The current paper focuses on the development of an enhanced Mobile Journalism (MoJo) model for soundscape heritage crowdsourcing, data-driven storytelling, and management in the era of big data and the semantic web. Soundscapes and environmental sound semantics have a great impact on cultural heritage, also affecting the quality of human life, from multiple perspectives. In this view, context- and location-aware mobile services can be combined with state-of-the-art machine and deep learning approaches to offer multilevel semantic analysis monitoring of sound-related heritage. The targeted utilities can offer new insights toward sustainable growth of both urban and rural areas. Much emphasis is also put on the multimodal preservation and auralization of special soundscape areas and open ancient theaters with remarkable acoustic behavior, representing important cultural artifacts. For this purpose, a pervasive computing architecture is deployed and investigated, utilizing both client- and cloud-wise semantic analysis services, to implement and evaluate the envisioned MoJo methodology. Elaborating on previous/baseline MoJo tools, research hypotheses and questions are stated and put to test as part of the human-centered application design and development process. In this setting, primary algorithmic backend services on sound semantics are implemented and thoroughly validated, providing a convincing proof of concept of the proposed model.

Nowadays, news coverage implies the existence of video footage and sound, from which arises the need for fast reflexes by media organizations. Social media and mobile journalists assist in fulfilling this requirement, but quick on-site presence is not always feasible. In the past few years, Unmanned Aerial Vehicles (UAVs), and specifically drones, have evolved to accessible recreational and business tools. Drones could help journalists and news organizations capture and share breaking news stories. Media corporations and individual professionals are waiting for the appropriate flight regulation and data handling framework to enable their usage to become widespread. Drone journalism services upgrade the usage of drones in day-to-day news reporting operations, offering multiple benefits. This paper proposes a system for operating an individual drone or a set of drones, aiming to mediate real-time breaking news coverage. Apart from the definition of the system requirements and the architecture design of the whole system, the current work focuses on data retrieval and the semantics preprocessing framework that will be the basis of the final implementation. The ultimate goal of this project is to implement a whole system that will utilize data retrieved from news media organizations, social media, and mobile journalists to provide alerts, geolocation inference, and flight planning.