ISPRS Int. J. Geo-Inf., Volume 5, Issue 1 (January 2016) – 7 articles

3D building segmentation is an important research issue in the remote sensing community with relevant applications to urban modeling, cloud-to-cloud and cloud-to-model registration, 3D cartography, virtual reality, cultural heritage documentation, among others. In this paper, we propose automatic, parametric and robust approaches to segment façades from 3D point clouds. Processing is carried out using elevation images and 3D decomposition, and the final result can be reprojected onto the 3D point cloud for visualization or evaluation purposes. Our methods are based on geometrical and geodesic constraints. Parameters are related to urban and architectural constraints. Thus, they can be set up to manage façades of any height, length and elongation. We propose two methods based on façade marker extraction and a third method without markers based on the maximal elongation image. This work is developed in the framework of TerraMobilita project. The performance of our methods is proved in our experiments on TerraMobilita databases using 2D and 3D ground truth annotations. Full article

Contribution inequality widely exists in OpenStreetMap (OSM), which means that most data come from a minority of the contributors, while the majority only accounts for a small percentage of data. This phenomenon is of great importance to understanding from where the data come and how the project evolves. The investigation in this paper is dedicated to answering the following questions: How does contribution inequality change over time in OSM? Which group of contributors plays a more important role in influencing trends in contribution inequality: the “vocal minority” or the “silent majority”? To answer the first question, we provide overall measurements for contribution inequality using the Lorenz curve and the Gini coefficient. To answer the second question, we use quantile-based classifying strategy to analyze structural changes in the community, and use the Mann-Whitney-Wilcoxon test to analyze productivity changes. Our case study shows that in countries without significant imports, contributions become more unequal over time. This trend is consistent with the rapid expansion of the silent majority, even though other classes of contributors also grow at a slower pace. On the other hand, contribution inequality fluctuates a lot in countries with huge imports, and agrees well with the productivity changes in the vocal minority. Full article

This paper proposes an extended semi-supervised regression approach to enhance the prediction accuracy of housing prices within the geographical information science field. The method, referred to as co-training geographical weighted regression (COGWR), aims to fully utilize the positive aspects of both the geographical weighted regression (GWR) method and the semi-supervised learning paradigm. Housing prices in Beijing are assessed to validate the feasibility of the proposed model. The COGWR model demonstrated a better goodness-of-fit than the GWR when housing price data were limited because a COGWR is able to effectively absorb no-price data with explanatory variables into its learning by considering spatial variations and nonstationarity that may introduce significant biases into housing prices. This result demonstrates that a semisupervised geographic weighted regression may be effectively used to predict housing prices. Full article

This paper proposes a method for combining stereo visual odometry, Light Detection And Ranging (LiDAR) odometry and reduced Inertial Measurement Unit (IMU) including two horizontal accelerometers and one vertical gyro. The proposed method starts with stereo visual odometry to estimate six Degree of Freedom (DoF) ego motion to register the point clouds from previous epoch to the current epoch. Then, Generalized Iterative Closest Point (GICP) algorithm refines the motion estimation. Afterwards, forward velocity and Azimuth obtained by visual-LiDAR odometer are integrated with reduced IMU outputs in an Extended Kalman Filter (EKF) to provide final navigation solution. In this paper, datasets from KITTI (Karlsruhe Institute of Technology and Toyota technological Institute) were used to compare stereo visual odometry, integrated stereo visual odometry and reduced IMU, stereo visual-LiDAR odometry and integrated stereo visual-LiDAR odometry and reduced IMU. Integrated stereo visual-LiDAR odometry and reduced IMU outperforms other methods in urban areas with buildings around. Moreover, this method outperforms simulated Reduced Inertial Sensor System (RISS), which uses simulated wheel odometer and reduced IMU. KITTI datasets do not include wheel odometry data. Integrated RTK (Real Time Kinematic) GPS (Global Positioning System) and IMU was replaced by wheel odometer to simulate the response of RISS method. Visual Odometry (VO)-LiDAR is not only more accurate than wheel odometer, but it also provides azimuth aiding to vertical gyro resulting in a more reliable and accurate system. To develop low-cost systems, it would be a good option to use two cameras plus reduced IMU. The cost of such a system will be reduced than using full tactical MEMS (Micro-Electro-Mechanical Sensor) based IMUs because two cameras are cheaper than full tactical MEMS based IMUs. The results indicate that integrated stereo visual-LiDAR odometry and reduced IMU can achieve accuracy at the level of state of art. Full article

The key focus of this paper is to establish a procedure that combines the use of Geographical Information Systems (GIS) and remote sensing in order to achieve simulation and modeling of the landscape impact caused by construction. The procedure should be easily and inexpensively developed. With the aid of 3D virtual reconstruction and visualization, this paper proposes that the technologies of remote sensing and GIS can be applied to the landscape for post-urbanization environmental restoration. The goal is to create a rural zone in an urban development sector that integrates the residential areas and local infrastructure into the surrounding natural environment in order to measure the changes to the preliminary urban design. The units of the landscape are determined by means of two cartographic methods: (1) indirect, using the components of the landscape; and (2) direct methods, using the landscape’s elements. The visual basins are calculated for the most transited by the population points, while establishing the zones that present major impacts for the urbanization of their landscape. Based on this, the different construction types are distributed (one-family houses, blocks of houses, etc.), selecting the types of plant masses either with ornamentals or integration depending on the zone; integrating water channels, creating a water channel in recirculation and green spaces and leisure time facilities. The techniques of remote sensing and GIS allow for the visualization and modeling of the urbanization in 3D, simulating the virtual reality of the infrastructure as well as the actions that need to be taken for restoration, thereby providing at a low cost an understanding of landscape integration before it takes place. Full article

Floating car data (FCD) recorded with the Global Positioning System (GPS) are an important data source for traffic research. However, FCD are subject to error, which can relate either to the accuracy of the recordings (measurement error) or to the temporal rate at which the data are sampled (interpolation error). Both errors affect movement parameters derived from the FCD, such as speed or direction, and consequently influence conclusions drawn about the movement. In this paper we combined recent findings about the autocorrelation of GPS measurement error and well-established findings from random walk theory to analyse a set of real-world FCD. First, we showed that the measurement error in the FCD was affected by positive autocorrelation. We explained why this is a quality measure of the data. Second, we evaluated four metrics to assess the influence of interpolation error. We found that interpolation error strongly affects the correct interpretation of the car’s dynamics (speed, direction), whereas its impact on the path (travelled distance, spatial location) was moderate. Based on these results we gave recommendations for recording of FCD using the GPS. Our recommendations only concern time-based sampling, change-based, location-based or event-based sampling are not discussed. The sampling approach minimizes the effects of error on movement parameters while avoiding the collection of redundant information. This is crucial for obtaining reliable results from FCD. Full article