The Human Right to water and sanitation

What does your morning routine look like? For most readers I’d assume you use the toilet, wash your hands, and maybe take a shower.  However, do you ever stop to consider the water you use to shower, or the soap you use to wash your hands? Often, especially in developed countries, these things are taken for granted, rightly considering access to adequate water, sanitation, and hygiene (WASH) as basic Human Rights (Figure 1).

Human Right
Figure 1: In the 2010, the UN declared access to clean water and sanitation a Human Right (UN-Water 2010)


However, what about if you don’t have access to adequate WASH facilities? Your morning routine begins to look very different, as does the rest of your day. For many people across the world, this is an everyday reality. Those who are homeless or residing in informal settlements and refugee camps are amongst the most vulnerable to poor WASH services. With over 1 billion people recorded as living in informal settlements (24% of the global urban population) (United Nations 2020), 6.6 million of whom live in refugee camps (UNHCR 2021),this is not a small problem. 
Informal settlements are classed as: 

“residential areas where inhabitants often have no security of tenure for the land or dwellings they inhabit; neighbourhoods usually lack basic services and city infrastructure; and housing may not comply with planning and building regulations and is often situated in geographically and environmentally sensitive areas” - (UN-Habitat 2015).

Refugee camps, a type of informal settlement are classified as:

“temporary facilities built to provide immediate assistance and protection to people who have been forced to flee their homes…to keep people safe during specific emergencies, but emergency situations can become protracted, resulting in people living in camps for years or even decades” - (UNHCR 2021).

Whether due to their “temporary” nature, dense populations, lack of secure tenure, or harsh locations, informal settlements should not be excluded from receiving the same basic services as elsewhere. However, service delivery in such settlements, whether WASH, housing, nutrition, or education face unique challenges, with poor access to such services risking lives and livelihoods on a daily basis. 

Challenges for WASH services in informal settlements 

In some cities, between 30 and 70% of residents live in informal settlements that fall outside of the cities WASH service coverage (UNICEF and UN-Habitat 2020), posing a myriad of problems. With an absence of centralised sanitation systems and insufficient emptying of sceptic tanks and latrines, sewage and grey water often flow openly through streets exposing residents to disease. A faecal waste flow diagram (SFD) from Dhaka for demonstrates the lack of sewage systems and disproportionate volume (69%) of faecal sludge from on-site facilities that is left to overflow into the environment (Figure 2). This is exacerbated by poorly managed, shared toilet facilities (if they exist at all), with open defecation and close proximity to latrines encouraging contamination and unsanitary conditions. The lack of clean water, soap, and feminine hygiene products limits resident’s ability to practice adequate hygiene, encouraging the spread of disease, whilst long walks and wait times for water risk the physical safety of women and girls and reduce the amount of time girls spend in education.

Figure 2: Faecal waste flow diagram (SFD) of Dhaka informal settlement, Bangladesh highlighting the challenges of inadequate sanitation services (CRC for Water Sensitive Cities 2018)


These problems are compounded by a lack of accurate, relevant, and timely spatial and temporal data to describe the demographics and living conditions in informal settlements (Niva, Taka, and Varis 2019). Without data and subsequent maps on the size and complexity of such residencies and the infrastructure present, it is near impossible to plan for services and improve such spaces in the future. 
With the added pressures of Covid-19, alongside climate change, and a growth in displaced peoples and those living in informal settlements, the challenges faced in such situations will simultaneously expand. The Human Right to water and sanitation should not be extended to the privileged few, whilst leaving so much of society behind. So, what can be done about the challenges facing WASH service delivery in informal settlements and camps? 

Improving WASH delivery through space technologies 

Alongside the obvious need for improved infrastructure and availability of WASH services on the ground, investing in data management to achieve this is key. As Cooper (2020), a political scientist in the development sector highlights 

“evidence from humanitarian crises worldwide have shown how utilities performance were impacted by lack of data in marginalised communities and demonstrated  the  need  for  closer  collaboration  with  local  actors”.

Conventional methods of data collection in informal settlements are limited, in part due to time and their dynamic and dense nature. In this way, space technologies offer unparalleled opportunities for collecting data that can benefit access to WASH in such localities. 

Remote sensing and Geographic information system (GIS) technologies are able to address some of the data challenges within informal settlements. From delineating the extent of settlements and aiding camp set up, to identifying WASH services and population dynamics, integrated spatial data can help fill some of the gaps in our understandings of such settlements. Table 1 introduces the measuring abilities and analysis potential of different space technologies that have been shown to be useful in informal settlements. 

Table 1: Delineation of the measurements and subsequent analysis made by remotely sensed images of different resolutions and of GPS


Detection and site selection of informal settlements and camps 

Multiple studies have applied remotely sensed images to delineate the extent of informal settlements, especially utilising high-resolution (>30cm / pixel) satellite imagery from instruments such as IKONOS, QuickBird, SPOT-5, and ERS SAR (Muli 2013; Dalen et al. 2000). By using manual (visually interpreting maps) and automated (machine learning algorithms) techniques, settlements can be registered and services such as water points and latrines can be better planned and distributed. The following projects are examples of where such techniques have been used.

