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/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

Continuous generalization is a key technology for achieving continuous-scale representation of map features, with continuous building simplification being an important component. Existing methods often suffer from issues such as misalignment of feature points, disruption of local similarity, and a lack of hierarchical simplification. To address these challenges, this paper proposes a continuous building simplification model based on a conditional diffusion model. The model adopts an architecture comprising a PSRT (Pre-Trained Shape Feature Representations from Transformers) encoder and a BART (Bidirectional and Auto-Regressive Transformers) decoder, trained with sequences of building contour coordinate points as the target output and corresponding sequences of building control points as conditional input. After training, continuous building simplification is achieved by combining latent space interpolation with initial noise interpolation. Experimental results demonstrate that the proposed method can generate reasonable and coherent building simplification results at intermediate scales.

DiffSimplify: a building continuous simplification model based on conditional diffusion models