Publications
カンファレンス (国際) GEO-BLEU: Similarity Measure for Geospatial Sequences
Toru Shimizu, Kota Tsubouchi, Takahiro Yabe (Purdue University)
30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022)
2022.11.4
In recent geospatial research, the importance of modeling and generating human mobility trajectories is rising. Whereas there are already plenty of feasible approaches applicable to geospatial sequence modeling itself, there seems to be room to improve with regard to evaluation, specifically about measuring the similarity between generated and reference trajectories. In this work, we propose a novel similarity measure, GEO-BLEU, which can be especially useful in the context of geospatial sequence modeling and generation. As the name suggests, this work is based on BLEU, one of the most popular measures used in machine translation research, while introducing spatial proximity to the idea of n-gram. We compare this measure with an established method, dynamic time warping, applying both measures to simple artificial sequences and examining differences in their characteristics.
Paper : GEO-BLEU: Similarity Measure for Geospatial Sequences （外部サイト）