BUILDING GENERALIZATION USING DEEP LEARNING

37 citations as recorded by crossref.

1. A point selection method in map generalization using graph convolutional network model T. Xiao et al. https://doi.org/10.1080/15230406.2023.2187886
2. Cartography and Neural Networks: A Scientometric Analysis Based on CiteSpace S. Cheng et al. https://doi.org/10.3390/ijgi13060178
3. Reducing carbon emissions in the architectural design process via transformer with cross-attention mechanism H. Li et al. https://doi.org/10.3389/fevo.2023.1249308
4. Predicting Repair Costs of Residential Facilities Using Deep Learning Algorithms J. Kim et al. https://doi.org/10.3390/buildings16132612
5. Learning Cartographic Building Generalization with Deep Convolutional Neural Networks Y. Feng et al. https://doi.org/10.3390/ijgi8060258
6. Indoor point cloud aggregation and simplification based on parsimony of description F. Freiman et al. https://doi.org/10.1007/s41324-026-00712-w
7. GeoAI for map generalization in multi-scale cartography: foundations, a research agenda, and interdisciplinary perspectives Z. Zhou et al. https://doi.org/10.1080/13658816.2026.2626935
8. Raster-based method for building selection in the multi-scale representation of two-dimensional maps Y. Shen et al. https://doi.org/10.1080/10106049.2021.1943007
9. An intelligent simplification method for river networks with an unsupervised variational autoencoder D. Wang et al. https://doi.org/10.1080/17538947.2025.2495736
10. Data‐driven polyline simplification using a stacked autoencoder‐based deep neural network W. Yu & Y. Chen https://doi.org/10.1111/tgis.12965
11. Determining the optimal generalization operators for building footprints using an improved graph neural network model X. Niu et al. https://doi.org/10.1080/10106049.2024.2306265
12. A LSTM algorithm-driven deep learning approach to estimating repair and maintenance costs of apartment buildings J. Kim et al. https://doi.org/10.1108/ECAM-11-2023-1194
13. Deep Learning for Enrichment of Vector Spatial Databases G. Touya & I. Lokhat https://doi.org/10.1145/3382080
14. Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethics Y. Kang et al. https://doi.org/10.1080/15230406.2023.2295943
15. Measuring the similarity between shapes of buildings using graph edit distance X. Lu et al. https://doi.org/10.1080/17538947.2024.2310749
16. A visual transformer and multi-task learning framework for building generalisation from raster maps J. Yu et al. https://doi.org/10.1080/17538947.2026.2724180
17. Is deep learning the new agent for map generalization? G. Touya et al. https://doi.org/10.1080/23729333.2019.1613071
18. A Progressive Simplification Method for Buildings Based on Structural Subdivision R. Zhai et al. https://doi.org/10.3390/ijgi11070393
19. Detection of Collapsed Bridges from Multi-Temporal SAR Intensity Images by Machine Learning Techniques W. Liu et al. https://doi.org/10.3390/rs13173508
20. Road Network Selection Improvement Using Graph Convolutional Networks for Small-Scale Mapping A. Adolf et al. https://doi.org/10.3390/app162211114
21. Automatic simplification of the geometry of a cartographic line using contractive self-mapping – illustrated with an example of a polyline band J. Bac-Bronowicz et al. https://doi.org/10.2478/pcr-2023-0007
22. A Transformer Network for Settlement Generalization Integrating Raster Maps and Remote Sensing Images H. Han et al. https://doi.org/10.3390/rs18203467
23. Multi-Criteria Geographic Analysis for Automated Cartographic Generalization G. Touya https://doi.org/10.1080/00087041.2020.1858608
24. Review of road selection methods for the purpose of multiscale mapping A. Adolf & I. Karsznia https://doi.org/10.2478/mgrsd-2025-0031
25. A polygonal buildings aggregation method considering obstacle elements and visual clarity F. Zhang et al. https://doi.org/10.1080/10106049.2023.2266672
26. A new model combining building block displacement and building block area reduction for resolving spatial conflicts P. Pilehforooshha et al. https://doi.org/10.1111/tgis.12731
27. Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection E. Al-Sakkari et al. https://doi.org/10.1016/j.dche.2024.100207
28. Polyline simplification based on the artificial neural network with constraints of generalization knowledge J. Du et al. https://doi.org/10.1080/15230406.2021.2013944
29. Make It Simple: Effective Road Selection for Small-Scale Map Design Using Decision-Tree-Based Models I. Karsznia et al. https://doi.org/10.3390/ijgi11080457
30. Polyline simplification using a region proposal network integrating raster and vector features B. Jiang et al. https://doi.org/10.1080/15481603.2023.2275427
31. Automatic Building Aggregation Supported by Knowledge Graph Y. Su et al. https://doi.org/10.1111/tgis.70007
32. Keeping walls straight: data model and training set size matter for deep learning in building generalization C. Fu et al. https://doi.org/10.1080/15230406.2023.2264757
33. Deep learning in automatic map generalization: achievements and challenges X. Yan et al. https://doi.org/10.1080/10095020.2025.2480815
34. Turistik amaçlı mekânsal sanal ortamların oluşturulması: Tarihi Yarımada Örneği S. BUHUR et al. https://doi.org/10.29128/geomatik.1133484
35. Segmentation and sampling method for complex polyline generalization based on a generative adversarial network J. Du et al. https://doi.org/10.1080/10106049.2021.1878288
36. End-to-End Vector Simplification for Building Contours via a Sequence Generation Model L. Cui et al. https://doi.org/10.3390/ijgi14030124
37. A Scale-Adaptive Framework for Building Generalization via Instruction-Guided Diffusion and Geometry-Aware Vectorization K. Fan et al. https://doi.org/10.1007/s41651-026-00282-3