstruointiprosessi ilmalaserkeilauksesta sekä esitellään erilaisia rekonstruointitekniikoita. Myös CityGML standardi esitellään sekä se, mitä 3D rakennusmalleille pitää tehdä, jotta ne saataisiin CityGML muotoon. Tämän työn pääpiste oli, kuinka hyvin on mahdollista automaattisesti luoda CityGML 2.0 muotoisia 3D kaupunkimalleja pelkästään ilmalaserkeilaamalla kerätystä aineistosta. CityGMLssä on viisi erilaista yksityiskohtatasoa, jotka kertovat, kuinka tarkasti rakennus on mallinnettu. Näistä LOD1 ja LOD2 olivat tämän työn kannalta oleellisimmat. Tämän vuoksi sitä, kuinka hyvin ohjelmista saadaan ulos rakennusmalleja CityGML muodossa näillä tarkkuusvaatimuksilla, testattiin. Saatuja tuloksia verrattiin virallisiin CityGML vaatimuksiin, jotta saatiin selville, kuinka hyvin vaatimukset täyttyivät. Myös se käytiin läpi, mitä muutoksia tarvittaisiin, jotta malleista saataisiin korkeamman, LOD3, tason malleja. Valittiin kaksi erilaista testialuetta, joilla oli erilaisia rakennus- ja kattotyyppejä. Toisella alueella oli omakotitaloja, joista jotkut olivat osittain kasvillisuuden peittämiä ja toisella oli pääsääntöisesti kerrostaloja. Työstä käy ilmi, että vielä tällä hetkellä automaattinen CityGML 2.0 standardin mukaisten kaupunkimallien tuottaminen on haastavaa. Mallipohjaisilla menetelmillä oli vaikeuksia rakennusten pienien osien, kuten savupiippujen, suhteen. Näitä ei pystytty mallintamaan ohjelmilla, jotka pohjautuivat mallipohjaisiin menetelmiin. Toisaalta tietopohjaisilla menetelmillä oli ongelmia, kun ne muunnettiin CityGML formaattiin. Maanpinnan sekä maanpinnan ja rakennuksen leikkauksen mallintamisessa oli odotettua enemmän ongelmia. Useimmat tässä työssä käytetyt ohjelmat eivät pystyneet automaattisesti näitä mallintamaan. Kuitenkin, ne oli mahdollista lisätä jälkikäteen käyttämällä muita ohjelmia

Robust building identification from street views using deep convolutional neural networks

Street view imagery (SVI) is a rich source of information for architectural and urban analysis using computer vision techniques, but its integration with other building-level data sources requires an additional step of visual building identification. This step is particularly challenging in architecturally homogeneous, dense residential streets featuring narrow buildings, due to a combination of SVI geolocation errors and occlusions that significantly increase the risk of confusing a building with its neighboring buildings. This paper introduces a robust deep learning-based method to identify buildings across multiple street views taken at different angles and times, using global optimization to correct the position and orientation of street view panoramas relative to their surrounding building footprints. Evaluating the method on a dataset of 2000 street views shows that its identification accuracy (88%) outperforms previous deep learning-based methods (79%), while methods solely relying on geometric parameters correctly show the intended building less than 50% of the time. These results indicate that previous identification methods lack robustness to panorama pose errors when buildings are narrow, densely packed, and subject to occlusions, while collecting multiple views per building can be leveraged to increase the robustness of visual identification by ensuring that building views are consistent

Automated 3D model generation for urban environments [online]

Abstract In this thesis, we present a fast approach to automated generation of textured 3D city models with both high details at ground level and complete coverage for birds-eye view. A ground-based facade model is acquired by driving a vehicle equipped with two 2D laser scanners and a digital camera under normal traffic conditions on public roads. One scanner is mounted horizontally and is used to determine the approximate component of relative motion along the movement of the acquisition vehicle via scan matching; the obtained relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques using an aerial photograph or a Digital Surface Model as a global map. The second scanner is mounted vertically and is used to capture the 3D shape of the building facades. Applying a series of automated processing steps, a texture-mapped 3D facade model is reconstructed from the vertical laser scans and the camera images. In order to obtain an airborne model containing the roof and terrain shape complementary to the facade model, a Digital Surface Model is created from airborne laser scans, then triangulated, and finally texturemapped with aerial imagery. Finally, the facade model and the airborne model are fused to one single model usable for both walk- and fly-thrus. The developed algorithms are evaluated on a large data set acquired in downtown Berkeley, and the results are shown and discussed

