RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation

面向以人为本的街道评估的可解释多模态框架：融合视觉-语言模型进行感知性城市诊断

Interpretable Multimodal Framework for Human-Centered Street Assessment: Integrating Visual-Language Models for Perceptual Urban Diagnostics

本文提出融合VisualGLM-6B与GPT-4的多模态街道评估框架MSEF，利用哈尔滨15,000余张街景图像微调，在客观特征F1达0.84、感知一致性89.3%，并能捕捉情境矛盾与非线性感知模式，实现可解释的城市诊断。

Generating Landmark Navigation Instructions from Maps as a Graph-to-Text Problem

Car-focused navigation services are based on turns and distances of named streets, whereas navigation instructions naturally used by humans are centered around physical objects called landmarks. We present a neural model that takes OpenStreetMap representations as input and learns to generate navigation instructions that contain visible and salient landmarks from human natural language instructions. Routes on the map are encoded in a location- and rotation-invariant graph representation that is decoded into natural language instructions. Our work is based on a novel dataset of 7,672 crowd-sour…

StreetviewLLM：利用思维链多模态大语言模型提取地理信息

StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model

本文提出StreetViewLLM框架，将大语言模型与思维链推理及街景图像、地理坐标、文本等多模态数据结合，并利用检索增强生成技术提升地理空间预测精度，在七个全球城市的五项城市指标预测中均优于基线模型。

Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments

We study the problem of jointly reasoning about language and vision through a navigation and spatial reasoning task. We introduce the Touchdown task and dataset, where an agent must first follow navigation instructions in a real-life visual urban environment, and then identify a location described in natural language to find a hidden object at the goal position. The data contains 9,326 examples of English instructions and spatial descriptions paired with demonstrations. Empirical analysis shows the data presents an open challenge to existing methods, and qualitative linguistic analysis shows t…

停车、感知与零售：哈尔滨社区活力街道层面决定因素研究

Parking, Perception, and Retail: Street-Level Determinants of Community Vitality in Harbin

本研究提出基于街景图像与多模态大语言模型的可解释框架，结合美团和大众点评数据构建社区商业活力指数，揭示哈尔滨街道停车、绿化、清洁度与宽度对零售表现和用户满意度的非线性影响。

VELMA：用于街景中视觉与语言导航的 LLM 智能体言语化具身框架

VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View

本文提出 VELMA，通过轨迹与全景视觉观察的言语化提示让 LLM 执行街景 VLN，并在两个数据集上较此前最优方法实现 25%–30% 的相对任务完成率提升。