Open Access
19 August 2017 Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data
Benjamin Beaumont, Tais Grippa, Moritz Lennert, Sabine G. Vanhuysse, Nathalie R. Stephenne, Eléonore Wolff
Author Affiliations +
Abstract
Encouraged by the EU INSPIRE directive requirements and recommendations, the Walloon authorities, similar to other EU regional or national authorities, want to develop operational land-cover (LC) and land-use (LU) mapping methods using existing geodata. Urban planners and environmental monitoring stakeholders of Wallonia have to rely on outdated, mixed, and incomplete LC and LU information. The current reference map is 10-years old. The two object-based classification methods, i.e., a rule- and a classifier-based method, for detailed regional urban LC mapping are compared. The added value of using the different existing geospatial datasets in the process is assessed. This includes the comparison between satellite and aerial optical data in terms of mapping accuracies, visual quality of the map, costs, processing, data availability, and property rights. The combination of spectral, tridimensional, and vector data provides accuracy values close to 0.90 for mapping the LC into nine categories with a minimum mapping unit of 15  m2. Such a detailed LC map offers opportunities for fine-scale environmental and spatial planning activities. Still, the regional application poses challenges regarding automation, big data handling, and processing time, which are discussed.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Benjamin Beaumont, Tais Grippa, Moritz Lennert, Sabine G. Vanhuysse, Nathalie R. Stephenne, and Eléonore Wolff "Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data," Journal of Applied Remote Sensing 11(3), 036011 (19 August 2017). https://doi.org/10.1117/1.JRS.11.036011
Received: 15 February 2017; Accepted: 21 July 2017; Published: 19 August 2017
Lens.org Logo
CITATIONS
Cited by 13 scholarly publications.
Advertisement
Advertisement
KEYWORDS
Vegetation

Associative arrays

LIDAR

Buildings

Data acquisition

Visualization

Image segmentation

Back to Top