Geospatial Location Embedding (GLE) helps a Large Language Model (LLM)
assimilate and analyze spatial data. GLE emergence in Geospatial Artificial
Intelligence (GeoAI) is precipitated by the need for deeper geospatial
awareness in our complex contemporary spaces and the success of LLMs in
extracting deep meaning in Generative AI. We searched Google Scholar, Science
Direct, and arXiv for papers on geospatial location embedding and LLM and
reviewed articles focused on gaining deeper spatial "knowing" through LLMs. We
screened 304 titles, 30 abstracts, and 18 full-text papers that reveal four GLE
themes - Entity Location Embedding (ELE), Document Location Embedding (DLE),
Sequence Location Embedding (SLE), and Token Location Embedding (TLE).
Synthesis is tabular and narrative, including a dialogic conversation between
"Space" and "LLM." Though GLEs aid spatial understanding by superimposing
spatial data, they emphasize the need to advance in the intricacies of spatial
modalities and generalized reasoning. GLEs signal the need for a Spatial
Foundation/Language Model (SLM) that embeds spatial knowing within the model
architecture. The SLM framework advances Spatial Artificial Intelligence
Systems (SPAIS), establishing a Spatial Vector Space (SVS) that maps to
physical space. The resulting spatially imbued Language Model is unique. It
simultaneously represents actual space and an AI-capable space, paving the way
for AI native geo storage, analysis, and multi-modality as the basis for
Spatial Artificial Intelligence Systems (SPAIS).

Geospatial Location Embedding (GLE) 在大型语言模型（LLM）中帮助模型吸收和分析空间数据。GLE 的出现是由于在当代复杂空间中对更深层次的地理空间意识的需求以及 LLMs 在生成式人工智能方面提取深层含义的成功。GLE 为对空间的理解提供了帮助，但强调了在空间模态和推理方面进一步的提升的需求。GLE 标志着 “空间” 和 “LLM” 之间对话式对接，为建立将空间知识嵌入模型结构的 Spatial Foundation/Language Model（SLM）框架提供了契机。该框架推动了 Spatial Artificial Intelligence Systems（SPAIS）的发展，建立了一个与物理空间相映射的空间向量空间（SVS）。由此得到的具有空间特征的语言模型是独特的，同时代表了实际空间和基于人工智能的空间，为 SPAIS 的地理存储、分析和多模态提供了基础。

大规模语言模型中地理位置嵌入方法的系统回顾：通向空间人工智能系统的路径

A systematic review of geospatial location embedding approaches in large  language models: A path to spatial AI systems

Visual Place Recognition (VPR) is often characterized as being able to
recognize the same place despite significant changes in appearance and
viewpoint. VPR is a key component of Spatial Artificial Intelligence, enabling
robotic platforms and intelligent augmentation platforms such as augmented
reality devices to perceive and understand the physical world. In this paper,
we observe that there are three "drivers" that impose requirements on spatially
intelligent agents and thus VPR systems: 1) the particular agent including its
sensors and computational resources, 2) the operating environment of this
agent, and 3) the specific task that the artificial agent carries out. In this
paper, we characterize and survey key works in the VPR area considering those
drivers, including their place representation and place matching choices. We
also provide a new definition of VPR based on the visual overlap -- akin to
spatial view cells in the brain -- that enables us to find similarities and
differences to other research areas in the robotics and computer vision fields.
We identify numerous open challenges and suggest areas that require more
in-depth attention in future works.

本文通过调查与研究 Visual Place Recognition（VPR）的关键要素，提出一种基于视觉重叠的新的 VPR 定义，并指出未来需要深入关注的挑战和领域。