









































Pa
ge

 
1



Pa
ge

 
11

9

American Journal of  
Geospatial Technology (AJGT)

Optimization of  Road Construction Planning Using GIS and Remote Sensing 
Technologies

Rakibul Hassan1*, Md. Ariful Islam 2

Volume 4 Issue 1, Year 2025
ISSN: 2833-8006 (Online)

DOI: https://doi.org/10.54536/ajgt.v4i1.5844
https://journals.e-palli.com/home/index.php/ajgt

Article Information ABSTRACT

Received: July 30, 2025

Accepted: September 02, 2025

Published: September 18, 2025

Roads play a vital role in providing adequate transportation and developing sustainable 
infrastructure. The planning stage, including the decision on route and site locations, 
has considerable effects on the cost, time, and environmental friendliness of  the project. 
Conventional planning schemes are generally based on a time-consuming and resource-
intensive manual consideration of  many factors. This paper presents an integrated method 
for optimizing road construction planning, utilizing the facilities of  Geographic Information 
Systems (GIS) and Remote Sensing (RS). Utilizing spatial and satellite data, parameters of  
consideration such as topography, land use, soil type, hydrology, and environmentally sensitive 
areas are built into the model. Multi-criteria decision-making methods and optimization 
algorithms are used to evaluate alternative routes and select the final feasible alignment. 
The developed model improves the interoperability between geospatial data and assists 
automated decision support for road planning. Additionally, remote sensing data provide 
timely and large-scale information for detecting geohazards, slope instability, and ecological 
risks. The approach is tested and illustrated in an actual road construction project to show 
better efficiency in planning, less environmental impact, and fewer project risks. With the 
integration of  GIS and RS technology, this model provides an example of  a practical, cross-
disciplinary-based planning model for sustainable and economic road construction.

INTRODUCTION
Road is a key national developmental index, which 
promotes secure economic activities and the citizenry’s 
social well-being through the safe, reliable, and reasonable 
movement of  people and goods (Ali et al., 2021). 
Transport systems not only link rural areas with cities, but 
also facilitate trade, access to social services, and social 
integration. And so it is that the road planning has a crucial 
function to perform (Chen et al., 2025). The predominant 
one in the planning process is the choice of  the most 
appropriate position of  the planned road (Zhang et al., 
2025). Nonetheless, this is a very complicated process 
that involves many trade-offs between construction cost, 
environmental impact, ground conditions, and long-term 
viability. Once a road alignment is determined, significant 
project outputs (construction costs, maintenance costs, 
user benefits) are finalized. Thus, forethought in the 
design phase is crucial to prevent costly mistakes and 
inefficiencies down the road. In contrast to buildings, road 
construction projects typically face specific challenges 
such as different elevation levels of  land, shifting geology, 
and changing environmental conditions (Li & Xiao, 
2025). These dynamic elements render infrastructure 
projects more susceptible to unanticipated interference. 
The construction of  roads needs extensive data on 
topography, soil characteristics, hydrology, vegetation, 
and human habitations (Niyogakiza & Liu, 2025). It is 
not uncommon that planning and design have in the 
past been conducted in isolation, one from geological 
and environmental analysis, and in a manner that results 
in excessive cost and delay. Existing methods of  road 

Keywords
GIS, Road Construction, 
Optimization, Geospatial Analysis, 
Sustainable Planning

1 Department of  Civil Engineering, Rajshahi University of  Engineering & Technology, Bangladesh
2 Department of  Civil Engineering, Chittagong University of  Engineering & Technology, Bangladesh
* Corresponding author’s e-mail: nextgenresearch.info@gmail.com

alignment planning are usually road-oriented, with very 
little attention paid to the wider context of  environment, 
geotechnical, and socio-economic influences. Therefore, 
the planning process is time-consuming, discontinuous, 
and inadequate. To minimize the challenges, the GIS and 
remote sensing approach offer an integrated framework 
to plan the road construction effectively (Pelden et al., 
2025). A geographic information system (GIS) allows 
the gathering, storing, managing, and analyzing of  spatial 
information, while remote sensing facilitates the timely 
and accurate monitoring of  the Earth’s surface through 
satellite images and aerial photographs. By integrating 
these technologies, planners can analyze multiple data 
sets, including land use, slope, elevation, vegetation, water, 
and socio-economic characteristics, in a single analytical 
framework (Başkent & Başkent, 2025). This integrated 
method allows easier decision-making processes and 
more accurate identification of  feasible road alignments. 
One of  the major problems in road planning is data 
interoperability and integration. The road project is a 
multifaceted social and technical object that relies on the 
inputs of  engineering, geotechnics, environment, and 
socio-economics (Spanidis et al., 2025). Remote sensing 
offers dependable and broad information, including 
DEMs, soil moisture estimations, and land cover maps. 
When incorporated into GIS, these data sets support 
various spatial analyses. Slope maps generated from 
DEMs, for instance, assist in locating sites susceptible to 
landslides or soil erosion, and land use land cover data 
indicates environmentally sensitive zones that must be left 
untouched (Hossen et al., 2025). The combined analysis 



