State of the world’s sanitation 

Sanitation: the often overlooked component of Sustainable Development Goal 6 (SDG 6) needs to be brought to the forefront of our attention. With 4.2 billion people, predominantly in Lower-Middle Income Countries (LMICs) still lacking access to safely managed sanitation, we need to stop shying away from conversations about toilets, sewage, and poo.

Sanitation coverage
Figure 1: Progress towards universal access to safely managed sanitation 2015-2020, among countries with <99% coverage in 2020, by income. Does not include 8 countries that already had >99% coverage in 2020, or 3 countries that have estimate for 2020 but not for 2000 (JMP, WHO, and Unicef 2020).


SDG 6 aims to bring equity in the service delivery of water, sanitation, and hygiene (WASH) as a collective. With objectives from ending open defecation and reducing untreated wastewater, to increasing water-use efficiency, SDG 6 has the potential to significantly improve the lives of those currently left behind (Figure 1). Safely managed water can evidently not be achieved without an equal emphasis on sanitation and hygiene. Thankfully, space technologies, whilst often explored in relation to potable and marine water, have some applications in sanitation and hygiene, too. Earth Observation (EO) data can provide reliable, up-to-date information on sanitation services due to their wide availability and objectivity - especially important in difficult to access areas. 

Locating sanitation services

One application of space technologies in sanitation is the use of hand-held Global Positioning System (GPS) monitors and smart phones in recording the location of facilities such as latrines, sanitary bins, septic tanks, and sewage networks (Figure 2). This can be done by both policy makers and individuals. As precision GPS mapping in dense areas can be challenging, sometimes GPS references are combined with tagging unique reference numbers to facilities or taking photographs of them (Prat and Trémolet 2013). Multiple mobile applications exist which store and make available such data, including mSewage, The Sanitation Mapper, and Toilight. mSewage for example is an open source app that can be used by anyone, anywhere, to map sewage outflows and sanitation infrastructure, allowing communities to identifying at risk water sources. Toilight on the other hand is an application specifically for finding the nearest toilet with desired characteristics, allowing users to add new toilets via GPS or by directly marking them on the map (Prat and Trémolet 2013). 

Map of latrines in Mathare, Kenya
Figure 2: GPS mapped toilets in Mathare (Primus 2011)


Further, Sanitation Mapper is a monitoring tool specifically targeted at policy makers to map sanitation infrastructure through area-based mapping in order to monitor facilities and allow appropriate decisions to be made (Prat and Trémolet 2013). It instantly converts survey data into Google Earth compatible maps, eliminating the need for GIS skills (Figure 3). Latrine data can be analysed in one of 7 ways: improved sanitation coverage, people per latrine, hygiene analysis, menstrual hygiene facilities, disability inclusive facilities, faecal waste management, or adequacy of payment. This then generates coloured maps depending on the percentage of facilities identified as improved or with menstrual hygiene facilities for example (Share and WaterAid 2012). Such maps and applications can subsequently be used to increase sustainability of management, identify areas where services are needed, and increase utilisation of facilities.

Sanitation mapping
Figure 3: The Sanitation Mapping process 


Identifying priority areas for sanitation interventions

As briefly mentioned, GPS technology can help to identify priority areas where services are needed, with the goal of ultimately improving equitable access. Many LMIC communities, especially those in dense, urban cities rely on onsite sanitation. Faecal Sludge Management (FSM): the collection, transport and treatment of human excreta, water, and solid wastes from such onsite sanitation systems is an essential process to sustain healthy, functioning communities. FSM, however, remains a challenge across many LMICs. Data on the service coverage of faecal sludge emptying providers is often limited due to low capacity, time requirements, and difficulties collecting data in high density settlements. This leads to poor FSM, posing severe risks to human and environmental health such as water pollution and the spread of water-related diseases such as diarrhoea, cholera and dysentery (Strauss and Montangero 2002).  Geographic Information Systems (GIS), whilst underexplored here, offer opportunities in the planning and management of faecal sludge by identifying areas lacking adequate sanitation for priority intervention.

The potential of GPS and GIS to improve access to equitable sanitation services has been studied for example in Kampala, Uganda. Here, Schoebitz et al. (2017) collected data on the locations of faecal sludge collection and disposal sites using GPS data loggers GT-730FL-S fitted within mechanical emptying trucks. The loggers were connected to an external power bank which allowed continuous recording for 7.5 days as the emptying trucks carried out their daily rounds. The GPS data were then analysed using ArcGIS to identify the temporal and spatial distribution of faecal sludge emptying events and hence the extent of service provision (Figure 4). 31% of Kampala was recorded as not receiving any emptying service during the study, with services clearly declining the further you move away from the centre. These findings allowed the identification of both areas for priority intervention by the municipality and untapped markets for the private sector. 

Faecal sludge emptying events
Figure 4: Distribution of faecal sludge emptying events recorded during a study in Kampala (n = 5653) (Schoebitz et al. 2017).


