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

This paper considers the problem of extracting building footprints fromsatellite imagery -- a task that is critical for many urban planning anddecision-making applications. While recent advancements in deep learning havemade great strides in automated detection of building footprints,state-of-the-art methods available in existing literature often generateerroneous results for areas with densely connected buildings. Moreover, thesemethods do not incorporate the context of neighborhood images during trainingthus generally resulting in poor performance at image boundaries. In light ofthese gaps, we propose a novel Tuning Fork Network (TFNet) design for deepsemantic segmentation that not only performs well for widely-spaced buildingbut also has good performance for buildings that are closely packed together.The novelty of TFNet architecture lies in a a single encoder followed by twoparallel decoders to separately reconstruct the building footprint and thebuilding edge. In addition, the TFNet design is coupled with a novelmethodology of incorporating neighborhood information at the tile boundariesduring the training process. This methodology further improves performance,especially at the tile boundaries. For performance comparisons, we utilize theSpaceNet2 and WHU datasets, as well as a dataset from an area in Lahore,Pakistan that captures closely connected buildings. For all three datasets, theproposed methodology is found to significantly outperform benchmark methods.

TFNet: Tuning Fork Network with Neighborhood Pixel Aggregation for Improved Building Footprint Extraction