Placing flickr photos on a map

By Pavel Serdyukov, Vanessa Murdock, Roelof van Zwol. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2009

320 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geography, Planning and Development, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bc4975b6a179d6c2a2e4de4f87a37620d62c8d49

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Li Fei-Fei
2. Damian Borth
3. Trevor Darrell
4. Luc Van Gool
5. Jason Baldridge
6. Trevor Cohn
7. Benjamin Wing
8. Paul Cook

In Geography, Planning and Development

1. Timothy Baldwin
2. Paul Cook
3. Bo Han
4. Olivier Van Laere
5. Bart Thomee
6. Claudia Hauff
7. Makoto Okazaki
8. Takeshi Sakaki

In Signal Processing

1. Timothy Baldwin
2. Paul Cook
3. Bo Han
4. Jason Baldridge
5. Trevor Cohn
6. Damian Borth
7. Benjamin Elizalde
8. Claudia Hauff

Citations in the corpus

• Earthquake shakes Twitter users: real-time event detection by social sensors (2010, methodology): “investigated generic methods for placing photographs on Flickr on the world map [24].”
• Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development (2013, methodology): “Some studies have specifically investigated collaborative bookmarking data, as Flickr provides, from a spatiotemporal perspective: Serdyukov et al. describes investigations of generic methods for placing photographs from Flickr on the world map [29].”
• The Placing Task at MediaEval 2016 (2016, background): “In particular, we aim to see those taking part to improve upon the contributions of participants from previous editions, as well as of the research community at large, e.g. [8, 11, 4, 2, 6, 9].”
• The Placing Task: A Large-Scale Geo-Estimation Challenge for Social-Media Videos and Images (2014, methodology): “Also, user-contributed tags were exploited for geotagging by associating tag distributions with locations that were represented as grid cells on a map of the earth, which were then used to infer the geographic locations of where Flickr images were taken [16].”
• Supervised Text-based Geolocation Using Language Models on an Adaptive Grid (2012, methodology): “For text, both Serdyukov et al. (2009) and Wing and Baldridge (2011) use a similar approach, but compute document similarity based on language models rather than image features.”
• You are where you tweet: a content-based approach to geo-locating twitter users (2010, background): “These users actively publish short messages (“tweets”) of 140 characters or less to an audience of their subscribers (“followers”).”
• Geolocation Prediction in Social Media Data by Finding Location Indicative Words (2012, methodology): “(Lieberman and Lin, 2009) and Flickr image tags (Crandall et al., 2009; Serdyukov et al., 2009; Hauff and Houben, 2012) have been used as the basis for geolocation prediction.”
• Semi-supervised User Geolocation via Graph Convolutional Networks (2018, background): “2013); and (3) supervised models which are often framed as text classification (Serdyukov et al., 2009; Wing and Baldridge, 2011; Roller et al., 2012; Han et al., 2014) or text regression (Iso et al.”