Priority areas for FSM were also identified in Lusaka, Zambia by Riedler et al (2021), where onsite sanitation with irregular emptying is common. The authors identified eight EO-related indicators relevant for FSM: building density, building size, building use, urban greenness, groundwater vulnerability, distance to water, street conditions, and distance to treatment plants. Normalised Difference Vegetation Index (NDVI) and Normalised Difference Water Index (NDWI) were derived from very high resolution (VHR) satellite images, whilst other indicators came from local data and OpenStreetMap. Indicators were then harmonised, weighted, and aggregated into a composite index, to show distinct geographical patterns, such as inaccessible areas for emptying trucks. The indicators and subsequent aggregation to a composite index identified hotspots for priority intervention. Such areas were characterised by high building density - assuming high population density; small building sizes in combination with a low vegetation cover - proxies for low income; poor street conditions - preventing the use of trucks for emptying; and highly vulnerable groundwater conditions (Riedler et al. 2021). 

Optimisation of treatment plant location 

Analysis of spatial GIS data in Kampala by Schoebitz et al. (2017) was also shown to help optimise faecal sludge logistics – improving the location of treatment plants. The shortest travel distances between the current treatment plants and emptying events were recorded (Figure 5a). These were then compared with linear distances between the emptying events and future planned treatment plants (Figure 5b), revealing a potential reduction in linear distance from 6.4 km to 5.4 km (Schoebitz et al. 2017). This type of knowledge could greatly increase efficiency when siting treatment plants - reducing travel times, transport costs and increasing overall level of service coverage. 

Emptying events and treatment plants
Figure 5: Linear distances between emptying events and present treatment plants - Lubigi and Bugolobi (a) and linear distances between emptying evets, the present treatment plant of Lubigi, and future planned plant locations – Kinawataka and Nalukolongo (b) (Schoebitz et al. 2017).


The optimisation of plant locations has also been implemented in the larger sewage treatment industry through the use of remote sensing data, using weighted index overlay methods. Inappropriate location of sewage treatment plants can lead to extensive ground and surface water pollution, posing a threat to sustainable WASH management (Benujah and Devi 2013). 

One location where GIS was successfully used to evaluate potential sites for a sewage treatment plant was in Tamil Nadu, India. Satellite images from IRS P6 and existing maps were integrated to produce thematic maps of land-use, slope (prepared from the Shuttle Radar Topographic Mission 90m DEMs), roads and drainage, gathering valuable data on the study area. This could ensure that a suitable site was selected: one that was on a slope of <15% (to limit instability), away from densely populated areas (to limit contamination risk), 200m from a main road (to limit distance for trucks to drive) and 200m a water body (to limit contamination risk). ArcGIS was then used to perform a weighted index overlay analysis to classify sites as good, moderate, and poor (Figure 6) (Benujah and Devi 2013). 

By streamlining data collection and providing more accurate results than traditional methods, GIS and remote sensing afford greater protection to the environment and an improved standard of living to local residents by enabling the optimisation of treatment plant location.  

Sewage treatment plant site suitability map
Figure 6: Sewage treatment plant site suitability map in Tamil Nadu, India (Benujah and Devi 2013).

The Future

Whilst the application of space technologies have been explored to a greater extent in potable and marine water, their use in managing human excreta and sewage is equally important from both an environmental and human health perspective. The studies explored in this article highlight exciting opportunities for space technology to aid WASH service delivery from a sanitation perspective. With the relative infancy of these applications comes many opportunities for future research in space and sanitation to help us realise the goal of adequate and equitable sanitation for all.


Benujah, B. R, and G Devi. 2013. “Site Suitability Evaluation For Sewage Treatment Plant In Nagercoil Municipality, Tamil Nadu Using Remote Sensing Techniques,” 590–98.

JMP, WHO, and Unicef. 2020. “Progress on Household Drinking Water, Sanitation, and Hygiene.”

Kampala Capital City Authority. 2016. “Kampala Faecal Sludge Management: Improving Faecal Sludge Management For On-Site Sanitation.” 2016. feacal sludge management for on-site sanitation.pdf.

Prat, Marie-alix, and Sophie Trémolet. 2013. “NOTE 2: SANITATION APPS - A Brief Overview of Sanitation App Developments,” no. June: 6 pp.

Riedler, B, J Nodel, R Siber, N Andriessen, L Strande, and S Lang. 2021. “Supporting Urban Sanitation Management through the Integration of EO-Based Indicators.” In EARSeL Liege 2021.

Schoebitz, Lars, Fabian Bischoff, Christian Riuji Lohri, Charles B. Niwagaba, Rosi Siber, and Linda Strande. 2017. “GIS Analysis and Optimisation of Faecal Sludge Logistics at City-Wide Scale in Kampala, Uganda.” Sustainability (Switzerland) 9 (2).

Share, and WaterAid. 2012. “Sanitation Mapper: A Free, Simple, and Powerful Mapping Tool.”

Strauss, Martin, and Agnes Montangero. 2002. “Feacal Sludge Management Review of Practices, Problems and Initiatives.”