POI GPT: Extracting POI Information from Social Media Text Data

Abstract. Point of Interest (POI) is an important intermediary connecting geo data and text data in smart cities, widely used to extract and identify urban functional areas. While computer uses numerical coordinates, human uses places names or addresses to find location, leading to spatial-semantic ambiguities. However, traditional methods of extracting POIs are time-consuming and costly, and has the limitation of the lack of integration of functionalities such as information extraction (IE), information searching. Also, previous models have low accessibility and high barriers for users. With the advent of Large Language Models (LLMs) we propose a method that connects LLM models and POI information based on social media text data. By employing two steps, named entities recognition (NER) and POI information searching, we introduce POI GPT, the specialized model for providing precise location of POIs in social media text data. We compared its results with those obtained by human experts, NER model and zero-shot prompts. The findings show that our model effectively found the POI and precise location from social media text data. In result, POI GPT is a effective model that solves the existing POI extraction problems. We provide new extraction technique of POI GPT which is a new paradigm in traditional urban research methodologies and be actively utilized in urban studies in the future.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: POI GPT: Extracting POI Information from Social Media Text Data ; volume:XLVIII-4/W10-2024 ; year:2024 ; pages:113-118 ; extent:6 ; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-4/W10-2024 (2024), 113-118 (gesamt 6)

Beteiligte, Orts- und Zeitangaben

• Urheber: Kim, Hyebin ; Lee, Sugie

Weitere Informationen

• DOI: 10.5194/isprs-archives-XLVIII-4-W10-2024-113-2024
• URN: urn:nbn:de:101:1-2406060428273.307049587909
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 10:51 MESZ