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Following the first initiatives taken by the International Programme Committee of the XXIIIrd ISPRS Congress in Prague (Czech Republic) in 2016, modifications of the reviewing process of ISPRS events were further considered during the years 2017 and 2018. This evolution first targets to better fit such a process to the currents requirements and expectations of the ISPRS community. Secondly, it aims to provide unified guidelines for the different steps of the process. Under the aegis of the 2020 Congress Director and ISAC (International Science Advisory Committee) chair, several discussions were held in-between September 2017 and June 2018 with ISAC members, Technical Commission Presidents (TCP), council members, 2016 and 2020 Congress Programme Chairs. This document serves as a unique transparent basis that applies for all kinds of ISPRS events (from Congress and Geospatial Week to smaller workshops), and all categories of people that are bound to be involved in the evaluation process of scientific contributions (authors, reviewers, TCPs, … ). It also specifies the evaluation criteria for the works submitted to ISPRS events, both for full papers and abstracts. Subsequently, it helps authors to improve the content and shape of their contributions. Eventually, this paper is targeted to help new chairs to smoothly prepare their future event. The following guidelines were first adopted for the 2018 Technical Commission Symposia.

International audience; We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our main goal is to produce a reconstruction of a scene that is adapted to the local geometry of objects. Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create triangles for each triplet of self-connected edges. Last, we improve this method with a regularization based on the co-planarity of triangles and collinearity of remaining edges. We compare our results to a naive simplicial complexes reconstruction based on edge length.; Nous présentons une nouvelle méthode pour la reconstruction de complexes simpliciaux (ensembles de points, segments et triangles) à partir de nuages de points 3D obtenus par LiDAR mobile, à balayage plan. Notre méthode utilise la topo-logie inhérente au capteur LiDAR pour définir une relation spatiale entre les points. Pour cela, nous examinons chaque connexion possible entre points, pondérée en fonction de sa distance au capteur, et les filtrons en privilégiant les structures collinéaires, ou perpendiculaires aux impulsions du laser. Ensuite, nous créons et filtrons des triangles pour chaque triplet de segments connectés entre eux, en fonction de leur coplanarité locale. Nous comparons nos résultats à une reconstruction non pondérée d'un complexe simplicial. Abstract We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from plane sweep Mobile Laser Scanning (MLS). Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points, weighted according to its distance to the sensor, and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create and filter triangles for each triplet of self-connected edges and according to their local planarity. We compare our results to an unweighted simplicial complex reconstruction.

In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (linear structures, planar structures and volumetric structures) as well as a reference labeling with respect to five semantic classes (Wire, Pole/Trunk, Façade, Ground and Vegetation) is available.

An energetical approach is proposed for classification decision fusion in urban areas using multispectral and hyperspectral imagery at distinct spatial resolutions. Hyperspectral data provides a great ability to discriminate land-cover classes while multispectral data, usually at higher spatial resolution, makes possible a more accurate spatial delineation of the classes. Hence, the aim here is to achieve the most accurate classification maps by taking advantage of both data sources at the decision level: spectral properties of the hyperspectral data and the geometrical resolution of multispectral images. More specifically, the proposed method takes into account probability class membership maps in order to improve the classification fusion process. Such probability maps are available using standard classification techniques such as Random Forests or Support Vector Machines. Classification probability maps are integrated into an energy framework where minimization of a given energy leads to better classification maps. The energy is minimized using a graph-cut method called quadratic pseudo-boolean optimization (QPBO) with α-expansion. A first model is proposed that gives satisfactory results in terms of classification results and visual interpretation. This model is compared to a standard Potts models adapted to the considered problem. Finally, the model is enhanced by integrating the spatial contrast observed in the data source of higher spatial resolution (i.e., the multispectral image). Obtained results using the proposed energetical decision fusion process are shown on two urban multispectral/hyperspectral datasets. 2-3% improvement is noticed with respect to a Potts formulation and 3-8% compared to a single hyperspectral-based classification.

This paper proposes a pipeline to produce road orthophoto and DTM from Mobile Laser Scanning (MLS). For the ortho, modern laser scanners provide a reflectance information allowing for high quality grayscale images, at a much finer resolution than aerial photography can offer. For DTM, MLS offers a much higher accuracy and density than aerial products. This increased precision and resolution leverages new applications for both ortho and DEM. The first task is to filter ground vs non ground, then an interpolation is conducted to build image tiles from the filtered points. Finally, multiple layers are registered and blended to allow for seamless fusion. Our proposed approach achieves high quality products and scaling up is demonstrated.

Map generalization abstracts and simplifies geographic information to derive maps at smaller scales. The automation of map generalization requires techniques to evaluate the global quality of a generalized map. The quality and legibility of a generalized map is related to the complexity of the map, or the amount of clutter in the map, i.e. the excessive amount of information and its disorganization. Computer vision research is highly interested in measuring clutter in images, and this paper proposes to compare some of the existing techniques from computer vision, applied to generalized maps evaluation. Four techniques from the literature are described and tested on a large set of maps, generalized at different scales: edge density, subband entropy, quad tree complexity, and segmentation clutter. The results are analyzed against several criteria related to generalized maps, the identification of cluttered areas, the preservation of the global amount of information, the handling of occlusions and overlaps, foreground vs background, and blank space reduction.

