Existing pedestrian navigation systems often provide direction-only instructions with limited visual confirmation at complex intersections or along long straight paths. We study coordinate-free landmark extraction from dashcam image sequences. The model returns a set of landmarks with structured attributes (e.g., category and description), or abstains when visual evidence is insufficient. Although general MLLMs can recognize street-scene objects, they do not reliably follow expert rules for filtering ambiguous candidates. Moreover, a false landmark can mislead a pedestrian, making precision more important than exhaustive coverage in this task. We therefore develop a progressive alignment framework, supported by a landmark annotation and augmentation pipeline that converts navigation experts’ selection criteria into staged supervision signals. We further propose a hard-gated bipartite matcher for box-free evaluation and a navigation-aware risk-sensitive reward that penalizes false positives more heavily than abstentions without inducing blanket silence. On the held-out benchmark, our model achieves 92.29% precision while maintaining practical coverage, outperforming strong MLLM baselines. A 30-day A/B test with 2M users on the Amap platform further shows that landmark-augmented instructions reduce departure-confirmation latency by 23.5%. Together, these results demonstrate that risk-aware alignment enables reliable, abstention-capable landmark extraction for navigation.