Abstract
Abstract. The Walloon region of Belgium has launched a research project that aims at elaborating a methodology for automated, high-quality land cover mapping, based primarily on its yearly 0.25m orthophoto coverage. Whereas in urban areas an object-based (OBIA) approach has been the privileged path in the last years as it allows taking into account shape information relevant for the characterization of man-made constructions, such an approach has its limits in the rural and more natural areas due to increased difficulties for segmentation and less sharp boundaries, thus calling for a pixel-based approach. The project thus consists in developing a combination of methods, and to integrate their results through an ensemble fusion approach. As many of the more natural land cover classes have temporal profiles which cannot be detected in a one-date orthoimage, Sentinel 1 and 2 data are also included in order to take advantage of their higher spectral and temporal resolution. All methods are trained using existing regional databases. In a second step, we combine the different LC classification results by fusioning them into one high-accuracy (over 90% OA) product, using a series of different approaches ranging from rule-based to machine learning to the Dempster-Shafer method. The entire toolchain is based on free and open source software, mainly GRASS GIS and Orfeo ToolBox. Results indicate the importance of the quality of the individual classifications for the fusion results and justify the choice of combining OBIA and pixel-based approaches in order to avoid the pitfalls of each.
Highlights
IntroductionEven though the Walloon region in Belgium has compiled a rich catalogue of vector geodata, the actual Land cover (LC) map currently available dates back over a decade and an update was needed
Land cover (LC) maps, showing the characteristics of surface elements, e.g. vegetation, artificial constructions, water, etc, are essential components for regional decision-making, for uses as diverse as spatial planning, environmental monitoring and modelling, flood risk assessment, etc.Even though the Walloon region in Belgium has compiled a rich catalogue of vector geodata, the actual LC map currently available dates back over a decade and an update was needed
We present a small selection of results of the very high resolution (VHR) pixel-based and OBIA approaches in order to illustrate some difficulties in each
Summary
Even though the Walloon region in Belgium has compiled a rich catalogue of vector geodata, the actual LC map currently available dates back over a decade and an update was needed. The regional administration decided to launch a research project to develop a robust, automatized, scalable and reproducible method for creating these data, mainly based on the available VIS-NIR orthoimagery at 0.25 m resolution, as well as height information derived through photogrammetry from the raw version of that same imagery. The ultimate aim of the project is to provide recent (2018), INSPIREcompliant maps, and to elaborate a method that would make it easier for the region to reproduce such data at higher temporal frequency than in the past, ideally based on FOSS4G software in order to avoid vendor lock-in and licence costs. We begin with a very short overview of the current state-of-the-art in LC mapping, to go on to describing the data, methods and intermediate results, before discussing the lessons already learned
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