Pa
ge

 
12

0

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 119-128, 2025

guarantees that road layout is now based not only on 
cost and distance, but also on the maintenance of  a safe, 
environmentally friendly system (Al Hawarneh & Shahria 
Alam, 2025). Another important aspect of  road design 
is the analysis of  different alignments. Several alternative 
paths are typical for any prospective road (Buuveibaatar 
et al., 2025). These alternatives need to be compared 
by decision-makers according to several criteria. The 
straightest path isn’t necessarily the best, especially on 
those marginal numbers and where alternatives ultimately 
may be cheapest, safest, better for the environment or 
man, or more harmonious with the communities through 
which these routes play out. One-variable decisions, 
such as basing decisions on price, are not always the best 
choice. As such, multi-criteria decision-making techniques 
that utilize GIS and remote sensing are required for 
the simultaneous evaluation of  alternative options. 
Recently, simulation optimization methods based on, 
genetic algorithms (GAs) have become more popular for 
alignment planning (Liu et al., 2025). These approaches 
enable planners to specify the objective combination of  
tangible (minimum construction cost, travel distance, 
etc.) and intangible (environmental disturbance) criteria, 
and then optimize for what alignment best meets the 
criteria above. Integrated with the GIS and remote 
sensing data, optimization models can perform well with 
a data-driven approach with less manual intervention and 
subjectivity (Dritsas & Trigka, 2025). This improves the 
economy and precision of  road construction planning. 
The application of  GIS and remote sensing-derived 
models has the following advantages over conventional 
methods. It allows such a cross-disciplinary approach 
as integrating engineering, environmental, and socio-
economic perspectives in a single framework. This 
integrated perspective improves the relationship between 
the technical design and the actual geospatial context. 
Secondly, using remote sensing data increases the 
capability to identify potential hazards (such as flood 
risk areas, unstable slopes, or deforestation zones), 
which in turn can lead to more proactive planning and 
turnaround decisions before the construction. The third 
benefit is that combining spatial data with project data 
enhances the accuracy of  project-related analysis, such as 
earthwork volume calculation, material estimation, and 
cost prediction (Li et al., 2025). In conventional practice, 
such calculations can be time-consuming and subject 
to error, such that the overall cost of  the project tends 
to rise. In this regard, GIS and remote sensing provide 
more accuracy, fewer delays, and more reliable cost 
estimation. And the visualization function of  GIS can 
facilitate easier understanding of  complicated data for 
planning, engineering, and decision-making. Maps, 3D 
models, and so on can be produced to show how various 
alignment choices would interact with the topography 
and environs. This enhances technical analysis and 
simplifies communication with interested parties, 
including government officials, environmental agencies, 
and community members (Tumpa & Naeni, 2025). Early 

involvement of  stakeholders facilitates the identification 
of  possible conflicts and the integration of  social 
issues with the planning. In general, the incorporation 
of  GIS and remote sensing technologies has become a 
paradigm shift in road development planning. Through 
facilitating powerful and extensive data analysis, multi-
criteria optimization, precise visualization, reducing the 
waste of  resources, cost reduction, and sustainability of  
infrastructure are addressed. When contrasted to the 
standard approaches, it makes planning more automatic, 
efficient, and environmentally and socially friendly.

LITERATURE REVIEW
Geographic Information Systems (GIS) have emerged as 
the most powerful technology for spatial data handling, 
analysis, and visualization. They are used in various 
fields like transportation, urban planning, park path 
management, disaster mitigation, logistics, etc. As in the 
case of  road planning, GIS helps in dealing with large-
scale geospatial data, such as topography, hydrology, 
land use, and soil data (Yan et al., 2025). This capability 
to overlay spatial layers and analyze relationships is what 
makes GIS indispensable for infrastructure. Through 
GIS applications, professionals can incorporate the 
project environment into the planning process, which 
is essential for infrastructure projects due to their 
complexity (Pelden et al., 2025). For example, slope 
maps, DEMs, and land cover data can all be scrutinized 
at once to reveal countertops, flood-prone areas, or 
data inconsistencies. It supports the advanced planning 
of  site layouts through life-like simulations of  material 
transport, accessibility, and prevention of  space conflicts 
(Şimşek et al., 2025). Additionally, GIS is useful in project 
management issues, like schedule management, cost 
forecasting, and safety plan preparation using detailed 
spatial queries and simulations. are executing as there is 
a lack of  filter operators. The rising need for sustainable 
and data-driven infrastructure planning has made GIS go 
from being a leading force to a driving force in the global 
infrastructure industry. Remote sensing is a good source 
of  spatial data at a large scale and in real-time for GIS 
analysis (Babbar & Rani, 2025). Remote sensing through 
satellite imaging, aerial photography, and LiDAR (light 
detection and ranging) is increasingly playing an important 
role in road planning, where it provides detailed data on 
land cover, vegetation type, as well as soil moisture and 
terrain morphology. Remote sensing-derived DEMs are 
widely used for slope stability analysis, cut-and-fill data 
generation, and erosion risk prediction. Remote sensing 
is also useful in monitoring land use and environmental 
changes (Yono et al., 2025). For instance, land cover 
classification aids planners by avoiding development in 
ecologically fragile zones, and a vegetation index is used 
to point out deforestation or wetland regions that should 
not be disturbed. The timely and extensive acquisition 
of  data and resources in hundreds of  thousands to 
millions of  km2 inaccessible areas has eliminated the 
need to conduct large field surveys and saved time and 



