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Very high resolution (VHR) aerial images can provide detailed analysis about landscape and environment; nowadays, thanks to the rapid growing airborne data acquisition technology an increasing number ...
The recent success of deep convolutional neural networks (CNN) on a large number of applications can be attributed to large amounts of available training data and increasing computing power. In this p...
Forest pest is one of the most important factors affecting the health of forest. However, since it is difficult to figure out the pest areas and to predict the spreading ways just to partially control...
Over the last couple of years more and more analogue airborne cameras were replaced by digital cameras. Digitally recorded image data show significant advantages to film based data. Digital aerial pho...
Classification is an open, old and basic problem in many domains. Recently, a lot of new methods come forth, such as Bayesian Networks. Bayesian Networks, one of probabilistic networks, are a powerful...
Detailed geo-referenced information on the distribution and occurrence of forest and non-forest vegetation is essential for many different disciplines e.g. forestry, nature conservation, agriculture, ...
The growing needs in obtaining diverse categories of information about Earth surface, including land cover, require the effective and efficient methods of digital analysis to be work out. In contras...
A classification tree based approach for building detection was tested. A digital surface model (DSM) derived from last pulse laser scanner data was first segmented and the segments were classified in...
In the context of 3D building model production or updating, the models have to be manually checked one by one by a human operator in order to ensure their quality. In this paper, we investigate a new ...
In order to adequately model ecosystems services of the urban environment, it is necessary to accurately inventory urban vegetation abundance and spatial distribution. An object-based, methodological ...
One of the most promising approaches to solve the challenging task of urban land-use classification is integration of multi-spectral satellite image data and range data. In this paper, height data ac...
The latest developments in remote sensing analysis software allow new ways of integrating satellite imagery with archive data from aerial photography. This paper shows how this integration is useful i...
Land cover classification is one of the most significant applications in remote sensing. Compared to optical sensing technologies, synthetic aperture radar (SAR) can penetrate through clouds and hav...
Much of Scotland’s uplands are covered by organic peat soils, these form a significant proportion of the global peat resource. Peat ecosystems play a key role in the global carbon cycle through seques...

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