The project ENVIREF (ENVIronmental monitoring of REFugee camps using high-resolution satellite images), started in 1998, was one of the first projects to explore the potential for civilian geomatics to assist with complex humanitarian emergency responses. The project illustrated the type of intelligence that could feasibly be manually extracted from commercially available medium and high resolution satellite images(Joshi 2012). Data from LANDSAT TM/ETM+, SPOT and IRS-1D sensors have been used to measure the location and extent of refugee camps and their surroundings at scales from 1:15,000 to 1:250,000 (Johannessen et al. 2001). A map from Kukes, Albania, for example, was created from a combination of both a LANDSAT TM image (30m), to give the thematic information, and an IRS-1D image (6m) to give the high spatial details such as infrastructure, water resources and vegetation cover (Figure 3).

Albania camp
Figure 5: Camp location and surrounding map for the Kukes region of Albania (Johannessen et al. 2001)


ERS SAR (European Remote Sensing satellites Synthetic Aperture Radar) imagery offers opportunities beyond optical sensors as radar has almost all-weather capacity, making it useful for detecting settlements across the world, especially in tropical, cloud covered regions, where the use of other satellites is hindered. Radar images can be compared to topographic maps and optical images in order to detect settlements which may have been missed from using only radar images, however the main challenge to do this is obtaining a decent geo-rectification of the image supported by DTM-data. 

One example of the use of ERS SAR was in four refugee settlements in the Jhapa district of Nepal. Here, geo-rectification was done using topographic maps at 1:25,000 and a Landsat-7 ETM+ scene, following which the location and perimeter of the settlements were sought using vector data. Two of the settlements, one being Beldangi camp (Figure 4), were identified very accurately, possibly in part due to the high contrast with the surrounding forest (Dalen et al. 2000). 

Beldangi camp
Figure 4: Identified Beldangi refugee settlements, Nepal SAR images in 1992, 1996, and 1999 (left to right) (Dalen et al. 2000).


Several studies have also explored the potential of semi-automated and automated procedures for delineating informal settlements. Stasolla and Gamba (2007) used SPOT-5 satellite images with 2.5m spatial resolution to detect refugee camps in Darfur, Sudan (Figure 5). They used a semi-automatic procedure to detect the boundaries and extent of formal and informal settlements, differentiating between the two based on building densities by using variance and K-means algorithm. Whilst this method did not produce perfect results in delineating settlement extent, the authors suggest that refined results could come from spatial analysis performed using textural features and highlight that the method exceeded alternative maps available at the time. 

Figure 5: Al-Fashir, Sudan: (a) SPOT image (b) classification results (red = city, green = camp, yellow = desert (Stasolla and Gamba 2007)


In addition to detection, space-based technologies can also aid in the initial site selection and monitoring of refugee camps. This is less relevant for informal settlements, which tend to grow, unplanned over time. When selecting a site, WASH teams work closely with local authorities and shelter teams to ensure that the camp’s water and sanitation needs can be met. Features that must be considered include water quantity, water quality, soil composition and moisture content (for construction feasibility), flood risk (related to geomorphology) and seasonal variability (UNHCR 2017), all of which remote sensing can assist with. Remotely sensed images spanning several years previous, alongside geological maps, local knowledge, pumping tests, and hydrogeological surveys can be used collectively to aid in the site selection process, helping to ensure WASH needs are catered for as best as possible. Once a site is selected, construction can then be monitored. SatCen – the European Union Satellite Centre, for example, used Sentinel-1 and -2 in camp construction of Char Piya camp, Bangladesh particularly by applying automatic change detection algorithms to the images to observe the changing situation over time (Group on Earth Observations n.d.).

Mapping WASH needs

To improve communities’ access to a sufficient quantity and quality of water, sanitation, and hygiene practices, Non-Governmental Organisation (NGOs), alongside Governments organise WASH interventions in camps Such interventions must identify WASH infrastructure needs and constantly monitor the changing WASH situation (UNHCR 2017). 

Space based technologies can aid in the identification of surface water bodies and the estimation of groundwater stored in a region, and can monitor changes over time, helping to ensure that the needs of camp and host populations are met. Identifying potential sources of water pollution and poor-quality water bodies which may be contributing to disease is also crucial, with remote sensing contributing to assessing water colour and suspended solids, both of which are used in part to assess the safety of a water source. By identifying WASH services such as hand pumps, latrines and waste disposal facilities, remotely sensed images can be invaluable in quantifying WASH infrastructural gaps and further needs. Images can also be used to measure the distance of shelters from water points and latrines - indicating whether regulations, such as settlements being within 50 m of a latrine, are being met (Sphere Association 2018). In-situ GPS monitoring is also a very valuable mapping tool used to locate specific infrastructure (see community mapping below) and has been used extensively by Oxfam to map WASH facilities for Burundian refugees in Tanzania. 

With the development of high resolution remote sensing with sub-meter resolutions, the opportunity to identify specific building infrastructure on the ground has become a reality. This can either be done through manual visual interpretation or semi- and fully-automatic image segmentation methods, sometimes aided by in-situ mapping. Using very high resolution (VHR) images to identify the amount and spatial distribution of dwellings, dwelling density, camp structure and camp growth/shrinkage for example allows more accurate population estimates which helps to determine the level of service requirements such as number of latrines and water points. 