Pa
ge

 
12

1

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 119-128, 2025

cost. Furthermore, high-resolution satellite imagery can 
be used to identify specific coordinates of  settlements, 
rivers, and key infrastructure, which is required when 
considering alternative road options. Integration of  
GIS and RS has improved the process of  road planning 
through multi-criteria considerations. Remote sensing 
gives raw spatial information, while GIS is the tool 
used to organize and analyze thus, the fusion of  both 
technologies is instrumental in the best planning and 
implementation of  road infrastructure. GIS and RS are 
interdependent technologies that, combined, provide a 
robust platform for road construction planning (Chenchu 
et al., 2025). Remote sensing contributes with continuous 
spatial data coverage, and GIS with handling, analyzing, 
and visualizing the data in decision-making. Integration 
of  the software is a capability that allows planners to 
perform spatial analysis with greater complexity, such 
as relief  modelling, slope stability analysis, watershed 
delineation, and land use suitability analysis (Melsse et 
al., 2025). For infrastructure projects, the integration of  
GIS and remote sensing has been adopted to locate the 
best routes by avoiding environmentally sensitive areas, 
decreasing the costs of  earth movements, and lessening 
impacts on local populations. The combination of  these 
tools allows a more holistic perspective that takes into 
consideration engineering and environmental aspects 
as well. Planners may use the integration of  several 
spatial datasets to construct a 3D terrain model, to 
design different alignments, and to assess impacts on 
the economy and the environment. Planning of  road 
alignment is, by nature, a multi-criteria problem, as 
distance, cost, safety, environmental, and social issues 
need to be viewed in concert. Integration of  GIS and 
remote sensing systems for planning enables planners to 
weigh various criteria and simultaneously explore several 
alignment alternatives (Xiaoyu et al., 2025). Prioritization 
of  alternatives is commonly carried out using Multi-
Criteria Decision-Making (MCDM) techniques like 
Analytical Hierarchy Process (AHP) and Weighted Linear 
Combination (WLC). In addition, optimization methods 
such as genetic algorithm (GA), linear programming 
(LP), and dynamic programming have been used to 
optimize road planning (Ramyar et al., 2025). Especially 
the GAs can find close-to-optimal solutions by constantly 
looking for better alignments that minimize the costs and 
environmental impact and guarantee the safety. When 
supplemented with GIS and remote sensing information, 
these optimization approaches are even more robust and 
practical with fewer requirements for manual corrections. 
Apart from alignment planning, GIS and RS can also 
be used at all stages of  road construction (Akindele et 
al., 2025). During construction, material flows can be 
monitored, site layouts can be optimized, and safety zones 
can be monitored using GIS. Real-time monitoring of  
land changes with remote sensing can also provide early 
warnings for erosion, landslide, and flooding risks. Post-
construction, they help with maintenance and monitoring 
as well, looking for surface damage and vegetation 

intrusion, and noting drainage issues. GIS, with its ability 
to visualize, becomes a means to communicate among 
various participants (Hannum et al., 2025). With maps, 
3D models, and interactive tools, planners can display 
options to decision makers, citizens, and environmental 
regulators. This process of  engagement enhances 
transparency, helps minimize conflict, and helps to 
ensure that actions support wider development objectives 
(Tumpa & Naeni, 2025). The task to be most concerned 
about in road construction planning is the optimal 
arrangement of  alignment. The purpose of  the challenge 
is to find a route from some start point to some end point 
while minimizing total cost and environmental “damage”. 
However, the conventional approaches to selection 
of  alignments are labor-intensive, and the process is 
subjective. Contemporary methodologies use GIS and 
remote sensing information for the stricter thematic 
mapping of  terrain, land use, and geology (Jabeen et al., 
2025). A variety of  optimization models have been used 
in this domain, such as linear programming, dynamic 
programming, network optimization, and heuristics. 
In this, genetic algorithms have proved to be the most 
encouraging owing to the fact that they could produce real 
alignments and have co-optimized vertical and horizontal 
paths, resulting in tremendous gains in both time and 
cost. Aided by remote sensing data and GIS analysis, these 
algorithms can offer a reliable and automatic solution that 
improves the traditional planning approaches.

MATERIALS AND METHODS
As such, the developed methodology aims at combining 
GIS and remote sensing for road planning in a holistic 
method. In the pre-design phase of  a highway route, a 
reliable analysis and accurate spatial information are needed 
to assist with decision-making. For doing so, it includes an 
integrated GIS remote sensing model, an analysis layer, 
and an optimization process. The coupled model utilizes 
key geospatial and environmental data from remote 
sensing and GIS databases to facilitate advanced analysis 
and optimization. GIS offers high-resolution spatial and 
topographic data, and remote sensing can provide real-
time geodata information on terrain, vegetation, land use, 
and environmental situation. Then these data sets are used 
by the analysis layer: geological evaluation, network and 
accessibility analysis, cost evaluation, and environmental 
impact assessment. These analyses are then used in 
the optimization to produce alignment alternatives for 
road construction. The first step of  the workflow is the 
creation of  precise topographic information (DTM and 
DSM) and geographic information, along with satellite 
imagery and remote sensing-derived data. The datasets 
are used to create a digital terrain model (DTM) by GIS, 
so that they can be analyzed on a spatial and geological 
level. Incorporation of  remote sensing improves the 
identification of  geohazards, constraints, and land cover 
changes that affect route alignments. As preliminary 
alignments are formulated, several options will be 
developed and screened utilizing optimization algorithms, 



