## 5 Conclusions  
In this paper, smart low-cost sensors implementable via smartphones were used in order to obtain bathymetric data and an original method for 3D modelling of natural and artificial underwater environments was developed.  
The main topics addressed in this research were:  
1. Evaluation via analytical tests of the performance of the Deeper Smart Sonars CHIRP+ and Pro+ 2 in surveys conducted in shallow water;
2. Development and experimentation of a system to synchronise the Deeper Smart Sonars with an external GNSS receiver via smartphone to improve sonar geolocation in real-time and post-processing;
3. Experimentation of a mapping method based on an ML approach;
4. Implementation of a pipeline for 3D modelling of underwater natural surfaces and artificial structures.  
Based on the results obtained, it is possible to deduce that Deeper Smart Sonars are excellent sensors for conducting surveys in natural aquatic environments, characterized by regular seabed for scientific applications, e.g., in biology, geology, climatology, etc. In the case of surveys of artificial structures underwater, detailed surveys of natural environments close to the coast and/or with a complex bottom, or in the case of data fusion between aquatic and terrestrial environments, it would be preferable to use the G > S > P Sys with spacetime PPK synchronisation to achieve better performances in the geolocation of bathymetric data and in the subsequent 3D modelling.  
In this case study, the ML based mapping method proved to be able to achieve comparable if not superior results to established interpolation methods. An advantage of ML mapping is the automaticity of the method compared to the classical geostatistical methods. In future research, further studies will be conducted, e.g., varying the morphology and nature of the surface to be interpolated, in order to accumulate further data in different scenarios.  
The proposed 3D modelling pipeline has proven to be flexible, easily implemented, and capable of producing models with centimetric accuracy and precision. The opportunity to import bathymetric data into MeshLab and Rhinoceros(r) 7 allows 3D modelling by various algorithms depending on the goals. Furthermore, the import into CloudCompare can allow the geometric analysis of data via eigenfeatures analysis [42]. Finally, it is believed that the modelling of sonar data in specific software, such as CloudCompare, MeshLab and Rhinoceros(r) 7, would allow in future research an easier approach to data fusion collected with different techniques (photogrammetry, Light Detection And Ranging--LiDAR, etc.) and in different environments (water, ground, etc.)  
Conceptualization, G.V., D.C., M.P. and V.S.A.; methodology, G.V., D.C., M.P. and V.S.A.; software, G.V., D.C., M.P. and V.S.A.; validation, G.V., D.C., M.P. and V.S.A.; formal analysis, G.V., D.C., M.P. and V.S.A.; investigation, G.V., D.C., M.P. and V.S.A.; resources, G.V., D.C., M.P. and V.S.A.; data curation, G.V., D.C., M.P. and V.S.A.; writing--original draft preparation, G.V., D.C., M.P. and V.S.A.; writing--review and editing, G.V., D.C., M.P. and V.S.A.; visualization, G.V., D.C., M.P. and V.S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.