La Géographie française et le risque d'inondation: de la géographie physique à la géographie humaine

Les inondations, comme tous les risques naturels, ont été un thème qui est revenu souvent au long de l'histoire de la géographie française, mais toujours — et contrairement à ce qui se passe dans la géographie anglo-saxonne —, en relation à la géographie physique. Dans cet article, nous voulons expliquer les raisons de la présence de la géographie physique dans le traitement du risque d'inondation : nous mettons en relation les différents moments de la géographie française avec l'analyse du risque d'inondation et révisons le contenu des références aux inondations entre les géographes ou écoles géographiques les plus représentatifs

Towards Detecting Changes in Underwater Image Sequences

Detecting changes between images of the same scene taken at different times is of great interest for monitoring and understanding the environment. It is widely used for on-land application but suffers from different constraints. Unfortunately, Change detection algorithms require highly accurate geometric and photometric registration. This requirement has precluded their use in underwater imagery in the past. In this paper, the change detection techniques available nowadays for on-land application were analyzed and a method to automatically detect the changes in sequences of underwater images is proposed. Target application scenarios are habitat restoration sites, or area monitoring after sudden impacts from hurricanes or ship groundings. The method is based on the creation of a 3D terrain model from one image sequence over an area of interest. This model allows for synthesizing textured views that correspond to the same viewpoints of a second image sequence. The generated views are photometrically matched and corrected against the corresponding frames from the second sequence. Standard change detection techniques are then applied to find areas of difference. Additionally, the paper shows that it is possible to detect false positives, resulting from non-rigid objects, by applying the same change detection method to the first sequence exclusively. The developed method was able to correctly find the changes between two challenging sequences of images from a coral reef taken one year apart and acquired with two different cameras