Move and remove: Multi-task learning for building simplification in vector maps with a graph convolutional neural network

Zhiyong Zhou · Cheng Fu · Robert Weibel

30 citations0/0 files storedISPRS Journal of Photogrammetry and Remote Sensing

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 3D Modeling in Geospatial Applications Automated Road and Building Extraction

Abstract

Simplification of building footprints is an essential task in topographic map generalization from large to medium scales. The traditional rule- or constraint-based algorithms commonly require cartographers to enumerate and formalize as many scenarios as possible. Recently, some studies have introduced deep learning to image map generalization, whose outputs, however, may exhibit deformed boundaries due to pure image input. Vector maps are thus a reasonable solution to avoid such issues because of their accurate, object-based geometric representation. However, few existing studies have aimed to simplify buildings in vector maps with the help of neural networks. Building simplification in vector maps can be regarded as the joint contribution from two elementary operations on vertices of building polygons: remove redundant vertices and move kept vertices. This research proposes a multi-task learning method with graph convolutional neural networks. The proposed method formulates the building simplification problem as a joint task of node removal classification and node movement regression. A multi-task graph convolutional neural network model (MT_GCNN) is developed to learn node removal and movement simultaneously. The model was evaluated with a map from Stuttgart, Germany that contains 8494 buildings generalized from the source scale of 1:5,000 to the target scale of 1:10,000. The experimental results show that the proposed method can generate 80% of the buildings with positional errors of less than 0.2 m, 95% with a shape difference under 0.5, and around 98% with an area difference under 0.1 of IoU, compared to the ground truth target buildings, thus demonstrating the feasibility of the proposed method. The code is available at: https://github.com/chouisgiser/MapGeneralizer.

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 — 30 citations exist upstream.

References

2014Quality assessment for building footprints data on OpenStreetMap 1988A review and conceptual framework of automated map generalization 2005Optimization approaches for generalization and data abstraction 2005Developments in Spatial Data Handling 2022A review of location encoding for GeoAI: methods and applications 2010Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems 2019Learning Cartographic Building Generalization with Deep Convolutional Neural Networks 2007Modelling the Overall Process of Generalisation

Cited by

2025Deep learning in automatic map generalization: achievements and challenges 2024A road generalization method using graph convolutional network based on mesh-line structure unit 2023A Heuristic Approach for Resolving Spatial Conflicts of Buildings in Urban Villages 2026GeoAI for map generalization in multi-scale cartography: foundations, a research agenda, and interdisciplinary perspectives 2025Simultaneous selection and displacement of buildings and roads for map generalization via mixed-integer quadratic programming

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.