Pa
ge

 
12

2

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 119-128, 2025

taking the cost, safety, environmental, and engineering 
feasibility into account. Models that require alteration 
feed back into the model, therefore being iterative and 
adaptive planning. It uses GIS and remote sensing data 
integration, automation of  processes, minimizing risk of  
human error, as well as storing data in a structured way for 
analytical functions that are often developed specifically 
for the task at hand. While GIS can accommodate a variety 
of  analysis functions, remote sensing transmits timely and 
accurate information required for large-scale estimations. 
Together, the technologies provide optimal road alignment 
(considering engineering efficiency, economic feasibility, 
and environmental sustainability). This integrated 
solution enables much better planning in the design of  
road construction projects using data-driven insights at 
the disposal of  decision-makers. The geospatial ontology 
offers the semantic basis of  integrating GIS and remote 
sensing with road construction planning. An ontology is 
the conceptual model of  a domain, which allows us to 
define the concepts and to specify how they are related, 
in order to enable advanced analysis. In this regard, the 
GIS and Remote Sensing tools in the transportation 
infrastructures are considered with a semantic framework 
concept responsible for data harmonization, analysis, 
and decision making. In this conceptualization, primitive 
concepts are key classes of  spatial and environmental 
information (e.g., terrain models, land use categories, 
and vegetation indices), and defined concepts describe 
the finer-grained subclasses derived from these primitive 
classes. For instance, a terrain model from digital elevation 
data is primitive, and slope classification or elevation 
zones from that model are defined. This structured 
conceptualization facilitates the linking of  numerous 
datasets needed for well-founded planning of  the road 
alignment. The use of  semantic structuring makes it 
possible to effectively include information pertaining to 
space and environment from remote sensing images in 
GIS-based studies. Remote sensing produces extensive 
geospatial data, such as topographic, land cover, 
vegetation health, and hydrologic patterns, whereas GIS 
stores this data in relational constructs that are conducive 
to analysis and visualization. A GIS spatial database may 
have tables correlated with spatial features, including 
their attributes. Once integrated with semantic modelling, 
these attributes can be converted into understandable 
properties, helping to solve complex geospatial queries 
and time-critical optimization processes. This abstraction 
allows a simple and transparent mapping from remote 
sensing acquisition to data structures usable in any GIS 
context. For example, imagery from satellites may be 
turned into raster that depict land cover, which raster in 
turn may be classified and stored as attributes in layers 
in GISs. Such values can then be incorporated into 
optimization algorithms, determining valid roads that 
have the minimal impact on the environment and society. 
And by the semantic concepts structuring of  relationships 
among the GIS features and remote sensing derived 
data manipulation, decision makers can perform precise 

multi-criteria analysis for decision-making, and solve 
questions. Diagrams representing such a process may 
be used to visualize how spatial databases are translated 
into semantic representations and how remote sensing 
data and GIS layers are integrated to compute a complete 
decision-support environment for road construction 
planning.

Figure 1: A geographic information science (GIS) entity 
representation as an RDF.

Road construction planning Ontology matching is 
introduced in the integration of  the heterogeneous 
datasets in GIS and remote sensing forth road construction 
planning. Since these technologies usually represent spatial 
and environmental details in various structures and forms, 
the ontology matching offers a systematic procedure for 
resolving them as a single framework. This process aims 
to semantically align various types of  knowledge including 
topography, land use, vegetation indices, and hydrology 
to enable better data-driven decision support. It is the 
structural similarity that is essential to this integrated 
process. Graph-based ontology matching methods are 
employed to discover and quantify the relations between 
GIS-based spatial entities and the remote sensing-based 
environmental characteristics. In this method, we also 
generalize both of  GIS data (e.g., vector layers like roads, 
rivers, and land parcels) and remote sensing data (e.g., 
raster layers like elevation, slope, and vegetation cover) 
into a bipartite graph. Structural relationships between 
these graphs are then tested to recognize significant 
correspondences. There are several phases in the process 
of  ontology matching. First, GIS and remote sensing 
data are converted into spatial graph representations, so 
their structure can be expressed in a consistent manner. 
Second, we use coordination rules of  vector-based and 
raster-based data to align heterogeneous data types which 
are from different resolution or attribute presentation. 
Third, matrix representations of  both the datasets 
are made, initial values of  similarity are specified and 
convergence is threshold is set. Last, an iterative matching 



Pa
ge

 
12

3

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 119-128, 2025

process is implemented until the stable 1:1 mapping 
from the GIS and remote sensing data set features to 
the classes is established. Results of  ontology matching 
establish a one-to-one relation between geographical 
entities and environmental causes that leads to the merge 
of  topographical, climatic and land-use data into a unified 
DSS. By doing so, it becomes possible for road planners 
to perform multi-criteria analysis, improve alignment 
routes, and reduce environmental and social impacts 
during road construction.

Figure 2: RDF bipartite graph and matrix representation 
of  the GIS ontology example

In road construction planning, data from GIS and remote 
sensing are typically generated in diverse formats, such as 
shapefiles, raster images, and tabular datasets. However, 
these formats are not directly accessible for semantic web 
queries, as traditional GIS and remote sensing tools do not 
support them. To address this challenge, heterogeneous 
datasets are transformed into the Resource Description 
Framework (RDF), which provides a standard semantic 
representation. Once GIS and remote sensing datasets 
are formalized into RDF graphs, querying becomes 
possible using SPARQL, the standard query language for 
RDF. SPARQL enables planners to retrieve, filter, and 
analyze data across integrated spatial and environmental 
layers. For example, queries can be executed to identify 
road alignments that minimize slope constraints, avoid 
ecologically sensitive zones, or optimize land acquisition 
costs. The results of  these queries can be exported 
in semantic web–compatible formats such as Turtle, 
N-TRIPLE, JSON-LD, and RDF/XML, ensuring 
accessibility across various applications. This provides a 
unified knowledge base where spatial features and remote 
sensing attributes are integrated into a single semantic 

schema. By applying SPARQL, an integrated RDF 
graph of  GIS and remote sensing data is established. 
This graph consolidates geometries (e.g., road networks, 
terrain elevation) and complementary properties (e.g., soil 
stability, vegetation indices), offering a comprehensive 
decision-support environment. This integration allows 
road construction planning to move from fragmented 
datasets toward a semantically enriched model that 
supports multi-criteria optimization and sustainable 
infrastructure development.

