La Détection des changements tridimensionnels à l'aide de nuages de points : Une revue

peer reviewedChange detection is an important step for the characterization of object dynamics at the earth’s surface. In multi-temporal point clouds, the main challenge is to detect true changes at different granularities in a scene subject to significant noise and occlusion. To better understand new research perspectives in this field, a deep review of recent advances in 3D change detection methods is needed. To this end, we present a comprehensive review of the state of the art of 3D change detection approaches, mainly those using 3D point clouds. We review standard methods and recent advances in the use of machine and deep learning for change detection. In addition, the paper presents a summary of 3D point cloud benchmark datasets from different sensors (aerial, mobile, and static), together with associated information. We also investigate representative evaluation metrics for this task. To finish, we present open questions and research perspectives. By reviewing the relevant papers in the field, we highlight the potential of bi- and multi-temporal point clouds for better monitoring analysis for various applications.11. Sustainable cities and communitie

A FAST VOXEL-BASED INDICATOR FOR CHANGE DETECTION USING LOW RESOLUTION OCTREES

This paper proposes a change detection approach that uses a low-resolution octree enhanced with Gaussian kernels to describe free and occupied space. This so-called Gaussian Occupancy Octree is derived from range measurements and used to represent spatial information for a single epoch. Changes between epochs are encoded using a Delta Octree. A qualitative and quantitative evaluation of the proposed approach shows that its advantages are a fast runtime and the ability to make a statement about the re-exploration of space. An evaluation of the classification accuracy shows that our approach tents towards correct classifications with an overall accuracy of 51.5 %, but is also systematically biased towards the appearance of occupied space

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

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

Developing an interoperable cloud-based visualization workflow for 3D archaeological heritage data. The Palenque 3D Archaeological Atlas

In archaeology, 3D data has become ubiquitous, as researchers routinely capture high resolution photogrammetry and LiDAR models and engage in laborious 3D analysis and reconstruction projects at every scale: artifacts, buildings, and entire sites. The raw data and processed 3D models are rarely shared as their computational dependencies leave them unusable by other scholars. In this paper we outline a novel approach for cloud-based collaboration, visualization, analysis, contextualization, and archiving of multi-modal giga-resolution archaeological heritage 3D data. The Palenque 3D Archaeological Atlas builds on an open source WebGL systems that efficiently interlink, merge, present, and contextualize the Big Data collected at the ancient Maya city of Palenque, Mexico, allowing researchers and stakeholders to visualize, access, share, measure, compare, annotate, and repurpose massive complex archaeological datasets from their web-browsers

Multi-Sensor Data Fusion for Robust Environment Reconstruction in Autonomous Vehicle Applications

In autonomous vehicle systems, understanding the surrounding environment is mandatory for an intelligent vehicle to make every decision of movement on the road. Knowledge about the neighboring environment enables the vehicle to detect moving objects, especially irregular events such as jaywalking, sudden lane change of the vehicle etc. to avoid collision. This local situation awareness mostly depends on the advanced sensors (e.g. camera, LIDAR, RADAR) added to the vehicle. The main focus of this work is to formulate a problem of reconstructing the vehicle environment using point cloud data from the LIDAR and RGB color images from the camera. Based on a widely used point cloud registration tool such as iterated closest point (ICP), an expectation-maximization (EM)-ICP technique has been proposed to automatically mosaic multiple point cloud sets into a larger one. Motion trajectories of the moving objects are analyzed to address the issue of irregularity detection. Another contribution of this work is the utilization of fusion of color information (from RGB color images captured by the camera) with the three-dimensional point cloud data for better representation of the environment. For better understanding of the surrounding environment, histogram of oriented gradient (HOG) based techniques are exploited to detect pedestrians and vehicles.;Using both camera and LIDAR, an autonomous vehicle can gather information and reconstruct the map of the surrounding environment up to a certain distance. Capability of communicating and cooperating among vehicles can improve the automated driving decisions by providing extended and more precise view of the surroundings. In this work, a transmission power control algorithm is studied along with the adaptive content control algorithm to achieve a more accurate map of the vehicle environment. To exchange the local sensor data among the vehicles, an adaptive communication scheme is proposed that controls the lengths and the contents of the messages depending on the load of the communication channel. The exchange of this information can extend the tracking region of a vehicle beyond the area sensed by its own sensors. In this experiment, a combined effect of power control, and message length and content control algorithm is exploited to improve the map\u27s accuracy of the surroundings in a cooperative automated vehicle system

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

Semi-automated Generation of High-accuracy Digital Terrain Models along Roads Using Mobile Laser Scanning Data

Transportation agencies in many countries require high-accuracy (2-20 cm) digital terrain models (DTMs) along roads for various transportation related applications. Compared to traditional ground surveys and aerial photogrammetry, mobile laser scanning (MLS) has great potential for rapid acquisition of high-density and high-accuracy three-dimensional (3D) point clouds covering roadways. Such MLS point clouds can be used to generate high-accuracy DTMs in a cost-effective fashion. However, the large-volume, mixed-density and irregular-distribution of MLS points, as well as the complexity of the roadway environment, make DTM generation a very challenging task. In addition, most available software packages were originally developed for handling airborne laser scanning (ALS) point clouds, which cannot be directly used to process MLS point clouds. Therefore, methods and software tools to automatically generate DTMs along roads are urgently needed for transportation users. This thesis presents an applicable workflow to generate DTM from MLS point clouds. The entire strategy of DTM generation was divided into two main parts: removing non-ground points and interpolating ground points into gridded DTMs. First, a voxel-based upward growing algorithm was developed to effectively and accurately remove non-ground points. Then through a comparative study on four interpolation algorithms, namely Inverse Distance Weighted (IDW), Nearest Neighbour, Linear, and Natural Neighbours interpolation algorithms, the IDW interpolation algorithm was finally used to generate gridded DTMs due to its higher accuracy and higher computational efficiency. The obtained results demonstrated that the voxel-based upward growing algorithm is suitable for areas without steep terrain features. The average overall accuracy, correctness, and completeness values of this algorithm were 0.975, 0.980, and 0.986, respectively. In some cases, the overall accuracy can exceed 0.990. The results demonstrated that the semi-automated DTM generation method developed in this thesis was able to create DTMs with a centimetre-level grid size and 10 cm vertical accuracy using the MLS point clouds