Finding Hope in Rubble: Vector Search for Disaster Response

This article describes the development of a geocoding application using vector databases (LanceDB and Pgvector) and sentence transformers to search for locations and addresses in the aftermath of a disaster. The application leverages address information and geographical coordinates to find similar addresses and closest points.


  • The application was motivated by the challenges faced during the rescue efforts following the 2023 earthquake in Turkey, where lack of access to accurate and up-to-date location data hindered aid delivery.
  • The application uses address data containing city, district, neighborhood, street, building number, and location information.
  • Sentence transformers are used to convert addresses into vectors for similarity search.
  • LanceDB and Pgvector are used as vector databases to store and query the address vectors and geographical coordinates.
  • The application can find addresses similar to a given address and find the closest locations to a given point.
  • The chosen model for sentence transformers is 'emrecan/bert-base-turkish-cased-mean-nli-stsb-tr'.
  • The vector dimensions are 768 for address embeddings and 2 for geographical coordinates.
  • The application uses Euclidean distance (L2 metric) for similarity search.


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