GeoSense-AI: Fast Location Inference from Crisis Microblogs

TL;DR

GeoSense-AI is an efficient, real-time geolocation system for crisis microblogs that combines advanced NLP techniques and geographic knowledge bases to improve situational awareness during emergencies.

Contribution

It introduces a novel, low-latency NLP pipeline for real-time location inference directly from noisy microblog text, surpassing existing tools in speed and robustness.

Findings

Achieves high F1 scores in location extraction tasks.

Operates with significantly faster throughput than traditional NER tools.

Successfully deployed in live crisis scenarios for flood and outbreak monitoring.

Abstract

This paper presents an applied AI pipeline for realtime geolocation from noisy microblog streams, unifying statistical hashtag segmentation, part-of-speech-driven proper-noun detection, dependency parsing around disaster lexicons, lightweight named-entity recognition, and gazetteer-grounded disambiguation to infer locations directly from text rather than sparse geotags. The approach operationalizes information extraction under streaming constraints, emphasizing low-latency NLP components and efficient validation against geographic knowledge bases to support situational awareness during emergencies. In head to head comparisons with widely used NER toolkits, the system attains strong F1 while being engineered for orders-of-magnitude faster throughput, enabling deployment in live crisis informatics settings. A production map interface demonstrates end-to-end AI functionality ingest, inference, and visualization--surfacing locational signals at scale for floods, outbreaks, and other fastmoving events. By prioritizing robustness to informal text and streaming efficiency, GeoSense-AI illustrates how domain-tuned NLP and knowledge grounding can elevate emergency response beyond conventional geo-tag reliance.