mlsanityData to 13 Oct 2026

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

arXiv 2012.15329 ↗First posted 30 Dec 2020cs.CL cs.AIcited by 22

Not on the map. Why?

Abstract (arXiv)

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-sourced instances that have been verified by human navigation in Street View. Our evaluation shows that the navigation instructions generated by our system have similar properties as human-generated instructions, and lead to successful human navigation in Street View.

Key facts. Cited by 22 papers in the map (data through 13 Oct 2026). First posted on arXiv 30 Dec 2020.

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Citing papers per year: 2022: 3; 2023: 4; 2024: 6; 2025: 5; 2026: 4.

22 papers.

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  1. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 487
  2. 2024Evaluating the World Model Implicit in a Generative Modelcited by 109
  3. 2023VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street Viewcited by 67
  4. 2023Lana: A Language-Capable Navigator for Instruction Following and Generationcited by 39
  5. 2024V-IRL: Grounding Virtual Intelligence in Real Lifecited by 35
  6. 2025CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memorycited by 31
  7. 2022Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor Areascited by 24
  8. 2022Ground then Navigate: Language-guided Navigation in Dynamic Scenescited by 20
  9. 2024NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigationcited by 18
  10. 2024Loc4Plan: Locating Before Planning for Outdoor Vision and Language Navigationcited by 11
  11. 2022DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agentscited by 8
  12. 2025CitySeeker: How Do VLMS Explore Embodied Urban Navigation With Implicit Human Needs?cited by 8
10 more
  1. 2024How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?cited by 7
  2. 2023CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Qualitycited by 3
  3. 2025Mem4Nav: Boosting Vision-and-Language Navigation in Urban Environments with a Hierarchical Spatial-Cognition Long-Short Memory Systemcited by 3
  4. 2026Bridging the Indoor-Outdoor Gap: Vision-Centric Instruction-Guided Embodied Navigation for the Last Meterscited by 3
  5. 2024Into the Unknown: Generating Geospatial Descriptions for New Environmentscited by 1
  6. 2025City Navigation in the Wild: Exploring Emergent Navigation from Web-Scale Knowledge in MLLMscited by 1
  7. 2026360CityArena: A Realistic Virtual Urban Navigation Benchmark for Embodied Agentscited by 1
  8. 2025TurnBack: A Geospatial Route Cognition Benchmark for Large Language Models through Reverse Routecited by 0
  9. 2026Rule-VLN: Bridging Perception and Compliance via Semantic Reasoning and Geometric Rectificationcited by 0
  10. 2026NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulationcited by 0