Geocoding converts unstructured geographic text into structured spatial data, which is crucial in fields such as urban planning, social media spatial analysis, and emergency response systems. Existing approaches predominantly model geocoding as a geographic grid classification task but struggle with the output space dimensionality explosion as the grid granularity increases. Furthermore, these methods generally overlook the inherent hierarchical structure of geographical texts and grids. In this paper, we propose a hierarchy-aware geocoding model based on cross-attention within the Seq2Seq framework, incorporating S2 geometry to model geocoding as a task for generating grid labels and predicting S2 tokens (labels of S2 grids) character-by-character. By incorporating a cross-attention mechanism into the decoder, the model dynamically perceives the address contexts at the hierarchical level that are most relevant to the current character prediction based on the input address text. Results show that the proposed model significantly outperforms previous approaches across multiple metrics, with a median and mean distance error of 41.46 m and 93.98 m, respectively. Furthermore, our method achieves superior results compared to others in regions with sparse data distribution, reducing the median and mean distance error by 16.27 m and 7.52 m, respectively, suggesting that our model has effectively mitigated the issue of insufficient learning in such regions.

中文翻译：

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Seq2Seq 框架内基于交叉注意力的层次感知地理编码模型

地理编码将非结构化地理文本转换为结构化空间数据，这在城市规划、社交媒体空间分析和应急响应系统等领域至关重要。现有方法主要将地理编码建模为地理网格分类任务，但随着网格粒度的增加，输出空间维数爆炸。此外，这些方法通常忽略了地理文本和网格的固有层次结构。在本文中，我们提出了一种基于 Seq2Seq 框架内的交叉注意力的层次感知地理编码模型，将 S2 几何模型纳入地理编码模型，作为生成网格标签和逐个字符预测 S2 标记（S2 网格的标签）的任务。通过将交叉注意力机制合并到解码器中，模型可以根据输入地址文本动态感知与当前字符预测最相关的分层地址上下文。结果表明，所提出的模型在多个指标上显着优于以前的方法，中值和平均距离误差分别为 41.46 m 和 93.98 m。此外，我们的方法在数据分布稀疏的区域中取得了优于其他方法的结果，中值和平均距离误差分别减少了16.27 m和7.52 m，这表明我们的模型有效缓解了这些区域学习不足的问题。