Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GEO-BLEU: Similarity Measure for Geospatial Sequences   In recent geospatial research, the importance of modeling large-scale human
mobility data via self-supervised learning is rising, in parallel with progress
in natural language processing driven by self-supervised approaches using
large-scale corpora. 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 how to measure the
similarity between generated and reference sequences. 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 baseline, dynamic time
warping, applying it to actual generated geospatial sequences. Using
crowdsourced annotated data on the similarity between geospatial sequences
collected from over 12,000 cases, we quantitatively and qualitatively show the
proposed method's superiority.

12
The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
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The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat