The rapid development of scanning technology, especially mobile scanning, gives the possibility to collect spatial data coming from maritime measurement platforms and autonomous manned or unmanned vehicles. Presented solution is derived from the mobile scanning. However we should keep in mind that the specificity of laser scanning at sea and processing collected data should be in the form acceptable in Geographical Information Systems, especially typical for the maritime needs. At the same time we should be aware that data coming from maritime mobile scanning constitutes a new approach to the describing of maritime environment and brings a new perspective that is completely different than air and terrestrial scanning. Therefore, the authors, would like to present results of an experiment aimed at testing the possibilities of using mobile scanning at sea. Experiment was conducted in the harbour and the associated environment of neighbouring southern coast of the Baltic Sea.

ALS point cloud filtering involves the separation of observations representing the physical terrain surface from those representing terrain details. A digital terrain model (DTM) is created from a subset of points representing the ground surface. The accuracy of the generated DTM is influenced by several factors, including the survey method used, the accuracy of the source data, the applied DTM generation algorithm, and the survey conditions. This article proposes the use of a new estimation method in the filtering of point clouds obtained from airborne laser scanning (ALS), provisionally called M_split-estimation. The application of M_split-estimation in ALS data filtering requires the determination of the appropriate functional model for the surface, which will be used in the filtering of the set of points. A polynomial terrain surface model was selected for this purpose. Two methods of filtering using the M_split method are presented. The first is based on the estimated parameters of the polynomial describing the surface (called the ‘quality’ approach in the article). The second method (provisionally called the ‘quantity’ method) is carried out in two stages. The first stage is point cloud filtering, which results in two subsets being created. One of these is the subset of points intended for DTM creation, while the other contains the remaining points. The second stage of the approach is the creation of a DTM from the first subset. Since the M_split method has an analytical character, the ATIN method was selected to verify the correct operation of the method. The ATIN method is based on computational geometry and uses repeated Delaunay triangulation and statistical evaluation of the geometric parameters. Comparison of M_split with a method based on different principles mitigates errors arising from similarly functioning methods belonging to the same group of filters. The choice of the ATIN method was also dictated by its established position among filtering algorithms. The method is well-known, documented, and verified and this ensures that filtering by this method provides a reliable result that can serve as a reference for comparison with the proposed new filtering method. The theoretical discussion presented in this article was verified with two practical examples. The results obtained from computation by the M_split method with appropriate terrain models encourage more detailed theoretical and empirical tests of this method for the filtering and segmentation of ALS data-sets.