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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper we address three different aspects of semantic segmentationfrom remote sensor data using deep neural networks. Firstly, we focus on thesemantic segmentation of buildings from remote sensor data and propose ICT-Net.The proposed network has been tested on the INRIA and AIRS benchmark datasetsand is shown to outperform all other state of the art by more than 1.5% and1.8% on the Jaccard index, respectively.  Secondly, as the building classification is typically the first step of thereconstruction process, we investigate the relationship of the classificationaccuracy to the reconstruction accuracy.  Finally, we present the simple yet compelling concept of latent learning andthe implications it carries within the context of deep learning. We posit thata network trained on a primary task (i.e. building classification) isunintentionally learning about auxiliary tasks (e.g. the classification ofroad, tree, etc) which are complementary to the primary task. We extensivelytested the proposed technique on the ISPRS benchmark dataset which containsmulti-label ground truth, and report an average classification accuracy (F1score) of 54.29% (SD=17.03) for roads, 10.15% (SD=2.54) for cars, 24.11%(SD=5.25) for trees, 42.74% (SD=6.62) for low vegetation, and 18.30% (SD=16.08)for clutter.  The source code and supplemental material is publicly available athttp://www.theICTlab.org/lp/2019ICT-Net/.

Semantic Segmentation from Remote Sensor Data and the Exploitation of Latent Learning for Classification of Auxiliary Tasks