A reinforcement learning method for collaborative generalization of soundings and depth contours

Nowadays, the existing methods of automatic cartographic generalization usually generalize soundings and depth contours separately, which easily leads to unsatisfactory generalization results. To address this problem, a reinforcement learning method for collaborative generalization of soundings and depth contours is proposed. Firstly, training samples for collaborative generalization are obtained. Simultaneously, a reinforcement learning model is constructed based on the cartographic constraints and the related algorithms. Then, the constructed model is trained by using the sample data, so that the interaction between soundings and depth contours can be explored in the generalization process. Finally, the generalization algorithms of soundings and depth contours can be adaptively adjusted by utilizing the trained model, so that the mutual influence relationship between soundings and depth contours can be fully considered in the generalization process. The experimental results show that: compared with current common methods, the proposed method can effectively improve the quality of the cartographic generalization results, and is more suitable for the collaborative generalization of soundings and depth contours. © 2024 SinoMaps Press. All rights reserved.