English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The precise classification and reconstruction of crossroads from multiple aerial images is a challenging problem in remote sensing.We apply the Conditional Random Fields (CRF) approach to this problem, a probabilistic model that can be used to consider contextin classification. A simple appearance-based model is combined with a probabilistic model of the co-occurrence of class label atneighbouring image sites to distinguish classes that are relevant for scenes containing crossroads. The parameters of these models arelearnt from training data. We use multiple overlap aerial images to derive a digital surface model (DSM) and a true orthophoto withoutmoving cars. From the DSM and the orthophoto we derive feature vectors that are used in the classification. Within our framework wemake use of a car detector based on support vector machines (SVM), which delivers car probability values. These values are used asadditional feature to support the classification when the road surface is occluded by static cars. Our approach is evaluated on a datasetof airborne photos of an urban area by a comparison of the results to reference data. The evaluation is performed for images of differentresolution. The method is shown to produce promising results when using the car probability values and higher image resolution

The Application of a Car Confidence Feature for the Classification of Cross-Roads Using Conditional Random Fields