Airborne lidar feature selection for urban classification using random forests

Various multi-echo and Full-waveform (FW) lidar features can be processed. In this paper, multiple classifers are applied to lidar feature selection for urban scene classification. Random forests are used since they provide an accurate classification and run efficiently on large datasets. Moreover, they return measures of variable importance for each class. The feature selection is obtained by backward elimination of features depending on their importance. This is crucial to analyze the relevance of each lidar feature for the classification of urban scenes. The Random Forests classification using selected variables provide an overall accuracy of 94.35%.

ANALYSIS OF FULL-WAVEFORM LIDAR DATA FOR CLASSIFICATION OF URBAN AREAS

International audienceIn contrast to conventional airborne multi-echo laser scanner systems, full-waveform (FW) lidar systems are able to record the entire emitted and backscattered signal of each laser pulse. Instead of clouds of individual 3D points, FW devices provide connected 1D profiles of the 3D scene, which contain more detailed and additional information about the structure of the illuminated surfaces. This paper is focused on the analysis of FW data in urban areas. The problem of modelling FW lidar signals is first tackled. The standard method assumes the waveform to be the superposition of signal contributions of each scattering object in such a laser beam, which are approximated by Gaussian distributions. This model is suitable in many cases, especially in vegetated terrain. However, since it is not tailored to urban waveforms, the generalized Gaussian model is selected instead here. Then, a pattern recognition method for urban area classification is proposed. A supervised method using Support Vector Machines is performed on the FW point cloud based on the parameters extracted from the post-processing step. Results show that it is possible to partition urban areas in building, vegetation, natural ground and artificial ground regions with high accuracy using only lidar waveforms

Fully automatic analysis of archival aerial images current status and challenges

International audienceArchival aerial images are a unique and relatively unexplored means to generate detailed land-cover information in 3D over the past 100 years. Many long-term environmental monitoring studies can be based on this type of image series. Such data provide a relatively dense temporal sampling of the territories with very high spatial resolution. Furthermore, photogrammetric workflows exist in order to both produce orthoimages and Digital Surface Models, with reasonable interactive actions. However, today, there is no fully automatic pipeline for generating such kind of data. This paper presents the main avenues of research in order to develop such workflow, starting from registration and radiometric issues up to land-cover classification challenges

MANAGING FULL WAVEFORM LIDAR DATA: A CHALLENGING TASK FOR THE FORTHCOMING YEARS

International audienceThis paper proposes to summarize researches and new advances in full waveform lidar data. After a description of full waveform lidar systems, we will review different methodologies developed to process the waveforms (modelling, correlation, stacking). Applications on urban and vegetated areas are then presented. The paper ends up with recommendations on future research themes

A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

International audienceIn this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as " tree points " and " other points ". The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the " tree points ". This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6% are labeled as " tree points ". The derived results clearly reveal a semantic classification of high accuracy (up to 90.77%) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h)

Building large urban environments from unstructured point data

International audienceWe present a robust method for modeling cities from unstructured point data. Our algorithm provides a more complete description than existing approaches by reconstructing simultaneously buildings, trees and topologically complex grounds. Buildings are modeled by an original approach which guarantees a high generalization level while having semantized and compact representations. Geometric 3D-primitives such as planes, cylinders, spheres or cones describe regular roof sections, and are combined with mesh-patches that represent irregular roof components. The various urban components interact through a non-convex energy minimization problem in which they are propagated under arrangement constraints over a planimetric map. We experimentally validate the approach on complex urban structures and large urban scenes of millions of points

