Posted: September 12, 2014 at 11:01 am, Last Updated: January 27, 2017 at 3:19 pm
The analysis of social media content for the extraction geospatial information and event-related knowledge has recently received substantial attention. In this paper we present an approach that leverages the complementary nature of social multimedia content by utilizing heterogeneous sources of social media feeds to assess the impact area of a natural disaster. More specifically, we introduce a novel social multimedia triangulation process that uses jointly Twitter and Flickr content in an integrated two-step process: Twitter content is used to identify toponym references associated with a disaster; this information is then used to provide approximate orientation for the associated Flickr imagery, allowing us to delineate the impact area as the overlap of multiple view footprints. In this approach, we practically crowdsource approximate orientations from Twitter content and use this information to orient accordingly Flickr imagery and identify the impact area through viewshed analysis and viewpoint integration. This approach enables us to avoid computationally intensive image analysis tasks associated with traditional image orientation, while allowing us to triangulate numerous images by having them pointed towards the crowdsourced toponym location. The paper presents our approach and demonstrates its performance using a real-world wildfire event as a representative application case study.
Our cross-source triangulation framework is outlined in the figure below:
|The cross-source triangulation framework.|
To demonstrate the benefit of using cross-sourced social media in the triangulation process we applied three modes of the analysis:
- Mode 1: the impact area was estimated as the overlap of all viewsheds that were generated from all Flickr contribution locations without calculating a reference point or evaluating the Angle Of View (AOV) for each image. Accordingly, in this mode, we use only Flickr data, without constraining the viewshed analysis with any AOV information.
- Mode 2: the impact area was estimated by using the centroid of the locations of all Flickr contributions as the reference point for the AOV calculation, followed by a viewshed analysis of each image. Accordingly, in this mode we use only Flickr data, ignoring any toponym information from Twitter.
- Mode 3: the impact area was estimated by using the toponym reference, as derived from Twitter, as the reference point for the AOV calculation, followed by a viewshed analysis of each image. Accordingly, in this mode we use Twitter content to orient Flickr data and guide the viewshed analysis.
The figure below shows the result from Mode 3:
|A three-dimensional perspective of wildfire location assessment as derived by analysis mode 3.|
Panteras, G., Wise, S., Lu, X., Croitoru, A., Crooks, A.T. and Stefanidis, A. (2015), Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter, Transactions in GIS, 19(5): 694–715. (pdf)