633 research outputs found

    Classification of airborne laser scanning point clouds based on binomial logistic regression analysis

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    This article presents a newly developed procedure for the classification of airborne laser scanning (ALS) point clouds, based on binomial logistic regression analysis. By using a feature space containing a large number of adaptable geometrical parameters, this new procedure can be applied to point clouds covering different types of topography and variable point densities. Besides, the procedure can be adapted to different user requirements. A binomial logistic model is estimated for all a priori defined classes, using a training set of manually classified points. For each point, a value is calculated defining the probability that this point belongs to a certain class. The class with the highest probability will be used for the final point classification. Besides, the use of statistical methods enables a thorough model evaluation by the implementation of well-founded inference criteria. If necessary, the interpretation of these inference analyses also enables the possible definition of more sub-classes. The use of a large number of geometrical parameters is an important advantage of this procedure in comparison with current classification algorithms. It allows more user modifications for the large variety of types of ALS point clouds, while still achieving comparable classification results. It is indeed possible to evaluate parameters as degrees of freedom and remove or add parameters as a function of the type of study area. The performance of this procedure is successfully demonstrated by classifying two different ALS point sets from an urban and a rural area. Moreover, the potential of the proposed classification procedure is explored for terrestrial data

    The Application of LiDAR to Assessment of Rooftop Solar Photovoltaic Deployment Potential in a Municipal District Unit

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    A methodology is provided for the application of Light Detection and Ranging (LiDAR) to automated solar photovoltaic (PV) deployment analysis on the regional scale. Challenges in urban information extraction and management for solar PV deployment assessment are determined and quantitative solutions are offered. This paper provides the following contributions: (i) a methodology that is consistent with recommendations from existing literature advocating the integration of cross-disciplinary competences in remote sensing (RS), GIS, computer vision and urban environmental studies; (ii) a robust methodology that can work with low-resolution, incomprehensive data and reconstruct vegetation and building separately, but concurrently; (iii) recommendations for future generation of software. A case study is presented as an example of the methodology. Experience from the case study such as the trade-off between time consumption and data quality are discussed to highlight a need for connectivity between demographic information, electrical engineering schemes and GIS and a typical factor of solar useful roofs extracted per method. Finally, conclusions are developed to provide a final methodology to extract the most useful information from the lowest resolution and least comprehensive data to provide solar electric assessments over large areas, which can be adapted anywhere in the world

    Automated 3D scene reconstruction from open geospatial data sources: airborne laser scanning and a 2D topographic database

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    Open geospatial data sources provide opportunities for low cost 3D scene reconstruction. In this study, based on a sparse airborne laser scanning (ALS) point cloud (0.8 points/m2) obtained from open source databases, a building reconstruction pipeline for CAD building models was developed. The pipeline includes voxel-based roof patch segmentation, extraction of the key-points representing the roof patch outline, step edge identification and adjustment, and CAD building model generation. The advantages of our method lie in generating CAD building models without the step of enforcing the edges to be parallel or building regularization. Furthermore, although it has been challenging to use sparse datasets for 3D building reconstruction, our result demonstrates the great potential in such applications. In this paper, we also investigated the applicability of open geospatial datasets for 3D road detection and reconstruction. Road central lines were acquired from an open source 2D topographic database. ALS data were utilized to obtain the height and width of the road. A constrained search method (CSM) was developed for road width detection. The CSM method was conducted by splitting a given road into patches according to height and direction criteria. The road edges were detected patch by patch. The road width was determined by the average distance from the edge points to the central line. As a result, 3D roads were reconstructed from ALS and a topographic database

