Recognition of Dormers from lidar data using support vector machine

Satari, Mehran

doi:10.1109/igarss.2012.6352239

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to these projects, most of the research found focuses on a specific type of obstacle only. In that sense, Satari (2012) proposes a way to recognize dormers based on lidar data and an LoD2 model without detailed roof structures through support vector machine, a supervised machine learning model. The approach is based on the different normal direction of dormer points to the main roof points.…”

Section: Literature Reviewmentioning

confidence: 99%

Inferring Roof Semantics for More Accurate Solar Potential Assessment

Apra

Bachert

Tocora

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. In guiding the energy transition efforts towards renewable energy sources, 3D city models were shown to be useful tools when assessing the annual solar energy generation potential of urban landscapes. However, the simplified roof geometry included in these 3D city models and the lack of additional semantic information about the buildings’ roof often yield less accurate solar potential evaluations than desirable. In this paper we propose three different methods to infer and store additional information into 3D city models, namely on physical obstacles present on the roof and existing solar panels. Both can be used to increase the accuracy of roof solar panel retrofit potential. These methods are developed and tested on the open datasets available in the Netherlands, specifically AHN3 lidar point-cloud and PDOK aerial photography. However, we believe they can be adapted to different environments as well, based on the available datasets and their precision locally available.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Inferring Roof Semantics for More Accurate Solar Potential Assessment

Apra

Bachert

Tocora

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…For example, in (Satari, 2012) a support vector machine (SVM) is used for the recognition of three predefined dormer types. It utilizes the gradient and azimuth values of the normal vectors of the superstructure and the underlying roof plane as a discriminating factor.…”

Section: Related Workmentioning

confidence: 99%

Joint Simultaneous Reconstruction of Regularized Building Superstructures From Low-Density Lidar Data Using Icp

Wichmann

Kada

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:There are many applications for 3D city models, e.g., in visualizations, analysis, and simulations; each one requiring a certain level of detail to be effective. The overall trend goes towards including various kinds of anthropogenic and natural objects therein with ever increasing geometric and semantic details. A few years back, the featured 3D building models had only coarse roof geometry. But nowadays, they are expected to include detailed roof superstructures like dormers and chimneys. Several methods have been proposed for the automatic reconstruction of 3D building models from airborne based point clouds. However, they are usually unable to reliably recognize and reconstruct small roof superstructures as these objects are often represented by only few point measurements, especially in low-density point clouds. In this paper, we propose a recognition and reconstruction approach that overcomes this problem by identifying and simultaneously reconstructing regularized superstructures of similar shape. For this purpose, candidate areas for superstructures are detected by taking into account virtual sub-surface points that are assumed to lie on the main roof faces below the measured points. The areas with similar superstructures are detected, extracted, grouped together, and registered to one another with the Iterative Closest Point (ICP) algorithm. As an outcome, the joint point density of each detected group is increased, which helps to recognize the shape of the superstructure more reliably and in more detail. Finally, all instances of each group of superstructures are modeled at once and transformed back to their original position. Because superstructures are reconstructed in groups, symmetries, alignments, and regularities can be enforced in a straight-forward way. The validity of the approach is presented on a number of example buildings from the Vaihingen test data set.

show abstract

“…Only a few training samples, called support vectors, are required. SVM has shown its potential to cope with uncertainty in data caused by the noise and fluctuation, and its computationally efficient compared to several other methods [3]. Such properties are particularly suited for remote sensing classification problems and explains their large adoption [4].…”

Section: Introductionmentioning

confidence: 99%

3D Classification optimization of Airborne LiDAR data by parameters selection

Aissou¹,

Aissa²

2021

Proceedings of Conference GIS Ostrava 2021 Advances in Localization and Navigation

View full text Add to dashboard Cite

Airborne Laser Scanning (ALS) point cloud segmentation and classification are very important and attractive tasks that still interest many scientists. In this paper, 3D classification optimization method is applied for of airborne LIDAR point cloud, covering an urban area with a low density. Our main contribution is to solve classifier and kernel hyperparameters tuning issue. Typically, choice of parameters is done empirically. In almost all classification studies, no details are given for the classifier parameters choice, which is very important. The parameters of the classifier have a direct impact on the classification results. Finding hyper-parameters leads to optimal classification results. In order to determinate the hyper-parameters, authors propose to apply in the context of LiDAR data, a method proposed in the literature called parameters selection to deduce the best parameters, in order to optimize the classifications results. The results shown that parameters selection does not systematically lead to hyper-parameters, but can be used as first stage, then a fine search can be performed around the obtained interval.

show abstract

Recognition of Dormers from lidar data using support vector machine

Cited by 3 publications

References 0 publications

Inferring Roof Semantics for More Accurate Solar Potential Assessment

Inferring Roof Semantics for More Accurate Solar Potential Assessment

Joint Simultaneous Reconstruction of Regularized Building Superstructures From Low-Density Lidar Data Using Icp

3D Classification optimization of Airborne LiDAR data by parameters selection

Contact Info

Product

Resources

About