Strain state detection in composite structures: Review and new challenges

Developing an advanced monitoring system for strain measurements on structural components represents a significant task, both in relation to testing of in-service parameters and early identification of structural problems. This paper aims to provide a state-of-the-art review on strain detection techniques in composite structures. The review represented a good opportunity for direct comparison of different novel strain measurement techniques. Fibers Bragg grating (FBG) was discussed as well as non-contact techniques together with semiconductor strain gauges (SGs), specifically infrared (IR) thermography and the digital image correlation (DIC) applied in order to detect strain and failure growth during the tests. The challenges of the research community are finally discussed by opening the current scenario to new objectives and industrial applications

Strain State Detection in Composite Structures: Review and New Challenges

Developing an advanced monitoring system for strain measurements on structural components represents a significant task, both in relation to testing of in-service parameters and early identification of structural problems. This paper aims to provide a state-of-the-art review on strain detection techniques in composite structures. The review represented a good opportunity for direct comparison of different novel strain measurement techniques. Fibers Bragg grating (FBG) was discussed as well as non-contact techniques together with semiconductor strain gauges (SGs), specifically infrared (IR) thermography and the digital image correlation (DIC) applied in order to detect strain and failure growth during the tests. The challenges of the research community are finally discussed by opening the current scenario to new objectives and industrial applications

Building Footprint Extraction from LiDAR Data and Imagery Information

This study presents an automatic method for regularisation of building outlines. Initially, building segments are extracted using a new fusion method. Data- and model-driven approaches are then combined to generate approximate building polygons. The core part of the method includes a novel data-driven algorithm based on likelihood equation derived from the geometrical properties of a building. Finally, the Gauss-Helmert and Gauss-Markov models adjustment are implemented and modified for regularisation of building outlines considering orthogonality constraints

