**1.1 Remote sensing and GIS applications in urban planning**

Remote sensing can be applied in different aspects of urban planning such as (but not limited to) urban traffic analysis, urban environment analysis (air and water pollutions), and urban expansion. With recent developments in remote sensing technologies, remote sensing data can be exploited for urban studies. One example is the classification of land use based on high spatial and spectral resolution data such as orthomosaic and elevation images. Multidimensional spatiotemporal data can now be reliably obtained by sensors in different scale ranges and with flexible repetition rates [3].

#### **1.2 Remote sensing sensors for urban planning**

Medium- to high-resolution satellite imagery can be used by urban planners and land managers to monitor land conditions to support decision-making for sustainable urban development. Remote sensors are able to provide voluminous amounts of data, which can be exploited to produce/update GIS maps or for detection changes in urban land covers. High-resolution satellite sensors available on IKONOS, for example, can collect diverse geospatial data for studying vegetation. The sensors can sense 4 m resolution multispectral and 1 m resolution panchromatic, Quickbird imageries with 2.4 m resolution multispectral and 61 cm resolution panchromatic, and Worldview-4 imageries with 1.24 m resolution multispectral and 31 cm panchromatic. Medium-resolution satellite sensors, available on Landsat-8, Sentinel-2, and SPOT, are also valuable data sources for urban and vegetation change detection from various time periods during the same season, which further supports analyzing any past changes. Analysis of such data can then be used for decision-making and planning for further development of a particular urban area [4].

#### **1.3 Integration of GIS and remote sensing for urban planning**

Remote sensing data can be integrated with other spatial data to perform various types of full-fledged assessments. GIS techniques can be utilized to integrate the required spatial data and analytic data from various sources, such as field survey data, topographic maps, aerial photographs, and also archived data. The data can be represented as location (i.e., latitude and longitudes) or even as tabular attributes. GIS techniques play a substantial role in the data integration process of multilayer spatial information along with statistical information in various developmental scenarios [5].

#### **1.4 Methods and approaches for urban planning**

Multi-criteria decision-making (MCDM) is concerned with making a decision by evaluating multiple conflicting criteria. It embodies various methods and procedures where the gist is the formal incorporation of multiple conflicting criteria in

#### *Urban Planning Using a Geospatial Approach: A Case Study of Libya DOI: http://dx.doi.org/10.5772/intechopen.86355*

Moreover, timely selection requires effective planning and analysis and must consider multiple conflicting and disproportionate factors (such as those that have critical socioeconomic and environmental implications to different stakeholders). Urban planning application using remote sensing (RS) and geographical information systems (GIS) is one of the many areas that can be explored for city selection. Such applications would not only eliminate human bias but would also be able to

Remote sensing can be applied in different aspects of urban planning such as (but not limited to) urban traffic analysis, urban environment analysis (air and water pollutions), and urban expansion. With recent developments in remote sensing technologies, remote sensing data can be exploited for urban studies. One example is the classification of land use based on high spatial and spectral resolution data such as orthomosaic and elevation images. Multidimensional spatiotemporal data can now be reliably obtained by sensors in different scale ranges and with

Medium- to high-resolution satellite imagery can be used by urban planners and land managers to monitor land conditions to support decision-making for sustainable urban development. Remote sensors are able to provide voluminous amounts of data, which can be exploited to produce/update GIS maps or for detection changes in urban land covers. High-resolution satellite sensors available on IKONOS, for example, can collect diverse geospatial data for studying vegetation. The sensors can sense 4 m resolution multispectral and 1 m resolution panchromatic, Quickbird imageries with 2.4 m resolution multispectral and 61 cm resolu-

tion panchromatic, and Worldview-4 imageries with 1.24 m resolution

**1.3 Integration of GIS and remote sensing for urban planning**

**1.4 Methods and approaches for urban planning**

multispectral and 31 cm panchromatic. Medium-resolution satellite sensors, available on Landsat-8, Sentinel-2, and SPOT, are also valuable data sources for urban and vegetation change detection from various time periods during the same season, which further supports analyzing any past changes. Analysis of such data can then be used for decision-making and planning for further development of a particular

Remote sensing data can be integrated with other spatial data to perform various types of full-fledged assessments. GIS techniques can be utilized to integrate the required spatial data and analytic data from various sources, such as field survey data, topographic maps, aerial photographs, and also archived data. The data can be represented as location (i.e., latitude and longitudes) or even as tabular attributes. GIS techniques play a substantial role in the data integration process of multilayer spatial information along with statistical information in various developmental

Multi-criteria decision-making (MCDM) is concerned with making a decision by evaluating multiple conflicting criteria. It embodies various methods and procedures where the gist is the formal incorporation of multiple conflicting criteria in

make more objective decisions based on data.

*Sustainability in Urban Planning and Design*

**1.2 Remote sensing sensors for urban planning**

flexible repetition rates [3].

urban area [4].

scenarios [5].

**240**

**1.1 Remote sensing and GIS applications in urban planning**

the analytical process [6]. In the context of GIS, this refers to the spatial decisionmaking process based on GIS data with geolocation tags. Spatial decision-making techniques have been used to solve many GIS problems such as locating solar plants, urban planning, and project construction optimization [7]. Advanced MCDM methods include simple additive weighting (SAW) [8], analytic hierarchy process (AHP) [9], and TOPSIS [10]. Fuzzy set theory and random set theory are also MCDM techniques that incorporate sophisticated algorithms to resolve uncertainty in data [11–14].

TOPSIS is a MCDM technique that deals with real-world problems. It basically ranks criteria on the basis of the shortest distance from the positive ideal solution (PIS) and the farthest distance from the negative ideal solution (NIS) [15]. The work in [2] illustrates the application of a GIS-based MCDM tool for urban infrastructural planning. Awasthi et al. [16] presented a fuzzy TOPSIS method for selecting the best location for an urban distribution center in Canada. Uysal and Tosun [17] proposed a fuzzy TOPSIS-based maintenance management system using 17 criteria categorized under 5 contending parameters. The criteria were deduced from questionnaire feedbacks and interviews administered to company maintenance managers. In addition, Momeni et al. [18] presented a fuzzy TOPSIS-based method for maintenance strategy selection. Baysal et al. [19] developed a two-stage fuzzy method to determine the best sub-municipal projects among a set of proposed projects. The method simplifies the selection process and provides an objective decision outcome for stakeholders. Shelton and Medina [20] presented an integrated method to prioritize transportation projects in Wilmington Area, USA, based on multi-criteria decision support systems, AHP and TOPSIS methods. The process optimally selects the important routes that best serve the interest of the general public.

Based on the literature, TOPSIS has been successfully applied in many fields, producing reasonably accurate results. This study proposes an automated TOPSISbased solution for prioritizing urban projects based on criteria that meet sustainable development. Specifically, this work addresses the following questions on the value of remote sensing (and GIS) to urban planning:


From these questions, this study further looks at the automated prioritization of urban projects based on criteria that meet sustainable development practices. The specific objectives are (i) to identify factors that play major roles in urban development and (ii) to develop a geospatial solution based on TOPSIS for prioritizing projects for urban development.
