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            Geoplanning: Journal of Geomatics and Planning, Vol. 12, No. 1, 2025, 15 - 30 

 

Original Research 

Coastal Metropolitan Dynamics in Poland's Tri-

City and Indonesia's Semarang: NTL, BLFEI, 

and OBIA in Google Earth Engine 

Abdurrahman Zaki1*, Joanna Jaskuła1 

1. Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, 

Poland 

DOI: 10.14710/geoplanning.12.1.15-30 

Abstract 

The increasing global urbanization, particularly in coastal regions, coupled with the risks of climate change and land 

subsidence, underscores the need to monitor coastal urban development for sustainability. This study focused on the coastal 

metropolitan regions of Poland's Tri-City and Indonesia's Semarang, employing GIS, remote sensing (RS), and cloud 

computing. By integrating nighttime light (NTL) and the Built-Up Land Features Extraction Index (BLFEI) through 

Google Earth Engine (GEE) and Object-Based Image Analysis (OBIA), the study aimed to gain insights into urban 

development trends. The methodology encompassed image collection, analysis, and classification over three decades (1992, 

2007, 2022). Despite efforts to enhance accuracy through built-up masking in subsequent years, the methodology achieved 

an overall accuracy of 95% for the 2022 maps, while maps in 1992 and 2007 fell short (overall accuracy ranging from 0.81 

to 0.90) in comparison. The analysis revealed a gradual expansion of built-up areas in both regions, with Gdynia and Gdańsk 

emerging as primary drivers in the Tri-City metropolitan region and Semarang as the primary driver in the Semarang 

metropolitan region. Notably, the Semarang metropolitan region exhibited an increase in waterbody areas, attributed to 

coastal flooding and land subsidence challenges. 

Copyright © 2025  by Authors,  

Published by Universitas Diponegoro Publishing Group.  

This open access article is distributed under a  

Creative Commons Attribution 4.0 International license 

 

1. Introduction  

The world's population is projected to reach between 9.4 to 10.1 billion by 2050 and between 9.4 to 12.7 

billion by 2100, according to the United Nations (2019a). By 2050, more than 68% of the global population is 

expected to reside in cities due to urbanization (United Nations, 2019b). In many developing countries, 

urbanization often occurs without proper planning (Gumel et al., 2020; Sun et al., 2020), resulting in 

unsustainable urban development (Clement & Pino, 2023; Das et al., 2021; Esther, 2022). Ultimately, the impacts 

of urbanization in developing countries could be more severe than those in developed countries (Ezadin & Faraj, 

2022).  

Factors contributing to urban sprawl in developing countries include increasing population, 

industrialization (Hasnine & Rukhsana, 2020), the service industry, and real estate development (Zhang & Pan, 

2021), as well as peri-urban and infrastructure development (Ahmed et al., 2021). To address sustainable urban 

development in the future, monitoring the temporal and spatial patterns of urban areas is crucial (Dadashpoor 

et al., 2019b; Sumari et al., 2019). However, despite the challenges, cities located along coastal areas are still 

highly preferred residential areas for urban dwellers (Sanders & Oliveira, 2020; Siegel, 2020; Wang et al., 2021). 

e-ISSN: 2355-6544 
 
Received:  09 September 2024;  
Revised:  08 May 2025;  
Accepted:  09 May 2025;  
Available Online: 14 May 2025; 
Published:  26 May 2025.  
 
Keywords:  
Urbanization, Coastal Metropolitan, 
Data Fusion, Obia 
 
*Corresponding author(s)  
email: abdurrahman.zaki20@pwk.undip.ac.id  

 
 

 

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The future sustainability of coastal cities is threatened by the increasing risks of climate change (Sanders 

& Oliveira, 2020; Wojtowicz-Jankowska & Kalfouni, 2022), particularly in those located in low-income countries 

(Day et al. 2021). These coastal cities are at risk of land subsidence (Cian et al., 2019; Hu et al., 2019; Wdowinski 

et al., 2020), sea level rise (Qu et al., 2019; Taherkhani et al., 2020; Valente & Veloso-Gomes, 2020), rising 

temperatures (Hu, 2021; Qi et al., 2022), and pollution (Choi et al., 2020; Su et al., 2020). To address these issues, 

adaptation measures to combat climate change, such as adaptive coastal planning (Valente & Veloso-Gomes, 

2020; Buchori et al., 2022) and community-based adaptation (Berman et al., 2020), are needed to prepare for the 

uncertainties facing coastal cities in the future. In this regard, providing data on changing landscape trends in 

coastal areas can serve as a monitoring measure and input for coastal adaptation strategies (Hu et al., 2021; Ragia 

& Krassakis, 2019; Vitousek et al., 2023). 

