International audienceNumerous methods have been developed to geolocate fish from data storage tags. Whereas demersal species have been tracked using tide-driven geolocation models, pelagic species which undertake extensive migrations have been mainly tracked using light-based models. Here, we present a new HMM-based model that infers pelagic fish positions from the sole use of high-resolution temperature and depth histories. A key contribution of our framework lies in model parameter inference (diffusion coefficient and noise parameters with respect to the reference geophysical fields - satellite SST and temperatures derived from the MARS3D hydrodynamic model), which improves model robustness. As a case study, we consider long time series of data storage tags deployed on European sea bass for which individual migration tracks are reconstructed for the first time. We performed a sensitivity analysis on synthetic and real data in order to assess the robustness of the reconstructed tracks with respect to model parameters, chosen reference geophysical fields and the knowledge of fish recapture position. Model assumptions and future directions are discussed. Finally, our model opens new avenues for the reconstruction and analysis of migratory patterns of many other pelagic species in relatively contrasted geophysical environments

This paper introduces a novel methodology for automated detection of buildings from single high-resolution optical images with only visible red, green, and blue bands of data. In particular, we first investigate the shadow evidence to focus on building regions. Then, a novel Markov random field (MRF)-based region growing segmentation technique is proposed. Image is oversegmented into smaller homogeneous regions that can be used to replace the rigid structure of the pixel grid. An iterative classification merging is then applied over this set of regions. At each iteration, regions are classified using a region-level MRF model, then, according to the position of shadows, regions having the same class are merged to produce new regions whose shapes are appropriate to rectangles. The final buildings are determined using a recursive minimum bounding rectangle. The experimental results prove that the proposed method is applicable in various areas (high dense urban, suburban, and rural) and is highly robust and reliable.

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