Nathalie Stephenne

An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification

This study presents the development of a semi-automated processing chain for urban object-based land-cover and land-use classification. The processing chain is implemented in Python and relies on existing open-source software GRASS GIS and R. The complete tool chain is available in open access and is adaptable to specific user needs. For automation purposes, we developed two GRASS GIS add-ons enabling users (1) to optimize segmentation parameters in an unsupervised manner and (2) to classify remote sensing data using several individual machine learning classifiers or their prediction combinations through voting-schemes. We tested the performance of the processing chain using sub-metric multispectral and height data on two very different urban environments: Ouagadougou, Burkina Faso in sub-Saharan Africa and Liege, Belgium in Western Europe. Using a hierarchical classification scheme, the overall accuracy reached 93% at the first level (5 classes) and about 80% at the second level (11 and 9 classes, respectively).

Interactivity with the urban information users in the land-use/land-cover information extraction from VHR data

Very High Resolution (VHR) satellite imagery offers a great potential for extracting land-use and land-cover related information for urban areas, but do they meet the requirements of present day urban planners? Assessing user needs for urban land use/land cover data, and investigating the potential of VHR data to better meet these needs is therefore essential. These two parts lead to an interactive definition of remote sensing products in Belgium. This paper presents the background of our analysis (previous surveys at European and French level), the methods that we use to assess the urban users needs (questionnaire and survey), how these can be met by VHR data (classification results) and some preliminary results of the Belgian survey obtained for both the Walloon and Brussels region. Among these results, the survey reports the preference on ortho-rectified aerial photographs when this product is available, a scarce use of remote sensing data explained by spatial resolution and cost reasons, and the lack of awareness of the new VHR images capabilities. As results for the ongoing survey become complete, we hope to better understand what data products derived from VHR imagery can potentially be of interest to users of LU/LC data in Belgium. This will enable us to propose image processing methods that better fulfil the needs of local and regional authorities in Belgium.

An open-source semi-automated processing chain for urban obia classification

This study presents the development of a semi-automated processing chain for OBIA urban land-cover and land-use classification. Implemented in Python and relying on existing open-source software GRASS GIS and R. The complete tool chain is available in open-access and adaptable to specific user needs. For automation purpose, we developed two GRASS GIS add-ons allowing (1) to optimize segmentation parameters in an unsupervised manner and (2) to classify remote sensing data using several individual machine learning classifiers or their predictions combination through voting-schemes. We tested the performance and transferability of the processing chain using sub-metric multispectral and height data on two very different urban environments: Ouagadougou, Burkina Faso in sub-Saharan Africa and Liege, Belgium in Western Europe. Using a hierarchical classification scheme, the kappa values reached for both cities about 0.78 at the second level (9 and 11 classes) and 0.90 at the first level (5 classes).

Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data

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.