RESULTS AND DISCUSSIONS  
The planned scheme will use several layers of  analytical 
tools in GIS and remote sensing technologies to provide 
optimized solutions for alignment selection. Central 
analyses consist of  geological and geospatial analysis, 
network analysis, and cost estimation. Geomorphological 
and geospatial investigation support in locating potential 
hazards in the project vicinity, such as instability of  slopes, 
susceptibility of  areas to flooding or erosion (Rawat et 
al., 2025). High-resolution remote sensing images for 
terrain and environment understanding. The remote 
sensing images are the scale images of  the terrain and 
environment, while the GIS can build out cover, elevation, 
and hydrological conditions. This integration lets planners 
decide which areas will be affected and design a plan 
to mitigate impacts or to bypass low-safety areas (Van 
den Hurk et al., 2014). Network analysis allows efficient 
determination of  routes that link points with prescribed 
orientations in space, taking into account constraints 
revealed by geological and geospatial analysis. By using 
vector GIS data and remote sensing-based terrain models, 
this analysis can calculate several alignment options in 
terms of  travel distances, accessibility, and positioning 
in the road network. The cost estimates are combined 
between construction and maintenance aspects, using 
geospatial and remote sensing information to estimate 
earthwork volumes, material needs, and possible 
environmental remediation costs (Schnebele et al., 2015). 
By integrating GIS and remote sensing technology, 
geotechnical and environmental data can be visualized 
in detail and can be used to promote closer cooperation 
among planners, engineers, and environmentalists. 
Dangerous areas and key ground features can be easily 
distinguished, adding to the decision-making process 
and assisting in altering proposed alignments. These 
analyses provide the basis of  optimization for selected 
road alignments that are safe, cost-effective, and will not 
cause significant environmental impacts. Featuring direct 
visibility to the possible risks and resource demand, this 
analysis module effectively reduces planning effort and 
helps to work out the right road construction strategies.
It is necessary to carry out accurate geotechnical survey 
work on the construction site before the road construction, 
as a road or pavement is subjected to severe stresses and 
pressures. Among the most frequent and costly problems 
in road construction are slope failures/landslides 
(Otoma & Ayothiraman, 2025). GIS in combination 



Pa
ge

 
12

4

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 119-128, 2025

Figure 3: An example of  geological analysis.

with remote sensing technologies can be used to forecast 
the susceptibility of  landslides through the fusion of  
topological, geological, and climatic factors. Additional 
analysis, including maximum rainfall, historical average 
annual rainfall, and historic landslide history, is then used 
to predict the probability and magnitude of  landslides in 
the area of  the project (Klose et al., 2016). Seismic risk 
also has an essential influence on decisions about road 
construction planning, particularly in seism genic areas. 
Soil liquefaction and decreased bearing capacity due to 
earthquakes can result in embankment failure or surface 
crack growth. Analysis of  historical earthquake records 
and active fault mapping of  GIS and remote sensing data 
can help in delineating potential seismic risk zones (Geiß 
& Taubenböck, 2013). Designers use this data to develop 
proposals to mitigate the impacts of  earthquakes on the 
road system. Road building projects can have profound 
socio-economic and environmental implications for both 
the surrounding localities and cultural heritage, forests, 
wetlands, and even ecologically sensitive areas. Noise 
and air pollution can also rise during and after building. 
The GIS-based spatial analysis helps planners overlay 
road alignments on maps of  socio-environmental data 
to avoid or minimize the impact on environmentally or 
socially sensitive areas. This holistic approach facilitates 
the decision for routes that are both technically feasible 
and environmentally/ socially acceptable.
Earthworks constitute a significant proportion of  the 
total project cost & among the total cost, cut and fill 
take approximately 25%. Precise computation of  cut 
and fill can play a crucial role in cost reduction and final 
adequacy of  the material itself. When a BIM modeling 
system can be combined with terrain and soil data 

obtained from GIS and remote sensing, a complete 
model can be established to calculate the earthwork 
volume. The road’s vertical profile, with reference to the 
center line, is used to determine cut and fill quantities 
that keep the cut and fill to a minimum, reducing costs 
and balancing the earthwork. This methodology ensures 
the central elevation is accomplished with the least 
amount of  earthwork and that the alignment criteria are 
met. For the optimization of  road alignment, several 
options are produced and assessed from the economic, 
environmental, and geotechnical points of  view. The GIS 
and remote sensing data are used as essential inputs for 
the optimization algorithms, such as genetic algorithms. 
Each candidate alignment is encoded as a set of  points 
on the centerline, the coordinates of  which constitute 
a chromosome to be optimized by GA. The combined 
model computes costs such as earth work, pavement, 
right of  way, user costs, and the cost of  violations in 
environmental or design constraints. The GA constantly 
modifies alignment layouts to reduce the overall cost 
function while taking into account safety, according to 
standards for construction and environmental impact. 
The model developed considers a number of  components 
in its cost function, including the cost of  pavements, 
earthwork, right-of-way, the cost incurred by the user, 
and, for moves that violate environmental or design 
conditions, penalty costs. The cost of  the pavement 
depends on the cost per unit, road length, and width, and 
the cost of  the earthwork includes the cost of  cut and 
fill and transport of  materials. Right-of-way expenditure 
is charged based on the area of  land altered by the road 
and the value of  the land, while user cost is based on 
the length of  the alignment in terms of  travel distance 



Pa
ge

 
12

5

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 119-128, 2025

(Geremew, G., 2024). Penalty costs are, of  course, imposed 
when the alignment traverses ecologically sensitive lands 
or does not satisfy design criteria, thus making sure 
that the optimized solution adheres to biophysical and 
regulatory considerations. When GIS mapping, remote 
sensing, and optimization algorithms are integrated, the 

proposed method offers a tool for road construction 
planning. It facilitates planners in an efficient assessment 
of  various alignment options, considering environmental 
and socio-economic conditions, geological hazards, and 
construction costs, so that the road infrastructure can be 
more sustainable, safe, and cost-effective.

Figure 4: (a) A series of  PIs; (b) geometric specification of  typical horizontal alignment.

