TY - GEN
T1 - Poly-GAN
T2 - 20th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2023
AU - Niroshan, Lasith
AU - Carswell, James D.
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Regularizing polygons involves simplifying irregular and noisy shapes of built environment objects (e.g. buildings) to ensure that they are accurately represented using a minimum number of vertices. It is a vital processing step when creating/transmitting online digital maps so that they occupy minimal storage space and bandwidth. This paper presents a data-driven and Deep Learning (DL) based approach for regularizing OpenStreetMap building polygon edges. The study introduces a building footprint regularization technique (Poly-GAN) that utilises a Generative Adversarial Network model trained on irregular building footprints and OSM vector data. The proposed method is particularly relevant for map features predicted by Machine Learning (ML) algorithms in the GIScience domain, where information overload remains a significant problem in many cartographic/LBS applications. It addresses the limitations of traditional cartographic regularization/generalization algorithms, which can struggle with producing both accurate and minimal representations of multisided built environment objects. Furthermore, future work proposes a way to test the method on even more complex object shapes to address this limitation.
AB - Regularizing polygons involves simplifying irregular and noisy shapes of built environment objects (e.g. buildings) to ensure that they are accurately represented using a minimum number of vertices. It is a vital processing step when creating/transmitting online digital maps so that they occupy minimal storage space and bandwidth. This paper presents a data-driven and Deep Learning (DL) based approach for regularizing OpenStreetMap building polygon edges. The study introduces a building footprint regularization technique (Poly-GAN) that utilises a Generative Adversarial Network model trained on irregular building footprints and OSM vector data. The proposed method is particularly relevant for map features predicted by Machine Learning (ML) algorithms in the GIScience domain, where information overload remains a significant problem in many cartographic/LBS applications. It addresses the limitations of traditional cartographic regularization/generalization algorithms, which can struggle with producing both accurate and minimal representations of multisided built environment objects. Furthermore, future work proposes a way to test the method on even more complex object shapes to address this limitation.
KW - Generative Adversarial Networks
KW - Geographic Information System
KW - OpenStreetMap
KW - Polygon Regularization
UR - http://www.scopus.com/inward/record.url?scp=85163976033&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-34612-5_13
DO - 10.1007/978-3-031-34612-5_13
M3 - Conference contribution
AN - SCOPUS:85163976033
SN - 9783031346118
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 179
EP - 193
BT - Web and Wireless Geographical Information Systems - 20th International Symposium, W2GIS 2023, Proceedings
A2 - Mostafavi, Mir Abolfazl
A2 - Del Mondo, Géraldine
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 12 June 2023 through 13 June 2023
ER -