Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented

3D Classification of Power Line Scene Using Airborne Lidar Data

Failure to adequately maintain vegetation within a power line corridor has been identified as a main cause of the August 14, 2003 electric power blackout. Such that, timely and accurate corridor mapping and monitoring are indispensible to mitigate such disaster. Moreover, airborne LiDAR (Light Detection And Ranging) has been recently introduced and widely utilized in industries and academies thanks to its potential to automate the data processing for scene analysis including power line corridor mapping. However, today’s corridor mapping practice using LiDAR in industries still remains an expensive manual process that is not suitable for the large-scale, rapid commercial compilation of corridor maps. Additionally, in academies only few studies have developed algorithms capable of recognizing corridor objects in the power line scene, which are mostly based on 2-dimensional classification. Thus, the objective of this dissertation is to develop a 3-dimensional classification system which is able to automatically identify key objects in the power line corridor from large-scale LiDAR data. This dissertation introduces new features for power structures, especially for the electric pylon, and existing features which are derived through diverse piecewise (i.e., point, line and plane) feature extraction, and then constructs a classification model pool by building individual models according to the piecewise feature sets and diverse voltage training samples using Random Forests. Finally, this dissertation proposes a Multiple Classifier System (MCS) which provides an optimal committee of models from the model pool for classification of new incoming power line scene. The proposed MCS has been tested on a power line corridor where medium voltage transmission lines (115 kV and 230 kV) pass. The classification results based on the MCS applied by optimally selecting the pre-built classification models according to the voltage type of the test corridor demonstrate a good accuracy (89.07%) and computationally effective time cost (approximately 4 hours/km) without additional training fees

Vegetation Detection and Classification for Power Line Monitoring

Electrical network maintenance inspections must be regularly executed, to provide a continuous distribution of electricity. In forested countries, the electrical network is mostly located within the forest. For this reason, during these inspections, it is also necessary to assure that vegetation growing close to the power line does not potentially endanger it, provoking forest fires or power outages. Several remote sensing techniques have been studied in the last years to replace the labor-intensive and costly traditional approaches, be it field based or airborne surveillance. Besides the previously mentioned disadvantages, these approaches are also prone to error, since they are dependent of a human operator’s interpretation. In recent years, Unmanned Aerial Vehicle (UAV) platform applicability for this purpose has been under debate, due to its flexibility and potential for customisation, as well as the fact it can fly close to the power lines. The present study proposes a vegetation management and power line monitoring method, using a UAV platform. This method starts with the collection of point cloud data in a forest environment composed of power line structures and vegetation growing close to it. Following this process, multiple steps are taken, including: detection of objects in the working environment; classification of said objects into their respective class labels using a feature-based classifier, either vegetation or power line structures; optimisation of the classification results using point cloud filtering or segmentation algorithms. The method is tested using both synthetic and real data of forested areas containing power line structures. The Overall Accuracy of the classification process is about 87% and 97-99% for synthetic and real data, respectively. After the optimisation process, these values were refined to 92% for synthetic data and nearly 100% for real data. A detailed comparison and discussion of results is presented, providing the most important evaluation metrics and a visual representations of the attained results.Manutenções regulares da rede elétrica devem ser realizadas de forma a assegurar uma distribuição contínua de eletricidade. Em países com elevada densidade florestal, a rede elétrica encontra-se localizada maioritariamente no interior das florestas. Por isso, durante estas inspeções, é necessário assegurar também que a vegetação próxima da rede elétrica não a coloca em risco, provocando incêndios ou falhas elétricas. Diversas técnicas de deteção remota foram estudadas nos últimos anos para substituir as tradicionais abordagens dispendiosas com mão-de-obra intensiva, sejam elas através de vigilância terrestre ou aérea. Além das desvantagens mencionadas anteriormente, estas abordagens estão também sujeitas a erros, pois estão dependentes da interpretação de um operador humano. Recentemente, a aplicabilidade de plataformas com Unmanned Aerial Vehicles (UAV) tem sido debatida, devido à sua flexibilidade e potencial personalização, assim como o facto de conseguirem voar mais próximas das linhas elétricas. O presente estudo propõe um método para a gestão da vegetação e monitorização da rede elétrica, utilizando uma plataforma UAV. Este método começa pela recolha de dados point cloud num ambiente florestal composto por estruturas da rede elétrica e vegetação em crescimento próximo da mesma. Em seguida,múltiplos passos são seguidos, incluindo: deteção de objetos no ambiente; classificação destes objetos com as respetivas etiquetas de classe através de um classificador baseado em features, vegetação ou estruturas da rede elétrica; otimização dos resultados da classificação utilizando algoritmos de filtragem ou segmentação de point cloud. Este método é testado usando dados sintéticos e reais de áreas florestais com estruturas elétricas. A exatidão do processo de classificação é cerca de 87% e 97-99% para os dados sintéticos e reais, respetivamente. Após o processo de otimização, estes valores aumentam para 92% para os dados sintéticos e cerca de 100% para os dados reais. Uma comparação e discussão de resultados é apresentada, fornecendo as métricas de avaliação mais importantes e uma representação visual dos resultados obtidos

