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Map Search

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Jun 25, 2026 · Artificial Intelligence

ReaGeo: The First End‑to‑End LLM Geocoding Framework Linking Precise Mapping and Spatial Correlation

ReaGeo, a novel end‑to‑end geocoding system built on the Qwen2.5‑3B large language model, converts address text directly into Geohash sequences using chain‑of‑thought reasoning and GRPO reinforcement learning, achieving an average error of 119.6 m and 97.2 % accuracy within 500 m on Beijing data, surpassing commercial APIs and academic baselines while also modeling broader spatial correlation for line‑ and area‑type queries.

GeocodingLLMMap Search

0 likes · 15 min read

ReaGeo: The First End‑to‑End LLM Geocoding Framework Linking Precise Mapping and Spatial Correlation

Amap Tech

Nov 29, 2019 · Artificial Intelligence

Advancements in Query Analysis for Map Search: City Analysis, Where‑What Segmentation, and Path Planning

The article details Amap’s upgraded map‑search query analysis, introducing a two‑stage city‑identification system, enhanced where‑what segmentation with CRF and GBDT models, a three‑stage path‑planning pipeline, and outlines future deep‑learning and knowledge‑graph enhancements for robustness and low‑frequency query handling.

Map SearchNLPcity detection

0 likes · 14 min read

Advancements in Query Analysis for Map Search: City Analysis, Where‑What Segmentation, and Path Planning

Amap Tech

Nov 21, 2019 · Artificial Intelligence

Advances in Geographic Text Processing for Map Search: Query Analysis, Error Correction, Rewriting, and Omission

Recent advances in map‑search text processing replace rule‑based pipelines with machine‑learning and deep‑learning models for query analysis, error correction, rewriting, and omission, using phonetic and spatial entity correction, vector‑based similarity, and CRF sequence labeling within a three‑stage architecture of analysis, recall, and ranking to deliver more precise POI results.

Error CorrectionMap SearchNLP

0 likes · 17 min read