@inbook{97d32784e9c14873bb5396ddd11202af,
title = "OSM-GAN: Using Generative Adversarial Networks for Detecting Change in High-Resolution Spatial Images",
abstract = "Detecting changes to built environment objects such as buildings/roads/etc. in aerial/satellite (spatial) imagery is necessary to keep online maps and various value-added LBS applications up-to-date. However, recognising such changes automatically is not a trivial task, and there are many different approaches to this problem in the literature. This paper proposes an automated end-to-end workflow to address this problem by combining OpenStreetMap (OSM) vectors of building footprints with a machine learning Generative Adversarial Network (GAN) model - where two neural networks compete to become more accurate at predicting changes to building objects in spatial imagery. Notably, our proposed OSM-GAN architecture achieved over 88% accuracy predicting/detecting building object changes in high-resolution spatial imagery of Dublin city centre.",
keywords = "Change detection, Generative Adversarial Networks, GIS, OpenStreetMap, Remote sensing",
author = "Lasith Niroshan and Carswell, {James D.}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2022",
doi = "10.1007/978-3-031-08017-3_9",
language = "English",
series = "Lecture Notes on Data Engineering and Communications Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "95--105",
booktitle = "Lecture Notes on Data Engineering and Communications Technologies",
address = "Germany",
}