Poly-GAN: Regularizing Polygons with Generative Adversarial Networks
Published in Lecture Notes in Computer Science, 2023
This paper introduces Poly-GAN, a data-driven approach utilizing Deep Learning to regularize irregular building footprints from OpenStreetMap data. Poly-GAN aims to optimize polygonal representations of buildings for digital maps, addressing limitations of traditional methods in accuracy and efficiency.
Recommended citation: Niroshan, L. and Carswell, J.D., 2023, June. Poly-GAN: Regularizing Polygons with Generative Adversarial Networks. In International Symposium on Web and Wireless Geographical Information Systems (pp. 179-193). Cham: Springer Nature Switzerland.
Recommended citation: Niroshan, L. and Carswell, J.D., 2023, June. Poly-GAN: Regularizing Polygons with Generative Adversarial Networks. In International Symposium on Web and Wireless Geographical Information Systems (pp. 179-193). Cham: Springer Nature Switzerland. https://lasith-niro.github.io/files/polygan.pdf