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Hybrid geo-information processing : crowdsourced supervision of geo-spatial machine learning tasks

The new modes of production and consumption of geographic information offer great opportunities for improved near real-time decision making of individuals and organizations, from small daily tasks like where to buy groceries or park your car, to critical tasks like coordinating humanitarian crisis response and saving lives. As has been argued before [5], there are several challenges to tackle before we can harness the full potential of geo-social media streams that contain volunteered geographic information or user-generated geographic content (UGGC). These include the heterogeneity and volatility of data formats and sources, the sheer volume of information streams that can change rapidly, and the credibility and accuracy of the information. The main objectives of this short paper are threefold. First, to explore the state-of-the-art of comprehensive approaches to processing geo-social media streams. Second, to identify the potential of crowd-sourced supervised learning to improve the handling of UGGC. Third, to develop a conceptual architecture based on a methodological review that serves as a first step to the implementation and testing of a prototype. Numerous studies investigated UGGC, including event detection and information processing for disaster management [17], citizen science observations on phenology [2], neo-cartography [13], health-related inquiries [14], environmental monitoring [6], and the automatic description of places [19]. Although often producing astonishing and useful results, all efforts had their effectiveness reduced by the UGGC's unknown quality-the uncertainty about its uncertainty. UGGC often shows areas of data scarcity and data abundance or even data redundancy [9, 10]. Its availability seems to follow a positive feedback loop, increasing the inequalities and generating false data shadows [21]. Further, content does not arrive in anticipated packages, but as highly varying streams that often need processing in near real-time. UGGC formats are heterogeneous, with content commonly being unstructured. Finally, the credibility of the source and the quality of the data is often obscure, with the source's level of expertise and the motivation for participation only indirectly inferable [7], and the origins of inaccuracies manifold, e.g. conflicting feature classifications between countries or cultures, outdated or wrong information available to the contributor, or even malicious intent. In summary, effective and efficient information retrieval for UGGC remains a difficult task [16]. A common response to these challenges has been the crowdsourcing of curation tasks [23]. Despite encouraging results, the curation by human volunteers might not be as accurate as once hoped for, and the process itself is not replicable [4]. …