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This paper illustrates a land consumption map for Italy (year 2017) at a scale 1:1,300,000, and the assessment of its changes (2012–2017). We define land consumption as the replacement of a non-artificial land cover to an artificial land cover, both permanent and no-permanent. The maps are a 10 m spatial resolution raster, produced by photointerpretation of very high resolution images and semiautomatic classification of high resolution remote sensing images. An overall accuracy of 97.7% for the map of 2012 and of 99.66% for the map of 2017 was obtained. The results suggest that the method proposed is appropriate to detect land consumption, both for the urban densification and for the sprawling phenomena, from national to local level. Furthermore, because of the high spatial resolution and the classification scheme adopted, it is suitable for an effective monitoring system, compared to other existing classification systems or monitoring programs. [ABSTRACT FROM AUTHOR]

To overcome the weakness of modelling the relationships between map objects that represent the same geographic entities or phenomena at different times and scales, an agent-based approach is presented to modelling of the spatial evolution of map objects for better understanding of the evolutional process of different maps. Map agents are used to establish and manage the many-to-many corresponding relationships between map objects, and they render map objects active rather than traditionally passive. The evolutions of map objects are modelled by map agents using association and generation relationships to model the connectivity between map objects. This effort reduces the workload of multi-scale map updating by avoiding update of the entire map; instead, it simply allows map agents to detect, perceive and choose suitable behaviours on which to operate and update a spatially evolving object. Experiments of specific examples are presented to demonstrate the feasibility and the effectiveness of our approach. [ABSTRACT FROM PUBLISHER]

The aim of this study is to develop a cost-effective approach for soil sealing change detection integrating radiometric analysis, multi-resolution segmentation and object-based classifiers in two study areas in Italy: Campania region and Veneto region. The integrated approach uses multi-temporal satellite images and CORINE Land Cover (CLC) maps. A good overall accuracy was obtained for the soil sealing maps produced. The results show an improvement in terms of size of the minimum mapping unit and of the changed object (1,44 ha in both cases) in respect to the CLC. The approach proves to be cost-effective given the data which are provided at low or no cost and as well as the level of automation achievable. [ABSTRACT FROM AUTHOR]

In the process of topographic map updating, spatial conflicts (inconsistency) between rivers and contours may be created. This project studies the particularity and complexity of the relationships between rivers and contours, and develops a method for automatic detection of the spatial conflicts between them. This method consists of a refined descriptive spatial model (called a topological chain), an algorithm for the computation of spatial relations, and a set of rules for the determination of spatial conflicts. In the spatial relation model, the topological relationships, order relationships, and metric relationships are integrated to describe the line-line spatial relationships. The rules are derived from the natural relations between rivers and contours in the real world. The effectiveness of this method has been verified by using the national 1:50 000 topographic map databases. An accuracy of 91% has been achieved. [ABSTRACT FROM AUTHOR]