Humans subconsciously engage in geospatial reasoning when reading articles.
We recognize place names and their spatial relations in text and mentally
associate them with their physical locations on Earth. Although pretrained
language models can mimic this cognitive process using linguistic context, they
do not utilize valuable geospatial information in large, widely available
geographical databases, e.g., OpenStreetMap. This paper introduces GeoLM, a
geospatially grounded language model that enhances the understanding of
geo-entities in natural language. GeoLM leverages geo-entity mentions as
anchors to connect linguistic information in text corpora with geospatial
information extracted from geographical databases. GeoLM connects the two types
of context through contrastive learning and masked language modeling. It also
incorporates a spatial coordinate embedding mechanism to encode distance and
direction relations to capture geospatial context. In the experiment, we
demonstrate that GeoLM exhibits promising capabilities in supporting toponym
recognition, toponym linking, relation extraction, and geo-entity typing, which
bridge the gap between natural language processing and geospatial sciences. The
code is publicly available at this https URL

GeoLM 是一个地理信息语言模型，通过连接文本语料库中的语言信息和来自地理数据库的地理信息，利用对比学习和遮蔽语言建模的方法，结合空间坐标嵌入机制来捕捉地理空间上下文，从而提升自然语言中的地理实体的理解能力。实验证明 GeoLM 在地名识别、地名链接、关系提取和地理实体类型等方面具有良好的能力，弥合了自然语言处理和地理空间科学之间的差距。

GeoLM：地理空间语言理解模型的增强

GeoLM: Empowering Language Models for Geospatially Grounded Language  Understanding

Geospatial sciences include a wide range of applications, from environmental
monitoring transportation to infrastructure planning, as well as location-based
analysis and services. Graph theory algorithms in mathematics have emerged as
indispensable tools in these domains due to their capability to model and
analyse spatial relationships efficiently. This technical report explores the
applications of graph theory algorithms in geospatial sciences, highlighting
their role in network analysis, spatial connectivity, geographic information
systems, and various other spatial problem-solving scenarios. It provides a
comprehensive idea about the key concepts and algorithms of graph theory that
assist the modelling processes. The report provides insights into the practical
significance of graph theory in addressing real-world geospatial challenges and
opportunities. It lists the extensive research, innovative technologies and
methodologies implemented in this field.

该技术报告探讨了图论算法在地理空间科学中的应用，并突出了它们在网络分析、空间连接性、地理信息系统以及其他空间问题解决方案中的作用。它提供了有关辅助模型过程的图论的关键概念和算法的全面了解。这份报告提供了图论在解决现实世界地理空间挑战和机遇方面的实际意义的见解，并列出了在该领域实施的广泛研究、创新技术和方法学。