Information-optimal Abstaining for Reliable Classification of Building Functions
- Technical University of Munich, Department of Aerospace and Geodesy, Big Geospatial Data Management, Munich, Germany
Keywords: Probabilistic Classification, Social Media Text Mining, Land Use, Urban Analysis, Building Functions
Abstract. In the past decade, major breakthroughs in sensor technology and algorithms have enabled the functional analysis of urban regions based on Earth observation data. It has, for example, become possible to assign functions to areas in cities on a regional scale. With this paper, we develop a novel method for extracting building functions from social media text alone. Therefore, a technique of abstaining is applied in order to overcome the fact that most tweets will not contain information related to a building function albeit they have been sent from a specific building as well as the problem that classification schemes for building functions are overlapping.