Cities in Africa and developing countries in general are having a difficult time coping with
the influx of people arriving every day. Informal settlements are growing, and governments
are struggling to provide even the most fundamental services to their urban populations.
Kibera (edge region within the Nairobi) is the biggest informal settlement in Kenya, and one
of the biggest in Africa. The population estimates vary between 170,000 and 1 million and
are highly debatable. What is certain is that the area is large (roughly 2.5 km2), host at least
hundreds of thousands people, is informal and self-organized, stricken by poverty, disease,
population increase, environmental degradation, corruption, lack of security and - often
overlooked but extremely important – lack of information which all contribute to lack of
basic services such as access to safe water, sanitation, health care and formal education.
In Africa, but also in other continents, urban growth has reached alarming figures. Informal
settlements formation has been associated with the rapid growth of urban population
caused by rural immigration, triggered by difficult livelihood, civil wars and internal
disturbances. The result of this very rapid and unplanned urban growth is that 30% to 60%
of residents of most large cities in developing countries live in informal settlements
(UNHSP, 2005). Nowadays, informal residential environments (slums) are an important
component reflecting fast urban expansion in poor living conditions.
Densely populated urban areas in developing countries often lack any kind of data that
would enable the monitoring systems. Monitoring systems joining spatial (location) and
social data can be used for the monitoring, planning and management purposes. New
methods of monitoring are required to generate adequate data to help link the location
and socioeconomic data in urban systems to local policies and controlling actions. In the
past, rapid urban growth was quite difficult to manage and regulate when processes were
in progress. Available census data barely accounts for the reality, as in most cases, they
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408 Remote Sensing – Applications
are based on figures extrapolated from old census, carried out in the 1970s or, if recent,
they are obtained with poor accuracy, as informal settlements are difficult to survey
(Sartori et al., 2002). More can now be done at least to monitor the extent and
consequences of rapid urban growth. Where accurate maps of informal settlements and
relevant census data completely lack, answers can be found using independent survey,
derived from satellite or aerial technologies. Usage of satellite imagery nowadays enables
rather quick answers to questions such as: where informal settlements are, what was the
dynamics of their growth, how many people potentially live there, what basic services
inhabitants need. Among the main issues to be addressed in informal settlements are the
needs for potable water, waste evacuation, energy, education and health care facilities,
and crime control. It is believed these actions can be planned based on quality mapping of
the phenomena.
The spatial resolution of space-borne remote sensing has improved to such extent that their
products are comparable with the ones provided by aerial photography. Satellite images
taken with very high resolution (VHR) sensors, i.e. resolution around and below 1 m, enable
skilled user to identify and extract buildings, trees, narrow paths and other objects of
comparable size. A side effect of higher resolution is larger quantity of data which require
more storage capacities and processing costs. Detection of informal residential settlements
from satellite imagery is especially challenging task due to the microstructure,
merged/overlapping rooftops and irregular shapes of buildings in slum-like areas. High
spatial resolution is essential to facilitate extraction of individual buildings that are
characterized by small, densely packed shanties and other structures. Informal settlement
Kibera is composed of varying sizes of houses, where roofs can be a combination of many
different materials, and mainly unpaved road and path network. Typically this can produce
a spectral response on satellite imagery that is difficult to interpret and makes it difficult for
traditional classification strategies to differentiate across object class type.
Various approaches enable to extract data from imagery in urban environments.
Simultaneously with expansion of VHR satellite systems an object-based image analysis
(OBIA) was developed to answer new technological opportunities. OBIA approach works
in similar way as human brain perceives nature/environment, namely (high detailed)
image is segmented into homogeneous regions called segments or “image objects” (Benz
et al., 2004), which are then classified into meaningful classes, following the specific
context of the study.