Work

From Geolocated Images to Urban Region Identification and Description: a Large Language Model Approach

Year: 2024

Type: conference-paper

Abstract: Urban research faces challenges in understanding and describing city regions, which are essential for urban planning and tourism management. Traditional methods rely on predefined areas and non-human-readable representations. This paper presents a new unsupervised approach that overcomes these limitations using a data-driven method with Instruction-tuned Large Language Models (ILLMs). Our technique dynamically identifies urban regions with similar features and generates human-readable descriptions. We validate this method using Flickr images from Pisa, Italy, and our results show that it effectively captures the semantic features of urban regions and generates comprehensible textual descriptions.

Source: ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL)

DOI: 10.1145/3678717.3691317

Authors: Guido Rocchietti, Chiara Pugliese, Gabriel Sartori Rangel, Jonata Tyska Carvalho

Institutions: University of Pisa, Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo", Universidade Federal de Santa Catarina

Language: English

Topic: Advanced Image and Video Retrieval Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

OpenAlex ID: W4404611684

FWCI: 0.422

Cites: 12

Cited by: 2

Related to: 10