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Despite the great importance of cities, relatively little consistent information about their internal configuration (structure, cover and materials) is available. The World Urban Database and Access Portal Tools (WUDAPT) initiative aims at the acquisition, storage and dissemination of data on the form and function of cities indifferent levels. At the lowest level, the Local Climate Zone (LCZ) scheme provides a basic description of urban structure. This scheme is a climate-based typology of urban and natural landscapes that also provides relevant information on basic physical properties of the landscape, which can be used in modelling and observational studies. The LCZ scheme has large potential as a standard generic description of urban areas. In this paper the scheme and our standard mapping approach are presented, followed by recent improvements and research on object-based image analysis, transferability of trained LCZ classifiers, quality of crowd contributions, and the use of other data sources and methods.

Acacia tortilis subsp. raddiana represents the most important woody species in the pre-Saharan zone. It is the only forest tree persisting on the edge of the desert. Due to tree/environment interactions, canopy sub-habitats arise, enabling an increased storage of soil water, soil nutrients and soil oxygen. Depending on their density, they can also reduce erosion and reverse desertification. Soil erosion and desertification are the main problems faced by the UNESCO Biosphere Reserve in South-Tunisia (Bou-Hedma National Park). The restoration of the original woodland cover to combat desertification (particularly) by afforestation and reforestation of Acacia tortilis goes hand in hand with a climate change in the Biosphere Reserve, also influencing rural population outside the Biosphere Reserve. In order to study the different effects of woodland restoration in Bou-Hedma, the number of Acacia trees and their attributes have to be known. High resolution satellite imagery (GeoEye-1), was used with a GEOBIA approach. Field measurement of bole diameter, crown diameter and tree height were collected at > 400 locations. After segmentation, correlations with > 200 object features and tree attributes were calculated. For crown diameter and tree height, high correlations were observed with the features area and GLCM Entropy Layer 4 (90 degrees). Relations between these features and measured tree attributes were modeled, resulting in RMSE values of resp. 1.47 m and 1.62 m for crown diameter estimation and 0.92 m for tree height. The results show that a GEOBIA working strategy is suitable for estimating tree attributes in open forests in semi-arid regions.

Hyperspectral imagery contains a wealth of spectral and spatial information that can improve target detection and recognition performance. Typically, spectral information is inferred pixel-based, while spatial information related to texture, context and geometry are deduced on a per-object basis. Existing feature extraction methods cannot fully utilize both the spectral and spatial information. Data fusion by simply stacking different feature sources together does not take into account the differences between feature sources. In this paper, we propose a feature fusion method to couple dimension reduction and data fusion of the pixel- and object-based features of hyperspectral imagery. The proposed method takes into account the properties of different feature sources, and makes full advantage of both the pixel- and object-based features through the fusion graph. Experimental results on classification of urban hyperspectral remote sensing image are very encouraging.

In Flanders the large scale reference database called GRB, takes care of the layout, exchange and management of large scale geographic information with respect to, amongst others, roads, buildings and parcels. As Flanders is extremely urbanized (average population density of about 450 inhabitants per square kilometer), the large scale maps need to be highly accurate. Currently, accuracies at the centimeter level are guaranteed due to topographic field measurements aided by standard photogrammetry based on analogue aerial photographs. In order to speed up the GRB production and to ensure large scale map products at the long term, it is essential to automate this labour-intensive, but highly accurate production process. Segmentation of very high resolution digital images could be an alternative approach for maintaining and updating the Flemish GRB as long as high accuracy segmentation results are obtained. Based on DMC images (8 cm ground resolution) and several reference buildings, a comprehensive sensitivity analysis is performed testing different segmentation parameter settings in order to gain insight into their impact on segmentation accuracy. The segmentation quality is evaluated using similarity measures focusing on aspects of presence, shape and positional accuracy where emphasis is placed on interpretability of the measures with respect to operational conditions put on the reference data. The end user should be able to read the measures and link this to the return-on-investment he will gain by using a given segmentation process on his data.