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

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

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

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

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

MODELING OF ROOFS FROM POINT CLOUDS USING GENETIC ALGORITHMS

Building roof extraction has been studied for more than thirty years and it generates models that provide important information for many applications, especially urban planning. The present work aimed to model roofs only from point clouds using genetic algorithms (GAs) to develop a more automatized and efficient method. For this, firstly, an algorithm for edge detection was developed. Experiments were performed with simulated and real point clouds, obtained by LIDAR. In the experiments with simulated point clouds, three types of point clouds with different complexities were created, and the effects of noise and scan line spacing on the results were evaluated. For the experiments with real point clouds, five roofs were chosen as examples, each with a different characteristic. GAs were used to select, among the points identified during edge detection, the so-called ‘significant points’, those which are essential to the accurate reconstruction of the roof model. These points were then used to generate the models, which were assessed qualitatively and quantitatively. Such evaluations showed that the use of GAs proved to be efficient for the modeling of roofs, as the model geometry was satisfactory, the error was within an acceptable range, and the computational effort was clearly reduced

Investigation on roof segmentation for 3D building reconstruction from aerial LIDAR point clouds

Three-dimensional (3D) reconstruction techniques are increasingly used to obtain 3D representations of buildings due to the broad range of applications for 3D city models related to sustainability, efficiency and resilience (i.e., energy demand estimation, estimation of the propagation of noise in an urban environment, routing and accessibility, flood or seismic damage assessment). With advancements in airborne laser scanning (ALS), 3D modeling of urban topography has increased its potential to automatize extraction of the characteristics of individual buildings. In 3D building modeling from light detection and ranging (LIDAR) point clouds, one major challenging issue is how to efficiently and accurately segment building regions and extract rooftop features. This study aims to present an investigation and critical comparison of two different fully automatic roof segmentation approaches for 3D building reconstruction. In particular, the paper presents and compares a cluster-based roof segmentation approach that uses (a) a fuzzy c-means clustering method refined through a density clustering and connectivity analysis, and (b) a region growing segmentation approach combined with random sample consensus (RANSAC) method. In addition, a robust 2.5D dual contouring method is utilized to deliver watertight 3D building modeling from the results of each proposed segmentation approach. The benchmark LIDAR point clouds and related reference data (generated by stereo plotting) of 58 buildings over downtown Toronto (Canada), made available to the scientific community by the International Society for Photogrammetry and Remote Sensing (ISPRS), have been used to evaluate the quality of the two proposed segmentation approaches by analysing the geometrical accuracy of the roof polygons. Moreover, the results of both approaches have been evaluated under different operating conditions against the real measurements (based on archive documentation and celerimetric surveys realized by a total station system) of a complex building located in the historical center of Matera (UNESCO world heritage site in southern Italy) that has been manually reconstructed in 3D via traditional Building Information Modeling (BIM) technique. The results demonstrate that both methods reach good performance metrics in terms of geometry accuracy. However, approach (b), based on region growing segmentation, exhibited slightly better performance but required greater computational time than the clustering-based approach

Ilmalaserkeilausaineistojen vertailu perustuen kattojen ominaisuuksiin

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

Comprehensive approach for building outline extraction from LiDAR data with accent to a sparse laser scanning point cloud

The method of building outline extraction based on segmentation of airborne laser scanning data is proposed and tested on a dataset comprising 1,400 buildings typical for residential and industrial urban areas. The algorithm starts with setting a special threshold to separate building from bare earth points and low objects. Next, local planes are fitted to each point using RANSAC and further refined by least squares adjustment. A normal vector is assigned to each point. Similarities among normal vectors are evaluated in order to assemble planar or curved roof segments. Finally, building outlines are formed from detected segments using the a-shapes algorithm and further regularized. The extracted outlines were compared with reference polygons manually derived from the processed laser scanning point cloud and orthoimages. Area-based evaluation of accuracy of the proposed method revealed completeness and correctness of 87 % and 97 %, respectively, for the test dataset. The influence of parameters like number of points per roof segment, complexity of the roof structure, roof type, and overlap with vegetation on accuracy was evaluated and discussed

Innovative Use and Integration of Remote Sensed Geospatial Data for 3D City Modeling and GIS Urban Applications

Modern remote sensing instruments, mounted on a modern aerial platform and assisted through the use of automated procedures are now capable of acquiring data over a vast area in a short timeframe. Thanks to innovative processing methods and algorithms it is then possible to rapidly deliver results with a high detail and accuracy. The discussed thesis provides a detailed overview, through different case studies and examples, on the evolving complete pipeline required to survey, process, store, integrate, analyze and deliver data in the form of a 3D city model and GIS in the urban environment. A comprehensive 3D city model is, in fact, the necessary multi-disciplinary backbone for the ubiquitous sensors of a Smart City