Abstract
The research work presented in this paper has been funded by a national research project whose aims are to enable an Unmanned Aerial Vehicle (UAV) to fly autonomously with the use of a Digital Elevation Model (DEM) of the target area and to detect terrain changes with the use of a 3D Structure Change Detection Model (3D SCDM). A Convolutional Neural Network (CNN) works with both models in training the UAV in autonomous flying and in detecting terrain changes. The usability of such an autonomous flying IoT is demonstrated through its deployment in the search for water resources in areas where a satellite would not normally be able to retrieve images, e.g., inside gorges, ravines, or caves. Our experiment results show that it can detect water flows by considering different surface shapes such as standing water polygons, watersheds, water channel incisions, and watershed delineations with a 99.6% level of accuracy.
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