    Ilmalaserkeilausaineistojen vertailu perustuen kattojen ominaisuuksiin

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    Laser scanning is nowadays one of the most important technology in geospatial data collection. The technique has developed together with the other technologies and sciences, and the systems can be used with many different platforms on land, in the ocean and in the air. Airborne laser scanning (ALS) started right after the invention of the laser in 1960’s and the usage grew in 1990’s, when the first commercial system was released. The development has augmented the ways of surveying and the systems have new features and more options to collect as accurate data as possible. Several wavelengths and higher frequencies able thousands or even millions of measurements per second. The multispectral systems enable the characterization of the targets from the spectral information which helps for example in the data classification. Single photon technique provides higher imaging capability with lower costs and is used in the extensive topographic measurements. The processing of the point clouds are more important when the densities grow and the amount of noise points is higher. The processing usually includes preprocessing, data management, classification, segmentation and modeling to enable the analyzing of the data. The goal of the thesis is to compare and analyze the datasets of five different airborne laser scanners. The conventional LiDAR datasets are collected from low altitude helicopter with the Riegl’s VUX-1HA and miniVUX-1UAV systems. The state-of-the-art sensors, Titan multispectral LiDAR (Teledyne Optech) and SPL100 single photon LiDAR (Leica), are used in the data collection from the aircraft. The data is collected from the urban area of Espoonlahti, Finland, and the comparison is based on the roof features. Other land cover classes are left out from the investigation. From the roof features are investigated the differences, accuracies and qualities between the datasets. The urban environment was selected because the lack of ALS research done for the built environment, especially in Finland. The thesis introduces the background of the airborne laser scanning, theories and literature review, materials and methods used in the project. The laser scanners used in the work produce dense point clouds, where the most dense is up to 80 pts/m2. Based on the results the accuracies vary mainly between 0 and 10 cm. The scanners with infrared wavelengths produce better than 10 cm accuracies for the outlines of the roofs, unlike the green wavelength scanners. The differences in the corner coordinates are between 1 and 8 cm with a few exceptions. SPL100 system has the best height accuracy of 4.2 cm and otherwise the accuracies vary between 5 and 10 cm. The largest deviation compared to the roof planes occurs in the miniVUX-1UAV data (over 5 cm). For the surface areas the infrared frequencies produce differences of 0 to 2 percent from the reference data, whereas the differences of the green wavelength are mainly 1 to 7 percent. For the inclinations no significant differences were observed.Laserkeilaus on nykyään yksi tärkeimmistä tekniikoista geospatiaalisen tiedon keräämisessä. Tekniikka on kehittynyt yhdessä muiden teknologioiden ja tieteiden kanssa, ja järjestelmiä voidaan käyttää monilla eri alustoilla maassa, meressä ja ilmassa. Ilmalaserkeilaus (ALS) alkoi heti laserin keksimisen jälkeen 1960-luvulla ja käyttö kasvoi 1990-luvulla ensimmäisen kaupallisen järjestelmän julkaisun jälkeen. Kehitys on lisännyt mittaustapoja ja järjestelmien ominaisuuksien parantuessa on enemmän vaihtoehtoja kerätä tarkkaa aineistoa. Useilla aallonpituuksilla ja korkeammilla taajuuksilla pystytään tekemään tuhansia tai jopa miljoonia mittauksia sekunnissa. Monispektriset järjestelmät mahdollista-vat kohteiden tunnistamisen spektritietojen (aallonpituuksien jakauman) mukaan, jota voidaan hyödyntää esimerkiksi aineistojen luokittelussa. Yksifotoni–tekniikka mahdollistaa suuremman mittauskyvyn pienemmällä kustannuksella (energiankulutus) ja sitä käytetään laajojen alueiden mittauksissa. Pistepilvien käsittely on entistä tärkeämpää kun tiheydet kasvavat ja virhepisteiden määrä on suurempi. Prosessointiin kuuluu yleensä esikäsittely, tiedonhallinta, luokittelu, segmentointi ja mallinnus, ennen aineiston analysointia. Tämän opinnäytetyön tavoitteena on vertailla ja analysoida viiden eri ilmalaserkeilaimen tuottamia aineistoja. Ns. tavanomaiset LiDAR–aineistot on kerätty matalalla lentävästä helikopterista Rieglin VUX-1HA ja miniVUX-1UAV –keilaimilla. Viimeisintä tekniikkaa edustavat Titan monispektri LiDAR (Teledyne Optech) ja SPL100 single photon LiDAR (Leica) -aineistot on kerätty lentokoneesta. Aineistot on kerätty Espoonlahden alueelta ja vertailu perustuu kattojen ominaisuuksiin. Muut maanpinnan kohteet jätetään tarkastelun ulkopuolelle. Pistepilvien perusteella tutkitaan aineistojen välisiä eroja, tarkkuuksia ja muita ominaisuuksia. Kaupunkiympäristö valittiin kohteeksi vähäisen rakennetun ympäristön ALS–tutkimuksen takia etenkin Suomessa. Opinnäytetyössä esitellään ilmalaserkeilauksen taustaa, teoriaa ja tehdään kirjallisuuskatsaus aiheeseen liittyen, sekä käydään läpi projektissa käytetyt aineistot ja menetelmät. Työssä käytetyt keilaimet tuottavat tiheitä pistepilviä, joista tihein on jopa 80 pistettä/m2. Tulosten perusteella tarkkuudet vaihtelevat pääosin 0 – 10 cm välillä. Kattolinjojen kohdalla infrapuna-aallonpituutta käyttävät keilaimet pääsevät alle 10 cm, toisin kuin vihreän aallonpituuden keilaimet. Kattojen kulmakoordinaattien erot ovat 1 – 8 cm välillä muutamaa poikkeusta lukuun ottamatta. Korkeuksissa paras tarkkuus on SPL100 laserkeilaimella 4.2 cm, ja muuten ollaan 5 – 10 cm tarkkuuksissa. Suurimmat hajaumat tasoon verrattaessa syntyy miniVUX-1UAV aineistoon (yli 5 cm). Pinta-aloissa infrapunataajuudet tuottavat 0 – 2 prosentin eroja vertailuaineistoon, kun taas vihreällä aallonpituudella erot ovat pääosin 1 – 7 prosenttia. Kaltevuuskulmissa ei havaittu merkittäviä eroja

