Automatic modelling and 3D reconstruction of urban buildings from aerial imagery

2009 16th IEEE International Conference on Image Processing (ICIP)

2009

We present a scalable approach to tree detection in large urban landscapes using aerial LiDAR data. Similar to our previous work in 2006, our current method consists of segmentation followed by classification. However, unlike our previous work, the current approach does not use color information or aerial imagery, and hence is more generally applicable. Also, our current approach has been successfully tested on two very large datasets, which are many orders of magnitude larger than the dataset used in 2006. Specifically, we use a North American dataset, containing 125 million LiDAR returns over 3 km 2 , and a European dataset, containing 200 million LiDAR returns over 7 km 2 . For both datasets, we report precision and recall rates of over 95%.

Section: Introductionmentioning

confidence: 99%

2D tree detection in large urban landscapes using aerial LiDAR data

2009 16th IEEE International Conference on Image Processing (ICIP)

2009

“…Such stereo imaging methods use aerial imagery captured by an aircraft [2] or a satellite [3] to build the models. Automated stereo imaging methods usually suffer from poor performance, whereas the non-automated approaches are quite labor intensive.…”

mentioning

confidence: 99%

Tree Detection in Aerial Lidar and Image Data

Secord

2006 International Conference on Image Processing

2006

In this paper, we present an approach to detecting trees in registered aerial image and range data obtained via LiDAR. The motivation for this problem comes from automated city modeling, in which such data is used to generate textured 3-D models. Representing the trees in these models is problematic because the data is usually too sparsely sampled in tree regions to create an accurate 3-D model of the trees. Furthermore, including the tree data points interferes with the polygonization step of the building roof top models. Therefore, it is advantageous to detect and remove points that represent trees in both LiDAR and aerial imagery. In this paper, we propose a two-step method for tree detection consisting of segmentation followed by classification. The segmentation is done using a simple region-growing algorithm using weighted features from aerial image and LiDAR, such as height, texture map, height variation, and normal vector estimates. The weights for the features are determined using a learning method on random walks. The classification is done using weighted support vector machines (SVM), allowing us to control the misclassification rate. The overall problem is formulated as a binary detection problem, and receiver operating characteristic curves are shown to validate our approach.

“…Manual or automated matching of stereo images can be used to obtain a Digital Surface Model (DSM), i.e. a grid-like representation of the elevation level, and 3D models (Frere et al, 1998;Huertas et al, 1999). In recent years, advances in resolution and accuracy of Synthetic Aperture Radar (SAR) and airborne laser scanners have also rendered them suitable for the generation of DSMs and 3D models (Brenner et al, 2001;Maas, 2001).…”

Section: Introductionmentioning

confidence: 99%

An Automated Method for Large-Scale, Ground-Based City Model Acquisition

Früh

International Journal of Computer Vision

2004

177

Abstract. In this paper, we describe an automated method for fast, ground-based acquisition of large-scale 3D city models. Our experimental set up consists of a truck equipped with one camera and two fast, inexpensive 2D laser scanners, being driven on city streets under normal traffic conditions. One scanner is mounted vertically to capture building facades, and the other one is mounted horizontally. Successive horizontal scans are matched with each other in order to determine an estimate of the vehicle's motion, and relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques. Specifically, the final global pose is obtained by utilizing an aerial photograph or a Digital Surface Model as a global map, to which the ground-based horizontal laser scans are matched. A fairly accurate, textured 3D cof the downtown Berkeley area has been acquired in a matter of minutes, limited only by traffic conditions during the data acquisition phase. Subsequent automated processing time to accurately localize the acquisition vehicle is 235 minutes for a 37 minutes or 10.2 km drive, i.e. 23 minutes per kilometer.