Opportunities for Increasing Societal Value of Remote Sensing Data in South Africa’s Strategic Development Priorities: A Review

Despite the enormous capital required to fund remote sensing initiatives, governments worldwide are increasingly adopting earth observation technologies to optimise operational efficiency and societal benefit. However, the value of information derived from earth observation will increase substantially if augmented by socio-economic data within contextualised focus areas of direct societal relevance. Within the framework of the key strategic development priorities designed by the South African government, the objective of this paper was to review existing and emerging remote sensing applications and their relevance to South Africa’s development priorities. Whereas there is potential for adoption of remote sensing techniques in other prioritised areas, this paper identifies health, crime analysis, rural planning and agriculture, natural resource management and physical planning as areas with considerable potential. However, to realise the set strategic priorities and outcomes, decision support systems that incorporate information derived from remote sensing should be maximised. To achieve this, it will be necessary to link patterns and processes from expert knowledge to emerging and existing societal challenges identified and to develop requisite policies of governance. The paper concludes that remote sensing technology has considerable potential to support sustainable socio-economic strategic priorities set by the South African government

Satellite Earth observation to support sustainable rural development

Traditional survey and census data are not sufficient for measuring poverty and progress towards achieving the Sustainable Development Goals (SDGs). Satellite Earth Observation (EO) is a novel data source that has considerable potential to augment data for sustainable rural development. To realise the full potential of EO data as a proxy for socioeconomic conditions, end-users – both expert and non-expert – must be able to make the right decisions about what data to use and how to use it. In this review, we present an outline of what needs to be done to operationalise, and increase confidence in, EO data for sustainable rural development and monitoring the socioeconomic targets of the SDGs. We find that most approaches developed so far operate at a single spatial scale, for a single point in time, and proxy only one socioeconomic metric. Moreover, research has been geographically focused across three main regions: West Africa, East Africa, and the Indian Subcontinent, which underscores a need to conduct research into the utility of EO for monitoring poverty across more regions, to identify transferable EO proxies and methods. A variety of data from different EO platforms have been integrated into such analyses, with Landsat and MODIS datasets proving to be the most utilised to-date. Meanwhile, there is an apparent underutilisation of fusion capabilities with disparate datasets, in terms of (i) other EO datasets such as RADAR data, and (ii) non-traditional datasets such as geospatial population layers. We identify five key areas requiring further development to encourage operational uptake of EO for proxying socioeconomic conditions and conclude by linking these with the technical and implementational challenges identified across the review to make explicit recommendations. This review contributes towards developing transparent data systems to assemble the high-quality data required to monitor socioeconomic conditions across rural spaces at fine temporal and spatial scales

Modelling and mapping the intra-urban spatial distribution of Plasmodium falciparum parasite rate using very-high-resolution satellite derived indicators

BACKGROUND: The rapid and often uncontrolled rural-urban migration in Sub-Saharan Africa is transforming urban landscapes expected to provide shelter for more than 50% of Africa's population by 2030. Consequently, the burden of malaria is increasingly affecting the urban population, while socio-economic inequalities within the urban settings are intensified. Few studies, relying mostly on moderate to high resolution datasets and standard predictive variables such as building and vegetation density, have tackled the topic of modeling intra-urban malaria at the city extent. In this research, we investigate the contribution of very-high-resolution satellite-derived land-use, land-cover and population information for modeling the spatial distribution of urban malaria prevalence across large spatial extents. As case studies, we apply our methods to two Sub-Saharan African cities, Kampala and Dar es Salaam. METHODS: Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2-10 years (PfPR2-10) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR2-10 across the urban landscape. Intra-urban population distribution maps were used to adjust the estimates according to the underlying population. RESULTS: The results suggest that the spatial distribution of PfPR2-10 in both cities is diverse and highly variable across the urban fabric. Dense informal settlements exhibit a positive relationship with PfPR2-10 and hotspots of malaria prevalence were found near suitable vector breeding sites such as wetlands, marshes and riparian vegetation. In both cities, there is a clear separation of higher risk in informal settlements and lower risk in the more affluent neighborhoods. Additionally, areas associated with urban agriculture exhibit higher malaria prevalence values. CONCLUSIONS: The outcome of this research highlights that populations living in informal settlements show higher malaria prevalence compared to those in planned residential neighborhoods. This is due to (i) increased human exposure to vectors, (ii) increased vector density and (iii) a reduced capacity to cope with malaria burden. Since informal settlements are rapidly expanding every year and often house large parts of the urban population, this emphasizes the need for systematic and consistent malaria surveys in such areas. Finally, this study demonstrates the importance of remote sensing as an epidemiological tool for mapping urban malaria variations at large spatial extents, and for promoting evidence-based policy making and control efforts.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Flooding through the lens of mobile phone activity

Natural disasters affect hundreds of millions of people worldwide every year. Emergency response efforts depend upon the availability of timely information, such as information concerning the movements of affected populations. The analysis of aggregated and anonymized Call Detail Records (CDR) captured from the mobile phone infrastructure provides new possibilities to characterize human behavior during critical events. In this work, we investigate the viability of using CDR data combined with other sources of information to characterize the floods that occurred in Tabasco, Mexico in 2009. An impact map has been reconstructed using Landsat-7 images to identify the floods. Within this frame, the underlying communication activity signals in the CDR data have been analyzed and compared against rainfall levels extracted from data of the NASA-TRMM project. The variations in the number of active phones connected to each cell tower reveal abnormal activity patterns in the most affected locations during and after the floods that could be used as signatures of the floods - both in terms of infrastructure impact assessment and population information awareness. The representativeness of the analysis has been assessed using census data and civil protection records. While a more extensive validation is required, these early results suggest high potential in using cell tower activity information to improve early warning and emergency management mechanisms.Comment: Submitted to IEEE Global Humanitarian Technologies Conference (GHTC) 201

Measuring poverty in India with machine learning and remote sensing

In this paper, we use deep learning to estimate living conditions in India. We use both census and surveys to train the models. Our procedure achieves comparable results to those found in the literature, but for a wide range of outcomes

Delving into Geospatial Data Services: Monitoring Earth for Covid-19 Impact Measure and Decision Making

Geospatial technologies are crucial for many applications and can facilitate decision-making to benefit society.  When the Covid-19 pandemic restricted most of the services, geospatial technologies like satellite remote sensing, geographical information systems, and other allied technologies were found essential.  They speed up many critical decision-making processes in the fight against the pandemic.  This paper explores the significant contributions from the geospatial aspects throughout the pandemic in various research domains.  The potential applications of geospatial technology to assist humanity during the pandemic are thoroughly examined.  We categorized the entire study into i) environmental monitoring services, ii) disease control and management services, and iii) forecasting and decision-making services.  Many valuable findings are derived based on the systematic review of some remarkable works.  The outcome helps us understand how decision-making and forecasting are essential in the fight against the pandemic, with profound implications for future multidisciplinary research using geospatial technology