A Pipeline of 3D Scene Reconstruction from Point Clouds

3D technologies are becoming increasingly popular as their applications in industrial, consumer, entertainment, healthcare, education, and governmental increase in number. According to market predictions, the total 3D modeling and mapping market is expected to grow from $1.1 billion in 2013 to$7.7 billion by 2018. Thus, 3D modeling techniques for different data sources are urgently needed. This thesis addresses techniques for automated point cloud classification and the reconstruction of 3D scenes (including terrain models, 3D buildings and 3D road networks). First, georeferenced binary image processing techniques were developed for various point cloud classifications. Second, robust methods for the pipeline from the original point cloud to 3D model construction were proposed. Third, the reconstruction for the levels of detail (LoDs) of 1-3 (CityGML website) of 3D models was demonstrated. Fourth, different data sources for 3D model reconstruction were studied. The strengths and weaknesses of using the different data sources were addressed. Mobile laser scanning (MLS), unmanned aerial vehicle (UAV) images, airborne laser scanning (ALS), and the Finnish National Land Survey’s open geospatial data sources e.g. a topographic database, were employed as test data. Among these data sources, MLS data from three different systems were explored, and three different densities of ALS point clouds (0.8, 8 and 50 points/m2) were studied. The results were compared with reference data such as an orthophoto with a ground sample distance of 20cm or measured reference points from existing software to evaluate their quality. The results showed that 74.6% of building roofs were reconstructed with the automated process. The resulting building models provided an average height deviation of 15 cm. A total of 6% of model points had a greater than one-pixel deviation from laser points. A total of 2.5% had a deviation of greater than two pixels. The pixel size was determined by the average distance of input laser points. The 3D roads were reconstructed with an average width deviation of 22 cm and an average height deviation of 14 cm. The results demonstrated that 93.4% of building roofs were correctly classified from sparse ALS and that 93.3% of power line points are detected from the six sets of dense ALS data located in forested areas. This study demonstrates the operability of 3D model construction for LoDs of 1-3 via the proposed methodologies and datasets. The study is beneficial to future applications, such as 3D-model-based navigation applications, the updating of 2D topographic databases into 3D maps and rapid, large-area 3D scene reconstruction. 3D-teknologiat ovat tulleet yhä suositummiksi niiden sovellusalojen lisääntyessä teollisuudessa, kuluttajatuotteissa, terveydenhuollossa, koulutuksessa ja hallinnossa. Ennusteiden mukaan 3D-mallinnus- ja -kartoitusmarkkinat kasvavat vuoden 2013 1,1 miljardista dollarista 7,7 miljardiin vuoteen 2018 mennessä. Erilaisia aineistoja käyttäviä 3D-mallinnustekniikoita tarvitaankin yhä enemmän. Tässä väitöskirjatutkimuksessa kehitettiin automaattisen pistepilviaineiston luokittelutekniikoita ja rekonstruoitiin 3D-ympäristöja (maanpintamalleja, rakennuksia ja tieverkkoja). Georeferoitujen binääristen kuvien prosessointitekniikoita kehitettiin useiden pilvipisteaineistojen luokitteluun. Työssä esitetään robusteja menetelmiä alkuperäisestä pistepilvestä 3D-malliin eri CityGML-standardin tarkkuustasoilla. Myös eri aineistolähteitä 3D-mallien rekonstruointiin tutkittiin. Eri aineistolähteiden käytön heikkoudet ja vahvuudet analysoitiin. Testiaineistona käytettiin liikkuvalla keilauksella (mobile laser scanning, MLS) ja ilmakeilauksella (airborne laser scanning, ALS) saatua laserkeilausaineistoja, miehittämättömillä lennokeilla (unmanned aerial vehicle, UAV) otettuja kuvia sekä Maanmittauslaitoksen avoimia aineistoja, kuten maastotietokantaa. Liikkuvalla laserkeilauksella kerätyn aineiston osalta tutkimuksessa käytettiin kolmella eri järjestelmällä saatua dataa, ja kolmen eri tarkkuustason (0,8, 8 ja 50 pistettä/m2) ilmalaserkeilausaineistoa. Tutkimuksessa saatuja tulosten laatua arvioitiin vertaamalla niitä referenssiaineistoon, jona käytettiin ortokuvia (GSD 20cm) ja nykyisissä ohjelmistoissa olevia mitattuja referenssipisteitä. 74,6 % rakennusten katoista saatiin rekonstruoitua automaattisella prosessilla. Rakennusmallien korkeuksien keskipoikkeama oli 15 cm. 6 %:lla mallin pisteistä oli yli yhden pikselin poikkeama laseraineiston pisteisiin verrattuna. 2,5 %:lla oli yli kahden pikselin poikkeama. Pikselikoko määriteltiin kahden laserpisteen välimatkan keskiarvona. Rekonstruoitujen teiden leveyden keskipoikkeama oli 22 cm ja korkeuden keskipoikkeama oli 14 cm. Tulokset osoittavat että 93,4 % rakennuksista saatiin luokiteltua oikein harvasta ilmalaserkeilausaineistosta ja 93,3 % sähköjohdoista saatiin havaittua kuudesta tiheästä metsäalueen ilmalaserkeilausaineistosta. Tutkimus demonstroi 3D-mallin konstruktion toimivuutta tarkkuustasoilla (LoD) 1-3 esitetyillä menetelmillä ja aineistoilla. Tulokset ovat hyödyllisiä kehitettäessä tulevaisuuden sovelluksia, kuten 3D-malleihin perustuvia navigointisovelluksia, topografisten 2D-karttojen ajantasaistamista 3D-kartoiksi, ja nopeaa suurten alueiden 3D-ympäristöjen rekonstruktiota