Modeling urban landscapes from point clouds: a generic approach

We present a robust method for modeling cities from 3D-point data. Our algorithm provides a more complete description than existing approaches by reconstructing simultaneously buildings, trees and topographically complex grounds. A major contribution of our work is the original way of modeling buildings which guarantees a high generalization level while having semantized and compact representations. Geometric 3D-primitives such as planes, cylinders, spheres or cones describe regular roof sections, and are combined with mesh-patches that represent irregular roof components. The various urban components interact through a non-convex energy minimization problem in which they are propagated under arrangement constraints over a planimetric map. Our approach is experimentally validated on complex buildings and large urban scenes of millions of points and compare it to state-of-the-art methods.Nous présentons une méthode robuste pour modéliser les villes à partir de nuages de points 3D. Notre algorithme fournit une description plus complète que les approches existantes en reconstruisant simultanément bâtiments, arbres et sols topographiquement complexes. Une des contributions importantes réside dans la manière originale de modéliser en 3D les bâtiments, garantissant un niveau de généralisation élevé tout en ayant une représentation compacte et sémantisée. Des primitive géométriques 3D telles que des plans, des cylindres, des sphères ou des cones décrivent les facettes de toit régulières. Elles sont combinées avec des parties de maillages qui représentent les composants de toits irréguliers. Les différents éléments urbains intéragissent au sein d'un problème de minimisation d'énergie non convexe dans lequel ils sont propagés sous des contraintes d'arrangement sur une carte planimétrique. L'approche est validée expérimentalement sur des bâtiments complexes et sur des scènes à grandes échelles contenant des millions de points, et comparée à des méthodes références

Creating large-scale city models from 3D-point clouds: a robust approach with hybrid representation

International audienceWe present a novel and robust method for modeling cities from 3D-point data. Our algorithm pro- vides a more complete description than existing ap- proaches by reconstructing simultaneously buildings, trees and topologically complex grounds. A major con- tribution of our work is the original way of model- ing buildings which guarantees a high generalization level while having semantized and compact represen- tations. Geometric 3D-primitives such as planes, cylin- ders, spheres or cones describe regular roof sections, and are combined with mesh-patches that represent irregu- lar roof components. The various urban components in- teract through a non-convex energy minimization prob- lem in which they are propagated under arrangement constraints over a planimetric map. Our approach is ex- perimentally validated on complex buildings and large urban scenes of millions of points, and is compared to state-of-the-art methods

Full waveform topographic lidar : state-of-the-art

We present a survey of the literature available on full-waveform lidar systems and resulting data. This survey concerns satellite and aerial remote sensing using active systems. Full-waveform airborne laser scanning theoretical principles are first described as well as their technological applications. Besides, main full-wave sensors and their specifications are presented. Furthermore, a review of digitized received signal processes and extracted data analysis are tackled. Eventually, topics of interest dealing with the interpretation of full-waveform measures are discussed, especially vegetation structural parameters estimation and forest and urban modeling, showing the potentiality of such data.Nous présentons une étude bibliographique sur les systèmes lidar topographiques à retour d’onde complète, aussi nommés lidar full-waveform, ainsi que sur les données générées. Le contexte de cette étude se situe dans le domaine de la télédétection aérienne et spatiale par systèmes actifs. Les bases théoriques de ces systèmes sont tout d’abord décrites ainsi que les applications technologiques induites. Ensuite, nous présentons les principaux capteurs à retour d’onde complète avec les spécifications techniques. Alors, à la lumière d’articles publiés dans la littérature scientifique, nous abordons les mécanismes de traitement des données brutes, modélisation et analyse quantitative des données extraites. Enfin, les thématiques d’applications liées à la cartographie des milieux forestiers et urbains sont évoquées, mettant en avant le potentiel de ces données lidar

PATHWAY DETECTION AND GEOMETRICAL DESCRIPTION FROM ALS DATA IN FORESTED MOUNTANEOUS AREA

International audienceIn the last decade, airborne laser scanning (ALS) systems have become an alternative source for the acquisition of altimeter data. Compared to high resolution orthoimages, one of the main advantages of ALS is the ability of the laser beam to penetrate vegetation and reach the ground underneath. Therefore, 3D point clouds are essential data for computing Digital Terrain Models (DTM) in natural and vegetated areas. DTMs are a key product for many applications such as tree detection, flood modelling, archeology or road detection. Indeed, in forested areas, traditional image-based algorithms for road and pathway detection would partially fail due to their occlusion by the canopy cover. Thus, crucial information for forest management and fire prevention such as road width and slope would be misevaluated. This paper deals with road and pathway detection in a complex forested mountaneous area and with their geometrical parameter extraction using lidar data. Firstly, a three-step image-based methodology is proposed to detect road regions. Lidar feature orthoimages are first generated. Then, road seeds are both automatically and semi-automatically detected. And, a region growing algorithm is carried out to retrieve the full pathways from the seeds previously detected. Secondly, these pathways are vectorized using morphological tools, smoothed, and discretized. Finally, 1D sections within the lidar point cloud are successively generated for each point of the pathways to estimate more accurately road widths in 3D. We also retrieve a precise location of the pathway borders and centers, exported as vector data