Polyline and building simplification remain challenging in cartography. Most proposed
algorithms are geometric-based and rely on specific rules. In this study, we propose a deep …

Building orientation recognition is a fundamental component of cartographic applications
such as map regularization and map generalization. Most current methods effectively …

Deep learning-backed models have shown their potential of conducting map generalization
tasks. However, pioneering studies for raster-based building generalization encountered a …

Building patterns are crucial for urban form understanding, automated map generalization,
and 3 D city model visualization. The existing studies have recognized various building …

Recognizing morphological patterns in lines and segmenting them into homogeneous
segments is critical for line generalization and other applications. Due to the excessive …

Building outlines are important for emergency response, urban planning, and change
analysis and can be quickly extracted from remote sensing images and raster maps using …

Building simplification is an important research area in automatic map generalization. Up to
now, many approaches have been proposed by scholars. However, in the continuous …

We propose a method to continuously aggregate building footprints into generalized
polygons. This enables smooth zooming that preserves information about dense built-up …

Flow maps are thematic maps that visualize object movements across space with a tree
layout, in which the underlying tree structure is similar to a natural river system. In this paper …