Untitled Storyboard

Skaidrė: 1
Hmm. I will check the new geo-code X2, Y2. I'll look at all the real addresses within a 200-meter radius of it.
Oops, my mistake, Suresh. That MG Road address is actually at a different Lat/Long: X2, Y2.
All the addresses around X2, Y2 are "Hadapsar Mandi," "Hadapsar Station"... None of them match my target address! My previous cluster's confidence was much higher!
Since you're the local expert, Ramesh, I'll give your geo-code a small extra weight of 0.05. But even with that, my original X, Y prediction still has higher confidence. I'll stick with X, Y.
Skaidrė: 3
A new geo-code from the courier team is introduced, but it may be incorrect. To validate this, we run a 200m proximity search around the courier’s coordinates and compute a confidence score based on the semantic similarity between nearby addresses and the target address. The low similarity indicates the courier’s point does not match the true location. We still add a small experience weight (0.05) to acknowledge possible ground knowledge, but the model’s confidence remains higher, proving the original prediction is more accurate.
Simple. I've already done the clustering. You can just go to the centroid of that high-confidence Pune MG Road cluster. That will get you to the area.
Brother, I just need to go to MG Road for free sweets!
For Route/Area Prediction, the Cluster Centroid is used as the destination point, as it represents the most likely area for the address type.

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