The project addressed in this paper is a significant road 
network in a region of  high environmental and geological 
importance. The total length of  the route is around 283 
km and has an expected investment of  44.63 billion. The 
design speed for the road is 100km/h. Several factors 
make project planning and execution complex: the area is 
rugged and challenging to access, and the alignment has to 
avoid other infrastructure (railways, bridges, and pipelines); 
getting an optimal route requires careful attention to 
environmental and economic factors. Furthermore, the 
project area is located in an earthquake-prone region, and 
some severe earthquakes might have caused the failure 
of  the road. Soil liquefaction may occur in response to 
seismic activity, resulting in embankment slide and surface 
cracking. A comprehensive seismic hazard analysis was 
carried out for use in design loads and mitigation planning 
(Sousa & Tsionis, 2025). Construction work mainly 
consists of  mass excavation and backfill work, formation 
of  subgrade and base, road surfacing, drainage setting 
up, and slope reinforcement. GIS and remote sensing 
data were used in the present case to strengthen the road 
alignment optimization. Satellite images (high resolution), 
the LiDAR-derived terrain model, and topographic data 

sets were transferred to GIS for spatial analysis. The GIS 
database contained data on terrain elevation, slope, soil 
type, hydrology, vegetation, and available infrastructure. 
The use of  remote sensing data also improved the quality 
of  hazard identification, such as landslide susceptibility 
and flood-prone areas. The BIM of  the road was created 
to complement the geospatial analysis. Road components, 
pavement, embankment, bridge, tunnel, etc., with 
geometry and semantic information, were modeled by a 
software machine. The use of  the BIM model enabled 
project information such as cross sections, plans, profiles, 
and material quantities. The BIM and GIS data were 
integrated based on advances in the semantic web to 
provide interoperability and to ultimately calculate the 
project cost, environmental influence, and construction 
possibility (Sarigul & Gunaydin, 2025). In the integrated 
model, the costs of  highway construction, including 
pavement, earthworks, and user costs, were calculated by 
length, width, and material. This combination similarly 
facilitated not only planners to evaluate efficiently 
various alignment situations and to develop an optimal 
route based mainly on economic, environmental, and 
geotechnical effects.



Pa
ge

 
12

6

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 119-128, 2025

Table 1: The summary of  cost and environmental impacts of  the alignment alternatives.

Alternatives Highway Alignment 1 Highway Alignment 2 Highway Alignment 3
Length (km) 270.6 303.1 282.9
Total Cost (Billion BDT) 1874 2107 1856
Pavement Cost (Billion BDT) 380 430 400
User Cost (Billion BDT) 426 478 446
Right-of-Way Cost (Billion BDT) 199 223 208
Penalty Cost (Billion BDT) 197 136 133
Earthwork Cost (Billion BDT) 672 840 669
Geological Risk Low Low Low
Impacted Historical Sites (m²) 0 0 0
Impacted Farmland (ha) 1358 1204 909.53
Impacted Forest (ha) 508.5 487 444.9
Impacted Pasture (ha) 290.1 276.9 256.98

It forms part of  an essential regional road network in 
Bangladesh. The overall length of  the project is 283 km, and 
it passes through many districts with different geological, 
geographical, and environmental conditions. The design 
speed is 100 km/h and planning the project faces several 
challenges: (1) to work through a complex natural context 
(such as flood-prone region and depression land); (2) to 
deal with some bottlenecking obstacles including rivers, 
bridges, and local highways; and (3) to achieve the optimal 
alignment in terms of  environmental conditions such as 
wetlands, and impacts on socio-economy. The project 
vicinity is also subject to possible seismic risks such as soil 
liquefaction during earthquakes. Construction activities 
include earthwork, subgrade, base, embankment, surface, 
drainage, and slope reinforcement. Topographic and 
surrounding site features were mapped using ArcMap and 
satellite imagery. They developed a CityGML schema to 
represent the objects, the relationships, and the attributes 
of  the surroundings. A direct transformation from the 
CityGML to OWL was made. Similarities between the 
obtained OWL representations of  the highway design 
(from BIM) and the surrounding environment (from 
GIS) were matched using Python scripts to integrate 
them using semantic web technologies. SPARQL queries 
were then employed to retrieve elements (e.g., horizontal 
and vertical alignment segments) defined in BIM but 
do not have direct representation in CityGML. The 
geometrical and geographical information is exported 
to the GIS module for 3D visualization of  the artery 
and surroundings. Earthwork calculations and boundary 
costs were calculated in kilograms of  ANP (ET) and 
value, respectively, considering the GIS model.
The analysis within the GIS was used to identify 
environmentally sensitive areas (ESA), which were in turn 
used to determine penalty costs to mitigate environmental 
damage. The employee teams made unanimous responses 
that the 3D integrated model helped them to understand 
the project better, communicate with the stakeholders 
effectively, and mitigate the risks. Alignment planning of  
highways of  Bangladesh should consider environmentally 

sensitive and socially sensitive areas such as wetland, 
forest, agricultural land, residential, and necessary 
commercial regions. The trade-off  values for each land-
use type were included in the optimization. The Maximum 
Contributing Classes (MaxC) were imposed based on the 
priority of  each land-use. As an example, historical sites 
and protected areas are assigned MaxC = 0 to exclude 
the highway from them; agricultural land and residential 
areas were minimized, but the model allows for some 
impact on them. The optimal highway alignment was 
found using a genetic algorithm (GA) based on minimum 
total project cost, considering the environmental and 
multiple design constraints (Al-Hadad et al., 2024). 
The BIM module created the initial alignment route 
according to code and project requirements. These data 
were then imported to GIS, with the objective function 
including five cost elements: pavement cost, earthwork 
cost, right of  way cost, user cost, and environmental 
impact cost. The GA mechanic successively improved 
alignment options over 100 generations. The overall cost 
calculations were established for each generation on the 
integrated BIM-GIS model. The best alignment should 
take into account minimizing economic cost, avoiding 
hazardous sites, and reducing environmental impact. 
Alignment 3, which was chosen as the best alternative, 
also had the shortest total length (283 km), influencing 
about 910 ha of  farmland, 445 ha of  forest, 257 ha of  
pasture, 59 ha of  park, and 46 ha of  residential land, but 
without influence on the historical heritage protection 
area. The optimized alignments were tested in terms of  
sensitivity to the cost components. We examined three 
objective function scenarios:
1. C = Pavement cost + User cost

• Alignment passes through hazard-sensitive areas. 
Vertical alignment largely follows the terrain, but 
earthwork costs increase significantly in hilly regions.
2. C = Pavement cost + User cost + Right-of-way cost 
+ Penalty cost

• Alignment avoids environmentally sensitive areas but 
is longer and less optimal in terms of  vertical profile.



