Addressing the shortcomings of existing ship trajectory compression methods that rely on the empirical setting of a fixed threshold and face challenges in controlling the spatial similarity before and after compression, this paper proposes a self-adaptive compression method for ship trajectories that does not require threshold setting. Initially, a hierarchical and sequential tree structure based on the trajectory characteristics is constructed to determine the importance of a ship’s trajectory points. Subsequently, the dynamic time warping (DTW) operator is introduced to assess the spatial similarity of the trajectory before and after compression, exploring the laws governing similarity variations in the step changes during the compression process from lower to higher levels of the hierarchical and sequential sequence. Finally, the trajectory point that causes the largest step change in similarity within the hierarchical and sequential sequence is identified, and points at lower levels than this point are discarded, thus achieving the objective of self-adaptive adjustment of the level of compression. Our case study results demonstrate that, compared with existing ship trajectory compression methods based on empirically set thresholds, the proposed method has the following advantages: (1) it does not require presetting a fixed threshold and adaptively determines the degree of compression by identifying the trajectory point that leads to the largest step change in similarity, and (2) under the condition of a similar data compression rate, the DTW calculation value is reduced by approximately 25%, significantly enhancing the similarity of the trajectory before and after compression.

Aiming at the problem that the current double direction buffering algorithm is easy to use to seal the “bottleneck” area when simplifying coastlines, an adaptive simplification method for coastlines using a skeleton line “bridge” double direction buffering algorithm is proposed. Firstly, from the perspective of visual constraints, the relationship between the buffer distance and the coastline line width and the minimum recognition distance of the human eye is theoretically derived and determined. Then, based on the construction of the coastline skeleton binary tree, the “bridge” skeleton line is extracted using the “source tracing” algorithm. Finally, the shoreline adaptive simplification is realized by constructing a visual buffer of “bridge” skeleton lines to bridge the original resulting coastline and the local details. The experimental results show that the proposed method can effectively solve the problem that the current double direction buffering algorithm has, which can significantly improve the quality of simplification.