    A Pipeline of 3D Scene Reconstruction from Point Clouds

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    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 1.1billionin2013to1.1 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

    Towards Automated Analysis of Urban Infrastructure after Natural Disasters using Remote Sensing

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    Natural disasters, such as earthquakes and hurricanes, are an unpreventable component of the complex and changing environment we live in. Continued research and advancement in disaster mitigation through prediction of and preparation for impacts have undoubtedly saved many lives and prevented significant amounts of damage, but it is inevitable that some events will cause destruction and loss of life due to their sheer magnitude and proximity to built-up areas. Consequently, development of effective and efficient disaster response methodologies is a research topic of great interest. A successful emergency response is dependent on a comprehensive understanding of the scenario at hand. It is crucial to assess the state of the infrastructure and transportation network, so that resources can be allocated efficiently. Obstructions to the roadways are one of the biggest inhibitors to effective emergency response. To this end, airborne and satellite remote sensing platforms have been used extensively to collect overhead imagery and other types of data in the event of a natural disaster. The ability of these platforms to rapidly probe large areas is ideal in a situation where a timely response could result in saving lives. Typically, imagery is delivered to emergency management officials who then visually inspect it to determine where roads are obstructed and buildings have collapsed. Manual interpretation of imagery is a slow process and is limited by the quality of the imagery and what the human eye can perceive. In order to overcome the time and resource limitations of manual interpretation, this dissertation inves- tigated the feasibility of performing fully automated post-disaster analysis of roadways and buildings using airborne remote sensing data. First, a novel algorithm for detecting roadway debris piles from airborne light detection and ranging (lidar) point clouds and estimating their volumes is presented. Next, a method for detecting roadway flooding in aerial imagery and estimating the depth of the water using digital elevation models (DEMs) is introduced. Finally, a technique for assessing building damage from airborne lidar point clouds is presented. All three methods are demonstrated using remotely sensed data that were collected in the wake of recent natural disasters. The research presented in this dissertation builds a case for the use of automatic, algorithmic analysis of road networks and buildings after a disaster. By reducing the latency between the disaster and the delivery of damage maps needed to make executive decisions about resource allocation and performing search and rescue missions, significant loss reductions could be achieved

    Object-based Urban Building Footprint Extraction and 3D Building Reconstruction from Airborne LiDAR Data

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    Buildings play an essential role in urban intra-construction, urban planning, climate studies and disaster management. The precise knowledge of buildings not only serves as a primary source for interpreting complex urban characteristics, but also provides decision makers with more realistic and multidimensional scenarios for urban management. In this thesis, the 2D extraction and 3D reconstruction methods are proposed to map and visualize urban buildings. Chapter 2 presents an object-based method for extraction of building footprints using LiDAR derived NDTI (Normalized Difference Tree Index) and intensity data. The overall accuracy of 94.0% and commission error of 6.3% in building extraction is achieved with the Kappa of 0.84. Chapter 3 presents a GIS-based 3D building reconstruction method. The results indicate that the method is effective for generating 3D building models. The 91.4% completeness of roof plane identification is achieved, and the overall accuracy of the flat and pitched roof plane classification is 88.81%, with the user’s accuracy of the flat roof plane 97.75% and pitched roof plane 100%

    Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment

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    A relative height threshold is defined to separate potential roof points from the point cloud, followed by a segmentation of these points into homogeneous areas fulfilling the defined constraints of roof planes. The normal vector of each laser point is an excellent feature to decompose the point cloud into segments describing planar patches. An object-based error assessment is performed to determine the accuracy of the presented classification. It results in 94.4% completeness and 88.4% correctness. Once all roof planes are detected in the 3D point cloud, solar potential analysis is performed for each point. Shadowing effects of nearby objects are taken into account by calculating the horizon of each point within the point cloud. Effects of cloud cover are also considered by using data from a nearby meteorological station. As a result the annual sum of the direct and diffuse radiation for each roof plane is derived. The presented method uses the full 3D information for both feature extraction and solar potential analysis, which offers a number of new applications in fields where natural processes are influenced by the incoming solar radiation (e.g., evapotranspiration, distribution of permafrost). The presented method detected fully automatically a subset of 809 out of 1,071 roof planes where the arithmetic mean of the annual incoming solar radiation is more than 700 kWh/m2
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