Pa
ge

 
12

7

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 119-128, 2025

3. C = Pavement cost + User cost + Right-of-way cost + 
Penalty cost + Earthwork cost
oAlignment minimizes overall cost, avoids hazards and 
environmentally sensitive areas, and closely follows the 
terrain profile.
The results indicate that considering all major cost 
components simultaneously is crucial for realistic 
highway optimization. The integrated BIM-GIS model 
enabled accurate estimation of  individual cost items for 
each alignment alternative. Earthwork was the largest 
cost component, followed by user and pavement costs. 
Alignment 3 had the lowest total cost, demonstrating that 
the integrated approach effectively balances construction 
cost, environmental impact, and hazard mitigation. Table 
1 summarizes the cost and environmental impacts for the 
three alternatives.

CONCLUSIONS
Road construction planning in Bangladesh is a complex 
and resource-intensive process, especially when evaluating 
multiple alignment alternatives across varied geographical 
and environmental conditions. This study proposes an 
integrated model that combines GIS, remote sensing, 
and BIM to streamline the planning process. Semantic 
web technologies and ontology-based approaches were 
employed to ensure seamless integration of  diverse data 
sources.
The proposed model improves efficiency in geotechnical 
assessment and infrastructure planning, bridging the gap 
between highway design and site analysis while providing 
3D visualization of  the project. Optimization algorithms, 
specifically genetic algorithms, are used to select the 
most suitable alignment, minimizing construction 
costs and controlling geohazards simultaneously. The 
automated framework allows for the timely identification 
of  optimal alignments, reducing manual effort and 
increasing precision compared to conventional methods. 
Application of  the model to a highway project in 
Bangladesh demonstrates its practical relevance. As 
GIS, remote sensing, and BIM technologies continue 
to advance, this integrated approach can be widely 
adopted for efficient, informed, and cost-effective road 
construction planning. The model is also adaptable for 
other linear infrastructure projects and can be integrated 
with additional technologies for broader applications in 
construction and environmental management.

REFERENCE
Akindele, O., Ajayi, S., Oyegoke, A. S., Alaka, H. A., & 

Omotayo, T. (2025). Application of  Geographic 
Information System (GIS) in construction: a 
systematic review. Smart and Sustainable Built 
Environment, 14(1), 210-236.

Al-Hadad, B. M. A., Nadir, W. H., & Jukil, G. A. M. (2024). 
Intelligent optimization of  highway alignments: A 
novel approach integrating geographic information 
system and genetic algorithms. Engineering Applications 
of  Artificial Intelligence, 133, 108037.

Al Hawarneh, A., & Shahria Alam, M. (2025). Maintenance 
optimization model for existing reinforced concrete 
bridge piers based on an integrated life-cycle cost 
and performance-based design approach. Journal of  
Structural Engineering, 151(10), 04025157.

Ali, E. B., Anufriev, V. P., & Amfo, B. (2021). Green 
economy implementation in Ghana as a road map 
for a sustainable development drive: A review. Scientific 
African, 12, e00756.

Babbar, H., & Rani, S. (2025). AI-Driven integration 
of  remote sensing and geospatial data for 
Enhanced environmental monitoring and decision-
making. International Journal of  Remote Sensing, 1-17.

Başkent, E. Z., & Başkent, H. (2025). Integrating Technical, 
Socio-Economic, and Sustainability Dimensions for 
Spatial Stratification of  Ecosystem Services Using the 
AHP Method. Environmental Management, 1-18.

Buuveibaatar, M., Brilakis, I., Peck, M., Economides, 
G., & Lee, W. (2025). A Conceptual Framework for 
Planning Road Digital Twins. Buildings, 15(3), 316.

Chen, X., Hu, R., Luo, K., Wu, H., Biancardo, S. A., 
Zheng, Y., & Xian, J. (2025). Intelligent ship route 
planning via an A∗ search model enhanced double-
deep Q-network. Ocean Engineering, 327, 120956.

Chenchu, M. K., Ruikar, K., & Jha, K. N. (2025). A 
network-based framework for enhancing data 
integration and usage in highway infrastructure 
decision-making. Construction Innovation.

Dritsas, E., & Trigka, M. (2025). Remote sensing and 
geospatial analysis in the big data era: A survey. Remote 
Sensing, 17(3), 550.

Geiß, C., & Taubenböck, H. (2013). Remote sensing 
contributing to assess earthquake risk: from a literature 
review towards a roadmap. Natural Hazards, 68(1), 
7-48.

Geremew, G. (2024). Modeling and analyzing the 
impact of  on-street parking on traffic flow: a study 
of  the main highway in Debre Markos Town, 
Ethiopia. Transportation, 1-35.

Hannum, K., Wellstead, A. M., Howlett, M., & Gofen, 
A. (2025). Leveraging GIS for policy design: 
spatial analytics as a strategic tool. Policy Design and 
Practice, 8(1), 35-49.

Hossen, S., Uddin, M. S., Ali, Y., & Rana, P. (2025). 
Integrated analysis of  land use and land cover changes 
and landslide susceptibility: a machine learning 
approach in Rangamati Sadar, Bangladesh. Natural 
Hazards, 1-22.

