ABSTRACT
COVID-19 has had a significant economic impact on a global scale. The national lockdown, enacted through the Disaster Management Act 57 of 2002, disrupted multiple economic activities, including that of the Creative and Cultural industries. In the performance and celebrations domain, under which live events (.i.e., theatre and performance) and technical production services (i.e., technical support and services for theatre and performance) resort, the postponement or cancellation of events in the face of the pandemic had a devastating effect on lives and livelihoods. An initiative called #LightSAred, initiated by the South African Communications Industries Association (SACIA) and the Technical Production and Services Association, was one of the private initiatives aimed at extending financial relief to those who earn a living in the technical production and live events industry. However, the lack of reliable data about the industry's market and the impact of COVID-19 was a significant challenge in resource development and allocation. In response, the Sun Circle Group, a media company that services the live entertainment and professional audio-visual industries in South Africa, conducted an extensive online survey of businesses in this industry. For this study, permission has been obtained from the Sun Circle Group to analyse the secondary data set. Situated within the literature on the creative economy, analysing the secondary industry data in this article offers an understanding of the market position and perceived financial implications of the COVID-19 pandemic on the Live Events, Technical and Production Services industry. The results of this research support advocacy for a long-term national strategy to secure livelihoods and the sustainability of not only the Live Events, Technical and Production Services industry, but the CCIs as a whole.
ABSTRACT
Roads found in informal settlements arise out of convenience, and are often not recorded or maintained by authorities. This complicates service delivery, sustainable development and crisis mitigation, including management and tracking of COVID-19. We, therefore, aim to extract informal roads in remote sensing images. Existing techniques aiming at the extraction of formal roads are not suitable for the problem due to the complex physical and spectral properties of informal roads. The only existing approaches for informal roads, namely (Nobrega et al., 2006, Thiede et al., 2020), do not consider neural networks as a solution. Neural networks show promise in overcoming these complexities. However, they require a large amount of data to learn, which is currently not available due to the expensive and time-consuming nature of collecting such data. This paper implements a neural network to extract informal roads from a data set digitised by this research group. Data quality is assessed by calculating validity completeness, homogeneity and the V-measure, a measure of consistency, in order to evaluate the overall usability of the dataset for neural network informal road detection. We implement the GANs-UNet model that obtained the highest F1-score in a 2020 review paper (Abdollahi et al., 2020) on the state-of-the-art deep learning models used to extract formal roads. The results indicate that the model is able to extract informal roads successfully in the presence of appropriate training data.
ABSTRACT
AFRICAN DEVELOPMENT : The Covid-19 pandemic forced us to go online in a hurry whilst maintaining constructive alignment, enforce learning, and be student-centred, whilst simultaneously developing 21st century graduate attributes and critical thinking skills. Whilst interactive learning and collaborative is frequently cited as a good practice even in the online environment, this learning approach is not without challenges either for the student or the facilitator