E04Hum, is an example initiative led by Z_GIS at the University of Salzburg in collaboration with Medecins San Frontier (MSF). E04Hum uses images from satellites including Copernicus Sentinel-1 and Sentinel-2 and data from NASA’s Worldview, alongside in-situ data, to monitor population movements, groundwater potential, and the environment. For example, a system has been developed that, once a camp is delineated, can automatically update expansion or shrinkage in the camp, allowing population trends to monitored. The satellite imagery critically helps to estimate the number and location of people requiring water, sanitation, vaccinations etc., limits the time and resources needed to identify potential groundwater drilling sites on the ground by estimating groundwater locations from gravity changes. The initiative has successfully been used at the Minawo camp, Cameroon by MSF to improve water supplies and sanitation, alongside nutritional and healthcare support (Figure 6) (European Space Agency (ESA) 2019).

Figure 6: Refugee camps in Minawao, Cameroon, imaged by the Copernicus Sentinel-2 and WorldView-3 satellites. Copernicus Sentinel-2 sees a larger extent of land, whereas WideView-3 sees a smaller area at a higher resolution (European Space Agency (ESA) 2019).


The ENVIREF project, utilised high resolution satellite images from the IKONOS satellite (1m and 4m resolutions) obtained in 1999 to undertake similar work. Using the images to manually map individual buildings, shelters, and tents in Nepalese and Kenyan refugee camps, population estimates could be made. Aided by field studies, the project was able to delineate details such as hospitals, schools, water wells, petrol pumps and roads. The mapping was performed by interpretation and onscreen digitising of RGB (red-green-blue) and NIR (near-infrared) +RGB images, with the satellite image used to locate buildings and in-situ work often required to identify them (Johannessen et al. 2001). A detailed camp infrastructure map of Beldangi II in Nepal at a scale of 1:5,000 shows all minor roads, buildings coloured by category, with a dot signifying each camp hut (Figure 7) (Johannessen et al. 2001). 

Figure 7: Detailed camp infrastructure map of Beldangi II and extension camps, Nepal (Johannessen et al. 2001).


Traditional visual image interpretation tends to be work intensive, inaccurate, and subjective and is especially limited in complex situations where for example you have different roof materials or clouds. Furthermore, the use of shallow machine learning algorithms struggle to extract buildings due to the mixing of buildings with their surroundings, unknown height of buildings, and shadow effect of tall buildings (Xin 2018). This is where automated image segmentation, based on deep learning can be useful. (Xin 2018). 

A deep neural network designed to classify objects can lead to greater accuracy, objectivity and efficiency than (alternative or traditional methods). Deep learning can extract features by its ability to learn not only underlying characteristics such as colour and edge, but also intermediate features such as texture and shape, and more senior features such as a dog’s head (Figure 8) (Xin 2018). By extracting characteristics from different levels, deep learning can accurately identify and locate buildings, such as latrines, septic tanks, faecal sludge transfer stations, water tanks, and water sources. Mapping such infrastructure is key to highlighting the needs of communities so that interventions can be better planned and focussed and services better managed.   

Deep learning
Figure 8: Features extracted at different layers by deep learning method (Xin 2018)


Community mapping 

In addition to identifying infrastructure from satellite images, which is often carried about by agencies, work conducted by the communities themselves in informal settlements can be invaluable to mapping services and improving the living standards of those around them. This mapping is often done through in-situ data collection using handheld GPS monitors and smartphones. Although time intensive, the opportunity for empowerment and education of the large, young populations of many informal settlements in such skills, can make such projects a reality. Remote sensing and use of GPS are highly useful, employable skills. A number of innovative community-led projects around the world are capitalising on this by enabling residents to acquire such skills.

RefuGIS and Map Kibera are two such initiatives. RefuGIS, for example, has been training Syrian refugees in Zaatari camp, Jordan in the use of GPS, GIS, coordinate systems, Excel, and Open Data Kit, to be able to collect spatial data in the field in order to map places of interest around the camp, such as schools, mosques and shops (Figure 9) (Samson 2018). Devoid of adequate GIS applications and Information and Communications Technology (ICT) hardware, the camp layout remained unmapped until RefuGIS was established, improving the management of resources such as water, sanitation, and food.

Figure 9: Community mapping in Zaatari camp, Jordan (Samson 2018)



WASH provision in informal settlements is severely lacking, from broken pipes and shared latrines to insufficient water sources and soap. There is no questioning the need for better services. However, the successful planning and management of such services can only be realised when data on such settlements is available. When a settlement is not even acknowledged on a map, or no information is available on the services already there, the job of service providers is made almost impossible. 

Space technologies, from high resolution satellite images to GPS trackers, alongside artificial intelligence, and in-situ data collection offer lifesaving data collection opportunities for those living in informal settlements. Enabling the delineation of, and the location and identification of structures and population estimates within such settlements, service providers can better meet the needs of residents.


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