Jabeen, S. F., Bhat, M. S., Alam, A., Wani, M. S., & Mir, 
S. A. (2025). Towards sustainable mountain area 
development: unlocking the resource potential of  
borderland Himalayan watersheds. Environment, 
Development and Sustainability, 1-28.

Klose, M., Maurischat, P., & Damm, B. (2016). Landslide 
impacts in Germany: A historical and socioeconomic 
perspective. Landslides, 13(1), 183-199.

Li, X., Han, K., Li, J., & Li, C. (2025). A sustainable 
solution for high-standard farmland Construction—



Pa
ge

 
12

8

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 119-128, 2025

NGO–BP model for cost indicator prediction in 
fertility enhancement projects. Sustainability, 17(14), 
6250.

Li, Y., & Xiao, X. (2025). Deep Learning-Based Fusion of  
Optical, Radar, and LiDAR Data for Advancing Land 
Monitoring. Sensors, 25(16), 4991.

Liu, J., Wang, J., Peng, J., Hong, Y., & Shao, T. (2025). 
Multi-robot cooperative inspection planning of  
substation based on genetic algorithm and deep 
reinforcement learning. IEEE Access.

Melsse, D. W., Tegegne, M. A., Mekonnen, Y. A., & Bihon, 
Y. T. (2025). Morphometric analysis for understanding 
river basin hydrology: a case of  gelda watershed, Tana 
Sub-Basin, Ethiopia. Applied Water Science, 15(7), 171.

Niyogakiza, A., & Liu, Q. (2025). GIS-Driven Multi-
Criteria Assessment of  Rural Settlement Patterns and 
Attributes in Rwanda’s Western Highlands (Central 
Africa). Sustainability, 17(14), 6406.

Otoma, B. K., & Ayothiraman, R. (2025). State-of-the-Art 
Review on the Use of  Micropiles in Slope/Landslide 
Protection Measures. Indian Geotechnical Journal, 1-20.

Pelden, S., Banihashemi, S., Mohandes, S. R., 
Arashpour, M., & Kalantari, M. (2025). Enhancing 
infrastructure planning and design through BIM-GIS 
integration. Structure and Infrastructure Engineering, 1-20.

Pelden, S., Banihashemi, S., Mohandes, S. R., 
Arashpour, M., & Kalantari, M. (2025). Enhancing 
infrastructure planning and design through BIM-GIS 
integration. Structure and Infrastructure Engineering, 1-20.

Ramyar, A., Soltani, A., Ramyar, M., & Najafi Kashkooli, 
H. (2025). Urban land use allocation with hybrid 
linear programming–multi-objective ant colony 
algorithm. Earth Science Informatics, 18(2), 415.

Rawat, P. K., Belho, K., & Rawat, M. S. (2025). Geo-
environmental GIS modeling to predict flood hazard in 
heavy rainfall eastern Himalaya region: a precautionary 
measure towards disaster risk reduction. Environmental 
Monitoring and Assessment, 197(2), 220.

Sarigul, F. H., & Gunaydin, H. M. (2025). Integrated BIM, 
GIS and interoperable digital technologies in lifecycle 
management of  building construction projects: 
systematic literature review. Smart and Sustainable 
Built Environment.

Schnebele, E., Tanyu, B. F., Cervone, G., & Waters, 
N. J. E. T. R. R. (2015). Review of  remote sensing 
methodologies for pavement management and 

assessment. European Transport Research Review, 7(2), 7.
Sousa, M. L., & Tsionis, G. (2025). National seismic risk 

assessment: an overview and practical guide. Natural 
Hazards, 1-34.

Spanidis, P. M., Christakopoulou, J., Siontorou, C., 
Pavloudakis, F., Servou, A., & Roumpos, C. (2025). 
Exploring Stakeholder Preferences in Evaluation of  
Resilience Strategies for Post-mining Transformation 
Projects. Mining, Metallurgy & Exploration, 1-28.

Tumpa, R. J., & Naeni, L. (2025). Improving decision-
making and stakeholder engagement at project 
governance using digital technology for sustainable 
infrastructure projects. Smart and Sustainable Built 
Environment, 14(4), 1292-1329.

Tumpa, R. J., & Naeni, L. (2025). Improving decision-
making and stakeholder engagement at project 
governance using digital technology for sustainable 
infrastructure projects. Smart and Sustainable Built 
Environment, 14(4), 1292-1329.

Van den Hurk, M., Mastenbroek, E., & Meijerink, 
S. (2014). Water safety and spatial development: 
An institutional comparison between the United 
Kingdom and the Netherlands. Land Use Policy, 36, 
416-426.

Xiaoyu, H., Cuiying, Y., & Yiying, H. (2025). Building 
Sustainable Ecological Barriers Through Ecosystem Synergies 
and Socio-Ecological Integration. Land Degradation & 
Development.

Yan, H., Khan, A., Jamil, A., Abdeldjalil, B., Saidani, 
T., & Yacer, R. N. (2025). Deep Learning-Based 
Spatial Prediction of  Landslide Risk in Coastal areas 
Using GIS and Multi-Criteria Decision Making: A 
DeepLabV3+ Approach. IEEE Journal of  Selected 
Topics in Applied Earth Observations and Remote Sensing.

Yono, A., Mokua, R. A., & Dube, T. (2025). Remote 
sensing of  land cover change dynamics in 
mountainous catchments and semi-arid environments: 
a review. Geocarto International, 40(1), 2476602.

Zhang, F., Lv, H., & Kuai, C. (2025). Integrating user 
preferences and demand uncertainty in electric micro-
mobility battery-swapping station planning: A data-
driven three-stage model. Applied Energy, 389, 125713.

Şimşek, U. Y., Kuş, B., & Özgen, B. (2025). Evolutionary 
Algorithms: Creative Applications in the Early Design 
Phase. Technology| Architecture+ Design, 9(1), 98-115.


