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Bernd Resch

Researcher at University of Salzburg

Publications -  148
Citations -  3818

Bernd Resch is an academic researcher from University of Salzburg. The author has contributed to research in topics: Computer science & Geospatial analysis. The author has an hindex of 26, co-authored 128 publications receiving 3074 citations. Previous affiliations of Bernd Resch include Heidelberg University & Massachusetts Institute of Technology.

Papers
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Journal ArticleDOI

Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples

Maged N. Kamel Boulos, +9 more
- 21 Dec 2011 - 
TL;DR: A comprehensive review of the overlapping domains of the Sensor Web, citizen sensing and human-in-the-loop sensing in the era of Mobile and Social Web, and the roles these domains can play in environmental and public health surveillance and crisis/disaster informatics can be found in this article.

Crowdsourcing, citizen sensing and Sensor Web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples

Maged N. Kamel Boulos, +9 more
TL;DR: An in-depth review of the key issues and trends in these areas, the challenges faced when reasoning and making decisions with real-time crowdsourced data, the core technologies and Open Geospatial Consortium standards involved (Sensor Web Enablement and Open GeoSMS), as well as a few outstanding project implementation examples from around the world.
Journal ArticleDOI

Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment

Bernd Resch, +2 more
- 04 Jul 2018 - 
TL;DR: An approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic information extraction with spatial and temporal analysis (local spatial autocorrelation) for hot spot detection.
Journal ArticleDOI

Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data

Enrico Steiger, +3 more
- 01 Nov 2015 - 
TL;DR: A semantic topic model classification and spatial autocorrelation analysis is applied to detect tweets indicating specific human social activities, showing an overall strong positive correlation in comparison with workplace population census data, being a good indicator and representative proxy for analyzing workplace-based activities.
Book ChapterDOI

People as Sensors and Collective Sensing-Contextual Observations Complementing Geo-Sensor Network Measurements

Bernd Resch, +2 more
TL;DR: This chapter contains a disambiguation between the terms People as Sensors (people contributing subjective observations), Collective Sensing (analysing aggregated anonymised data coming from collective networks) and Citizen Science (exploiting and elevating expertise of citizens and their personal, local experiences).