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GeoSense-AI: Fast Location Inference from Crisis Microblogs

Deepit Sapru

TL;DR

The paper tackles the challenge of sparse geo-tags in crisis microblogs by introducing GeoSense-AI, a modular streaming pipeline that combines hashtag segmentation, syntactic pattern matching, dependency parsing, lightweight NER, and gazetteer grounding to extract toponyms directly from text. It demonstrates that the system achieves a strong F1 score while delivering orders-of-magnitude faster throughput than conventional NER toolkits, enabling near-real-time geolocation during emergencies. A production visualization interface maps extracted locations to support situational awareness for floods, disease outbreaks, and other fast-moving events. The work shows robust performance in informal text and lays a foundation for scalable, multilingual crisis analytics beyond traditional geo-tag reliance.

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.

GeoSense-AI: Fast Location Inference from Crisis Microblogs

TL;DR

The paper tackles the challenge of sparse geo-tags in crisis microblogs by introducing GeoSense-AI, a modular streaming pipeline that combines hashtag segmentation, syntactic pattern matching, dependency parsing, lightweight NER, and gazetteer grounding to extract toponyms directly from text. It demonstrates that the system achieves a strong F1 score while delivering orders-of-magnitude faster throughput than conventional NER toolkits, enabling near-real-time geolocation during emergencies. A production visualization interface maps extracted locations to support situational awareness for floods, disease outbreaks, and other fast-moving events. The work shows robust performance in informal text and lays a foundation for scalable, multilingual crisis analytics beyond traditional geo-tag reliance.

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.

Paper Structure

This paper contains 26 sections, 5 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Crisis Informatics Systems
  4. Location Extraction Methodologies
  5. Methodology
  6. System Architecture Overview
  7. Hashtag Segmentation for Location Discovery
  8. Text Preprocessing and Normalization
  9. Syntactic Pattern Matching for Location Identification
  10. Dependency Parsing for Contextual Location Extraction
  11. Lightweight Named Entity Recognition
  12. Gazetteer Verification and Disambiguation
  13. Experimental Evaluation
  14. Dataset Construction
  15. Baseline Methods
  16. ...and 11 more sections

Figures (2)

  • Figure 1: GeoSense-AI processing pipeline for location extraction from microblog text.
  • Figure 2: Precision-Recall comparison of location extraction methods. GeoLoc achieves optimal balance in the upper-right region.