## 1 Introduction  
In France, peri-urban development in rural areas, and the development of infrastructure for transport and tourism is made possible by the conversion of agricultural land [1; 2; 3]. Local dynamics (population growth, economic stakes, real estate, the extension of existing infrastructure) and the financial gains to be had by property owners, almost systematically lead to the approval of such land conversion.  
The dynamics underpinning such land conversion are considered to have a minor impact at local scale, but cause greater concern at the regional scale, and even more at the national scale when multiplied by the number of municipalities and accumulation over time. The loss of agricultural land may be irreversible as it would be very difficult and costly (if not impossible or at least economically unrealistic) to subsequently reclaim this land for agriculture. Consequently, this heritage is being destroyed for short term economic gain with no consideration of its collective utility in the medium to long term [1].  
In France, natural areas and aquatic environments are public domain and the need for conservation strategies to protect them has been acknowledged [4].  
In July 2010, a French law for the modernization of agriculture and fishing was passed which included the creation of an observatory for land conversion with the objective of reducing land conversion by 50% by 2020. To implement this law, a method is needed that would enable monitoring of any changes in land conversion at a national scale to help ensure that the objective is met.  
The method must be sufficiently: (i) accurate, to analyze phenomena involved at the municipal/local scale; (ii) exhaustive, to accurately account for changes in artificial land conversion; (iii) reliable and reproducible in homogenous spatial-temporal terms, so that it could be used periodically to evaluate changes in land conversion rates in the country as a whole.  
Remote sensing based on satellite images is an appropriate tool as it enables: (i) land use maps to be generated and the location of urban areas to be determined precisely; (ii) large areas to be processed; and (iii) the processes to be reproduced sufficiently rapidly to monitor any changes in land conversion in a timely manner [5; 6; 7].  
Processing data from satellite images data enables land use objects to be extracted. These then have to be aggregated in such a way that they incorporate all the peripheral areas that characterize each class (for example linking the edges of buildings (gardens, carparks, _etc._)) in the \"built-up\" class. Geographers and urban planners use the urban patch concept [8] as it enables the continuous generation of consistent data on urbanized land. It describes an item of information generated by mathematical morphology processing and gathers together the different component parts or built-up areas (housing, industrial zones, _etc._), in accordance with the criterion \"continuity distance between buildings\".  
We adapted the urban patch concept to peri-urban and rural contexts. We defined the term \"artificial patch\" in which \"artificial\" means built up, in the broadest sense of the term (_i.e._, not only urban areas but also roads and railways, quarries, landfills, _etc._).  
Constituent objects can be extracted either by photo-interpretation or automatically. As our method is intended to be spatially and temporarily reproducible and to be used for vast areas, we opted for automatic classification. This is accomplished in three steps that can generate global exhaustive information on land \"artificialization\" to enable monitoring of land conversion over time: (i) mapping of artificial land based on satellite images; (ii) creation of \"artificial patches\"; and (iii) diachronic analysis of artificial patch maps to characterize artificial sprawl.  
This article presents the methodological approach based on remote sensing and the concept of an artificial patch developed to meet the objectives of the French Ministry of Agriculture.