# GEO-BLEU: Similarity Measure for Geospatial Sequences

arXiv: https://arxiv.org/abs/2112.07144 · HTML: https://mlsanity.com/paper/2112.07144/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)

## Built on by (most cited first, 7 of 7)

- [ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility Prediction](https://mlsanity.com/paper/2410.14099/) (2024)
- [Llama-Mob: Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction](https://mlsanity.com/paper/2410.23692/) (2024)
- [GeoFormer: Predicting Human Mobility using Generative Pre-trained Transformer (GPT)](https://mlsanity.com/paper/2311.05092/) (2023)
- [Entropy-Driven Curriculum for Multi-Task Training in Human Mobility Prediction](https://mlsanity.com/paper/2509.01613/) (2025)
- [Training Machine Learning Models on Human Spatio-temporal Mobility Data: An Experimental Study [Experiment Paper]](https://mlsanity.com/paper/2508.13135/) (2025)
- [GPS-MTM: Capturing Pattern of Normalcy in GPS-Trajectories with self-supervised learning](https://mlsanity.com/paper/2509.24031/) (2025)
- [Classical Feature Embeddings Help in BERT-Based Human Mobility Prediction](https://mlsanity.com/paper/2510.20275/) (2025)

## Abstract (arXiv)

In recent geospatial research, the importance of modeling large-scale human mobility data and predicting trajectories is rising, in parallel with progress in text generation using large-scale corpora in natural language processing. 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 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.