mlsanityData to 7 Oct 2026

Geo-Information Harvesting from Social Media Data

arXiv 2211.00543 ↗First posted 1 Nov 2022cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 48 of its 227 references that are in the map. First posted on arXiv 1 Nov 2022.

Builds on

48 of 227 references.

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 1.

5 papers.

  1. 2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93
  2. 2024Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentationcited by 26
  3. 2025The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandatescited by 7
  4. 2024Beyond Grid Data: Exploring Graph Neural Networks for Earth Observationcited by 3
  5. 2026Decoding the Disaster: Multi-Task Geospatial Reasoning with Vision-Language Models and Crowdsourced Imagery for Disaster Mappingcited by 0