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Gaode Maps leverages advanced scene text recognition, evolving from traditional image processing to deep‑learning based detection and recognition pipelines, integrating multi‑stage models, data augmentation, and synthetic sample generation to achieve high‑accuracy, fast POI and road data automation.
Gaode’s continuous delivery journey transformed its multi‑stage release process into a standardized, automated pipeline that delivers backend services within an hour, cuts manual effort, boosts release efficiency by up to 90%, and embeds early quality checks and data‑driven insights for faster, higher‑quality business value.
The article describes how Gaode handles the challenges of supporting over 100 million daily active users by applying capacity planning, traffic control, disaster recovery, monitoring, rehearsal, and a self‑built full‑link load‑testing platform that simulates realistic traffic, manages resources, and provides detailed reporting to guarantee system stability.
Gaode Maps uses a two-stage machine‑learning pipeline (Faster‑RCNN with shape‑based region proposal networks and fine‑grained classifiers) to detect hundreds of traffic‑sign types in billions of street‑view images, achieving high recall and precision, scalable updates, and near‑real‑time map data refresh.