Managing imprecise map and image data in a possibility theory framework

The representation and combination of imprecise information is an important topic present in many applications. This paper first deals with the representation of imprecise positions of objects detected from maps and images of urban networks. In particular, it deals with the question of the combination of uncertain information, from different sources, to address the problem of inaccuracies related to the geographical coordinates of the detected objects. To illustrate the representation and the combination modes presented in this paper, we focus on wastewater networks data. More precisely, we use the manhole detection problem as an example of object detection in our study. We will use two sources of data: i) the images obtained from the google street view utility and ii) the maps of the sanitation networks. As the geographical positions of the detected objects are imprecise, we will use possibility theory to represent this uncertainty. Possibility theory is particularly suitable for representing qualitative uncertainty, where only the plausibility relation (between the different geographical positions that are candidates to be the actual position of the manholes) is important. Finally, we propose to use two aggregation modes, conjunctive and disjunctive modes, to combine the possibility distributions associated with the detected objects.