Road network in the Mekong Delta is mostly coastal and river routes, then they are often flooded when the floods come in. As a result, the foundation and pavement are destroyed, reduced life expectancy, resulting in unsafety in traffic, cost of maintenance and repairs…. This paper establishes the technical conditions for the calculation on the flexible pavement working in the wet conditions (so flooded) based on the maximum usage of available materials in the provinces in the Mekong Delta. Simultaneously, we propose the flooded flexible pavement under the current climate change conditions.

Planetary global localization is necessary for long-range rover missions in which communication with command center operator is throttled due to the long distance. There has been number of researches that address this problem by exploiting and matching rover surroundings with global digital elevation maps (DEM). Using conventional methods for matching, however, is challenging due to artifacts in both DEM rendered images, and/or rover 2D images caused by DEM low resolution, rover image illumination variations and small terrain features. In this work, we use train CNN discriminator to match rover 2D image with DEM rendered images using conditional Generative Adversarial Network architecture (cGAN). We then use this discriminator to search an uncertainty bound given by visual odometry (VO) error bound to estimate rover optimal location and orientation. We demonstrate our network capability to learn to translate rover image into DEM simulated image and match them using Devon Island dataset. The experimental results show that our proposed approach achieves ~74% mean average precision.