**5. Conclusions**

the percentage of land taken by settlements and industrial build-up, agriculture,

existing demands and plan for future needs. For instance, in Delhi, the maps illustrate that with industrial development, the urban expansion is

By using it, urban planners and development stakeholders could understand

accelerated. This mostly happened between 2003 to 2010; however, a considerable increase in construction sites shows that it will continue in the future, so it must

By using digitized spatial data, analysts would be able to study the target at different administrative levels: metropolitan, city, district or sub-district, and also other non-administrative units. These datasets make it possible to aggregate flexibly. One example is showing the proportion of sprawl by district, its density, the drivers of urban change and class evolution within urban areas. Together with environmental or socio-economic data, the data can prepare information on the proportion of population to urban growth, and can measure indicators like compactness, the ratio of green space to citizens, and the accessibility of these areas. The results of applying EO big data can be crucial for coordination between public, private and household investment in infrastructure, productive capital and housing, respectively. Thus, policymakers would be able to promote optimal spatial and transportation links between businesses, affordable housing and commercial units, health and education services and recreational areas. In addition, these views can be applied to support rural-to-urban migrants and ensuring that rapid urbanization is inclusive. Since EO big data methods spread across the world's megacities, and are refined and adapted, they will provide valuable tools to policymakers, and

natural or semi-natural vegetation and forest [101].

*Sample visualizations from the South Asia geospatial analysis [101].*

*Geographic Information Systems in Geospatial Intelligence*

greater benefits for the citizens of the future.

be planned [101].

**46**

**Figure 4.**

This book chapter briefly introduced ML and past research about the application of ML algorithms for processing of daily satellite imagery. It has been demonstrated several aspects of detecting and classification of Earth features merging into local geographical and geodetical system with further GIS development. The main purpose of the chapter is to provide existing resources for researchers to be aware of the up-to-date status of development of ML application in GIS in particular in studies of megacities.

The real potential of ML in GIS is not sufficiently developed yet. On one hand, both fields intersect in analytical discussions. At the same time, most GIS applications which are desirable for ML implementation, are driven by conventional approach and standard tools of commercial GIS packages.

Merging GIS and ML offers a potential mechanism to reduce the cost of analysis of spatial information by decreasing the amount of time spent on data interpretation. This integration allows the interpretive outcome from a small area to be transferred to a larger, geographically similar area, without the extra time and expense of putting geographers in the field for a time sufficient to cover geographical area.

ML can be considered both as a science and as engineering, depending on the goal. This technology is often seen as part of computing; however, it has links with various other areas including philosophy, psychology and linguistics. Its techniques can provide benefits within GIS over traditional methods, like statistical analysis, especially if data show some form of non-linearity. Thanks to such an opportunity of ML/GIS technology makes most successfully to apply for monitoring and observation consequences of megacity development.

Most people are unaware that they use artificial intelligence in their daily life. Finding solutions to decision-making issues by using models that allow decision makers to express their limitations and imprecise concepts that are used with large volume of geographic data, costs a lot. This chapter is expected to open opportunity to understand clearly fundamental aspects of ML/GIS development with basically related to the megacity studies.

*Geographic Information Systems in Geospatial Intelligence*

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