Mapping urban tree species in a tropical environment using airborne multispectral and LiDAR data

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAccurate and up-to-date urban tree inventory is an essential resource for the development of strategies towards sustainable urban planning, as well as for effective management and preservation of biodiversity. Trees contribute to thermal comfort within urban centers by lessening heat island effect and have a direct impact in the reduction of air pollution. However, mapping individual trees species normally involves time-consuming field work over large areas or image interpretation performed by specialists. The integration of airborne LiDAR data with high-spatial resolution and multispectral aerial image is an alternative and effective approach to differentiate tree species at the individual crown level. This thesis aims to investigate the potential of such remotely sensed data to discriminate 5 common urban tree species using traditional Machine Learning classifiers (Random Forest, Support Vector Machine, and k-Nearest Neighbors) in the tropical environment of Salvador, Brazil. Vegetation indices and texture information were extracted from multispectral imagery, and LiDAR-derived variables for tree crowns, were tested separately and combined to perform tree species classification applying three different classifiers. Random Forest outperformed the other two classifiers, reaching overall accuracy of 82.5% when using combined multispectral and LiDAR data. The results indicate that (1) given the similarity in spectral signature, multispectral data alone is not sufficient to distinguish tropical tree species (only k-NN classifier could detect all species); (2) height values and intensity of crown returns points were the most relevant LiDAR features, combination of both datasets improved accuracy up to 20%; (3) generation of canopy height model derived from LiDAR point cloud is an effective method to delineate individual tree crowns in a semi-automatic approach

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

Voxel-based extraction of individual pylons and wires from lidar point cloud data

Extraction of individual pylons and wires is important for modelling of 3D objects in a power line corridor (PLC) map. However, the existing methods mostly classify points into distinct classes like pylons and wires, but hardly into individual pylons or wires. The proposed method extracts standalone pylons, vegetation and wires from LiDAR data. The extraction of individual objects is needed for a detailed PLC mapping. The proposed approach starts off with the separation of ground and non ground points. The non-ground points are then classified into vertical (e.g., pylons and vegetation) and non-vertical (e.g., wires) object points using the vertical profile feature (VPF) through the binary support vector machine (SVM) classifier. Individual pylons and vegetation are then separated using their shape and area properties. The locations of pylons are further used to extract the span points between two successive pylons. Finally, span points are voxelised and alignment properties of wires in the voxel grid is used to extract individual wires points. The results are evaluated on dataset which has multiple spans with bundled wires in each span. The evaluation results show that the proposed method and features are very effective for extraction of individual wires, pylons and vegetation with 99% correctness and 98% completeness