3D scene analysis by automatically assigning 3D points a semantic label has become an issue of major interest in recent years. Whereas the tasks of feature extraction and classification have been in the focus of research, the idea of using only relevant and more distinctive features extracted from optimal 3D neighborhoods has only rarely been addressed in 3D lidar data processing. In this paper, we focus on the interleaved issue of extracting relevant, but not redundant features and increasing their distinctiveness by considering the respective optimal 3D neighborhood of each individual 3D point. We present a new, fully automatic and versatile framework consisting of four successive steps: (i) optimal neighborhood size selection, (ii) feature extraction, (iii) feature selection, and (iv) classification. In a detailed evaluation which involves 5 different neighborhood definitions, 21 features, 6 approaches for feature subset selection and 2 different classifiers, we demonstrate that optimal neighborhoods for individual 3D points significantly improve the results of scene interpretation and that the selection of adequate feature subsets may even further increase the quality of the derived results.

This article presents a generic and efficient method to register terrestrial mobile data with imperfect location on a geographic database with better overall accuracy but less details. The registration method proposed in this paper is based on a semi-rigid point to plane ICP ("Iterative Closest Point"). The main applications of such registration is to improve existing geographic databases, particularly in terms of accuracy, level of detail and diversity of represented objects. Other applications include fine geometric modelling and fine façade texturing, object extraction such as trees, poles, road signs marks, facilities, vehicles, etc. The geopositionning system of mobile mapping systems is affected by GPS masks that are only partially corrected by an Inertial Navigation System (INS) which can cause an important drift. As this drift varies non-linearly, but slowly in time, it will be modelled by a translation defined as a piecewise linear function of time which variation over time will be minimized (rigidity term). For each iteration of the ICP, the drift is estimated in order to minimise the distance between laser points and planar model primitives (data attachment term). The method has been tested on real data (a scan of the city of Paris of 3.6 million laser points registered on a 3D model of approximately 71,400 triangles).

Tree detection and reconstruction is of great interest in large-scale city modelling. In this paper, we present a marked point process model to detect single trees from airborne laser scanning (ALS) data. We consider single trees in ALS recovered canopy height model (CHM) as a realization of point process of circles. Unlike traditional marked point process, we sample the model in a constraint configuration space by making use of image process techniques. A Gibbs energy is defined on the model, containing a data term which judge the fitness of the model with respect to the data, and prior term which incorporate the prior knowledge of object layouts. We search the optimal configuration through a steepest gradient descent algorithm. The presented hybrid framework was test on three forest plots and experiments show the effectiveness of the proposed method.

Thanks to a hybrid georeferencing unit coupling GNSS and IMU sensors, mobile mapping systems (MMS) with lidar sensors provide accurate 3D point clouds of the acquired areas, mainly urban cities. When dealing with several acquisitions of the same area with the same device, differences in the range of several tens of centimeters can be observed. Such degradation of the georeferencing accuracies are due to two main reasons: inertial drift and losses of GNSS signals in urban corridors. The purpose of this paper is therefore to correct these differences with an accurate ICP-based registration algorithm, and then to correct the MMS trajectory using these retrieved local transformation parameters.The trajectory loop information plays a key role for that purpose. We propose a four-step method starting from a 3D point cloud with overlapping parts, and the trajectory of the mobile mapping system. First, a polygonal approximation of the trajectory is computed in order to first divide the whole registration problem in local sub-issues. Secondly, we aim to find all the potential overlapping acquired areas between these segments using simple bounding box intersections. Thirdly, for each pair of overlapping areas, an efficient variant of the ICP algorithm is proposed to (1) prune cases where segments do not share point clouds of the same areas and (2) retrieve the transformation parameters, for real overlapping cases. Finally, all these transformations are linked together, and fed into a global distance compensation problem, allowing to adjust the MMS trajectories for several passages. As a conclusion, this method is successfully applied to data acquired over Paris (France) with the Stereopolis mobile mapping system.

Automatic 3D point cloud registration is a main issue in computer vision and photogrammetry. The most commonly adopted solution is the well-known ICP (Iterative Closest Point) algorithm. This standard approach performs a fine registration of two overlapping point clouds by iteratively estimating the transformation parameters, and assuming that good a priori alignment is provided. A large body of literature has proposed many variations of this algorithm in order to improve each step of the process. The aim of this paper is to demonstrate how the knowledge of the optimal neighborhood of each 3D point can improve the speed and the accuracy of each of these steps. We will first present the geometrical features that are the basis of this work. These low-level attributes describe the shape of the neighborhood of each 3D point, computed by combining the eigenvalues of the local structure tensor. Furthermore, they allow to retrieve the optimal size for analyzing the neighborhood as well as the privileged local dimension (linear, planar, or volumetric). Besides, several variations of each step of the ICP process are proposed and analyzed by introducing these features. These variations are then compared on real datasets, as well with the original algorithm in order to retrieve the most efficient algorithm for the whole process. Finally, the method is successfully applied to various 3D lidar point clouds both from airborne, terrestrial and mobile mapping systems.

The full-waveform lidar technology allows a complete access to the information related to the emitted and backscattered laser signals. Although most of the common applications of full-waveform lidar are currently dedicated to the study of forested areas, some recent studies have shown that airborne full-waveform data is relevant for urban area analysis. We extend the field to pattern recognition with a focus on retrieval. Our proposed approach combines two steps. In a first time, building edges are coarsely extracted. Then, a physical model based on the lidar equation is used to retrieve a more accurate position of the estimated edge than the size of the lidar footprint. Another consequence is the estimation of more accurate planimetric positions of the extracted echoes.