GeoSense-AI: Fast Location Inference from Crisis Microblogs
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Computer Science > Computation and Language
arXiv:2512.18225 (cs)
[Submitted on 20 Dec 2025 (v1), last revised 11 Sep 2026 (this version, v2)]
Title:GeoSense-AI: Fast Location Inference from Crisis Microblogs
Authors:Deepit Sapru
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Abstract:This paper presents an applied AI pipeline for real-time 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 geo-tags. 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 fast-moving 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.
| Subjects: | Computation and Language (cs.CL); Social and Information Networks (cs.SI) |
| Cite as: | arXiv:2512.18225 [cs.CL] |
| (or arXiv:2512.18225v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2512.18225
arXiv-issued DOI via DataCite
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Submission history
From: Deepit Sapru [view email][v1] Sat, 20 Dec 2025 05:46:57 UTC (12 KB)
[v2] Fri, 11 Sep 2026 04:33:23 UTC (13 KB)
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View a PDF of the paper titled GeoSense-AI: Fast Location Inference from Crisis Microblogs, by Deepit Sapru
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