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Point cloud segmentation is an important first step in categorising a raw point cloud data. This step is necessary in order to better manage the data and generate other derivative products, e.g. 3D GI...
This paper addresses the task of semantic segmentation of orthoimagery using multimodal data e.g. optical RGB, infrared and digital surface model. We propose a deep convolutional neural network archit...
Identification and mapping of urban features such as buildings and roads are an important task for cartographers and urban planners. High resolution satellite imagery supports the efficient extraction...
3D city modeling has become important over the last decades as these models are being used in different studies including, energy evaluation, visibility analysis, 3D cadastre, urban planning, change d...
In SAR image interpretation, aircrafts are the important targets arousing much attention. However, it is far from easy to segment an aircraft from the background completely and precisely in SAR images...
Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on...
Detecting unregistered buildings from aerial images is an important task for urban management such as inspection of illegal buildings in green belt or update of GIS database. Moreover, the data acquis...
For the interpretation of point clouds, the semantic definition of extracted segments from point clouds or images is a common problem. Usually, the semantic of geometrical pre-segmented point cloud el...
One of the biggest challenges for an autonomous vehicle (and hence the WEpod) is to see the world as humans would see it. This understanding is the base for a successful and reliable future of autonom...
The fluvial transport is an important aspect of hydrological and geomorphologic studies. The knowledge about the movement parameters of different-size fractions is essential in many applications, such...
Point cloud segmentation is a crucial step in scene understanding and interpretation. The goal is to decompose the initial data into sets of workable clusters with similar properties. Additionally, it...
For extraction of road pixels from combined image and elevation data, Wegner et al. (2015) proposed classification of superpixels into road and non-road, after which a refinement of the classification...
Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a metho...
We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban sc...
Point cloud segmentation and classification is currently a research highlight. Methods in this field create labelled data, where each point has additional class information. Current approaches are to ...

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