Abstract
Abstract. In many countries digital maps are generally created and provided by Cadastre, Land Registry or National Mapping Agencies. These maps must be accurate and well maintained. However, in most cases, the update process of these maps is still done by hand, often using satellite or aerial imagery. Supporting this process via automatic change detection based on traditional classification algorithms is difficult due to the high level of noise in the data, such as introduced by temporary changes (e.g. cars being parked). This paper describes a method to detect changes between two time steps using 2.5D data and to transfer these insights to a digital map. For every polygon in the map, several attributes are collected from the input data, which are used to train a machine-learning model based on gradient boosting. A case study in Haarlem, in the Netherlands, was conducted to test the performance of this proposed approach. Results show that this methodology can recognize a substantial amount of changes and can support – and speed up – the manual updating process.
Highlights
Up-to-date digital maps are required for many applications in academia, government and industry, and they are produced and offered by many companies and governments worldwide
The goal of this paper is to find out to what extent change detection can be automated using gradient boosting, focusing on XGBoost
Change detection is a common topic in remote sensing and Geographic Information Systems (GIS) and is used in many applications for academia and industry
Summary
Up-to-date digital maps are required for many applications in academia, government and industry, and they are produced and offered by many companies and governments worldwide. 2. digital maps are static and can only display a snapshot of an area at a certain time Keeping these maps updated is vital so that they remain useful. Existing maps are compared with aerial images and used to change the digital map This process is a tedious work, can take long time and some changes are not detected by the operators. Change detection can be separated in pixelbased and object-based methods ( ̇Ilsever and Unsalan, 2012; Taubenbock et al, 2012; Shi et al, 2012). The latter method is the one used in this paper, using polygons as objects. An example of successful change detection using XGBoost—which is
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