TY - GEN
T1 - Post-analysis of OSM-GAN Spatial Change Detection
AU - Niroshan, Lasith
AU - Carswell, James D.
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Keeping crowdsourced maps up-to-date is important for a wide range of location-based applications (route planning, urban planning, navigation, tourism, etc.). We propose a novel map updating mechanism that combines the latest freely available remote sensing data with the current state of online vector map data to train a Deep Learning (DL) neural network. It uses a Generative Adversarial Network (GAN) to perform image-to-image translation, followed by segmentation and raster-vector comparison processes to identify changes to map features (e.g. buildings, roads, etc.) when compared to existing map data. This paper evaluates various GAN models trained with sixteen different datasets designed for use by our change detection/map updating procedure. Each GAN model is evaluated quantitatively and qualitatively to select the most accurate DL model for use in future spatial change detection applications.
AB - Keeping crowdsourced maps up-to-date is important for a wide range of location-based applications (route planning, urban planning, navigation, tourism, etc.). We propose a novel map updating mechanism that combines the latest freely available remote sensing data with the current state of online vector map data to train a Deep Learning (DL) neural network. It uses a Generative Adversarial Network (GAN) to perform image-to-image translation, followed by segmentation and raster-vector comparison processes to identify changes to map features (e.g. buildings, roads, etc.) when compared to existing map data. This paper evaluates various GAN models trained with sixteen different datasets designed for use by our change detection/map updating procedure. Each GAN model is evaluated quantitatively and qualitatively to select the most accurate DL model for use in future spatial change detection applications.
KW - Generative Adversarial Networks
KW - OpenStreetMap
KW - Remote sensing
KW - Spatial change detection
UR - https://www.scopus.com/pages/publications/85131141206
U2 - 10.1007/978-3-031-06245-2_3
DO - 10.1007/978-3-031-06245-2_3
M3 - Conference contribution
AN - SCOPUS:85131141206
SN - 9783031062445
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 28
EP - 42
BT - Web and Wireless Geographical Information Systems - 19th International Symposium, W2GIS 2022, Proceedings
A2 - Karimipour, Farid
A2 - Storandt, Sabine
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2022
Y2 - 28 April 2022 through 29 April 2022
ER -