Data‐driven polyline simplification using a stacked autoencoder‐based deep neural network

Wenhao Yu · Yu-Jie Chen

34 citations0/0 files storedTransactions in GIS

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Geographic Information Systems Studies Neural Networks and Applications Data Management and Algorithms

Abstract

Abstract Automatic simplification of polylines is an important issue in spatial database and mapping. Traditional rule‐based methods are usually limited in performance, especially when the man‐made rules have to be adapted to different polylines with different shapes and structures. Compared to the existing neural network methods focusing only on the output layer or the code layers for classification or regression, our proposed method generates multi‐level abstractions of polylines by extracting features from multiple hidden layers. Specifically, we first organize the cartographic polylines into the form of feature vectors acceptable to the neural network model. Then, a stacked autoencoder‐based deep neural network model is trained to learn the pattern features of polyline bends and omit unimportant details layer by layer. Finally, the multi‐level abstractions of input polylines are generated from different hidden layers of a single model. The experimental results demonstrate that, compared with the classic Douglas–Peucker and Wang and Muller algorithms, the proposed method is able to properly simplify the polylines while representing their essential shapes smoothly and reducing areal displacement.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 34 citations exist upstream.

References

2019GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond 1993Line generalisation by repeated elimination of points 2020GeoAI: Where machine learning and big data converge in GIScience 1992Algorithms for automated line generalization1based on a natural principle of objective generalization 1998Line Generalization Based on Analysis of Shape Characteristics 2019Learning Cartographic Building Generalization with Deep Convolutional Neural Networks 2019Is deep learning the new agent for map generalization?2016Envelope generation and simplification of polylines using Delaunay triangulation

Cited by

2024Machine learning in cartography 2023Move and remove: Multi-task learning for building simplification in vector maps with a graph convolutional neural network 2023A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints 2023A point selection method in map generalization using graph convolutional network model 2025Deep learning in automatic map generalization: achievements and challenges

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.