Spatial Pattern Matching: A Survey
Nicole Schneider, Kent O'Sullivan, Hanan Samet
Abstract
Recent developments in Artificial Intelligence (AI) have led to flexible ways for users to search through vast information. However, users may have questions that are grounded in the real world which require spatial inference, for which language models are not well suited. Conversely, traditional spatial search methods, like spatial pattern matching, can answer spatial reasoning questions correctly but are noise-intolerant, slow, and brittle. Given the current state, there are opportunities to integrate AI and spatial pattern matching to enable robust and flexible spatial search. To bridge this gap, we survey existing spatial pattern matching methods, including the few that apply AI to the problem, discussing their efficiency and limitations, and describing opportunities to further enable spatial search via AI.
BibTeX
@inproceedings{ijcai2026_spatialpatternma,
title = {Spatial Pattern Matching: A Survey},
author = {Nicole Schneider and Kent O'Sullivan and Hanan Samet},
booktitle = {IJCAI 2026},
year = {2026}
}