The monitoring of temporal and spatial changes in the Earth's landscape across vast areas is commonly 

carried out using a geographic information system (GIS) and remote sensing (Buchori et al., 2015; Fahad et al., 

2020; Liu & Yang, 2015; Woodcock et al., 2020; Zhang, 2020). Previous studies have demonstrated that GIS and 

remote sensing can be effectively used to analyze historical land use and land cover (LULC) maps (Adnani et al., 

2019; Viana et al., 2019), predict future LULC maps using the Cellular Automate (CA) algorithm (Hishe et al. 

2020; Mathanraj et al., 2021; Muhammad et al., 2022), and even apply object-based image analysis (OBIA) for 

improved accuracy in classifying LULC maps (How et al., 2020; Pangastuti & Wijayanto, 2021; Yadav et al., 

2022; Zaki et al., 2022). Recently, cloud computing, particularly Google Earth Engine, has significantly 

accelerated image analysis, enabling scholars to analyze large areas at the national or global scale without 

consuming local computer memory (Luo et al., 2021; Yadav et al., 2022; Zaki et al., 2022; Zaki et al., 2023;  Zhang 

& Li, 2022). 

When classifying Land Use and Land Cover (LULC) maps, a persistent issue is the occurrence of the "salt 

and pepper effect," which refers to the scattering of misclassified pixels. This phenomenon is particularly 

noticeable when using pixel-based image analysis, a conventional method for image classification. To mitigate 

the salt and pepper effect, a more recent classification method known as Object-Based Image Analysis (OBIA) 

was developed. OBIA functions by segmenting pixels before the classification process, allowing for the merging 

of scattered pixels with their homogeneous surrounding environment. In addition to advancing classification 

methods, efforts to enhance the accuracy of LULC maps have been made through data fusion, which involves the 

combination of satellite data from various sources to achieve improved results. For instance, prior studies have 

attempted to fuse Landsat image collections with nighttime light datasets using Google Earth Engine (Goldblatt 

et al., 2018; Liu et al., 2019). Our primary hypothesis centers on the idea that by integrating OBIA with data 

fusion, incorporating not only nighttime light data but also a remote sensing index, a scholar can enhance the 

accuracy of LULC mapping on a broader scale within the framework of Google Earth Engine. A secondary 

hypothesis aims to demonstrate that urban sprawl is more pronounced in the Semarang metropolitan region 

(Indonesia) compared to the Tri-City metropolitan region (Poland).  

Therefore, the objective of this study is to improve the classification of LULC maps by fusing Landsat 

image collections, integrating nighttime light (NTL) data, and Built-Up Land Features Extraction Index 

(BLFEI). This approach enables the analysis of three decades of urban development in medium-sized coastal 

metropolitan regions in Indonesia and Poland. However, it is essential to acknowledge certain limitations in this 

study: 1) social and political factors are not considered in this research, and 2) the categorization of LULC classes 

is limited to built-up areas, non-built-up areas, and waterbodies. Ultimately, this study aspires to contribute to 

the body of knowledge in GIS and remote sensing methodology for monitoring urban sprawl, thereby promoting 

sustainable coastal urban development. 

2. Data and Methods 

2.1. Study Area 

The study area of this research includes the coastal areas of the Tri-City metropolitan region in Poland 

(Figure 1) and the Semarang metropolitan region in Indonesia (Figure 2). These study areas were selected 

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because they are medium-sized metropolitan cities in their respective countries. In general, the study area was 

defined as a 20-kilometer radius from the coastal areas of the Tri-City and Semarang metropolitan regions. This 

definition was chosen considering that administrative boundaries do not limit urban growth, and a broader study 

area is necessary to encompass the urban core and suburban areas in each region. A radius of 20 kilometers was 

deemed appropriate to cover these aspects in both metropolitan regions. Additionally, the study area includes 

water bodies to account for landscape changes such as land reclamation, harbor construction, and coastal erosion. 

  

Figure 1. The study area of the Tri-City 

metropolitan region in Poland 

Figure 2. The Study Area of the Semarang 

Metropolitan Region in Indonesia 

2.2. Data 

The aim of this research is to monitor urban development over an extended period, and Landsat image 

collections were chosen as the dataset due to their continuous operation since the 1970s, despite differences 

between each Landsat mission. Although Sentinel-2A satellite image collections were available, they were not 

utilized in this research as their operational period only started from June 23, 2015. Instead, Landsat 5 

(operational from March 1, 1984, to June 5, 2013; LANDSAT/LT05/C02/T1_L2) and Landsat 8 (operational 

from February 11, 2013; LANDSAT/LC08/C02/T1_L2) provided by the U.S. Geological Survey (USGS) were 

used in this study. By utilizing these two Landsat datasets, which consist of atmospherically corrected surface 

reflectance, the research generated maps for three time periods: 1992, 2007, and 2022. The selection of these 

years was based on available data, taking into account cloud covers and the availability of nighttime light data. 

The next dataset was the nighttime light data, which indicates urbanization. It included the datasets 

NOAA/DMSP-OLS/NIGHTTIME_LIGHTS with a spatial resolution of 927.67 meters, covering the period 

from 1992 to 2014, and NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG with a spatial resolution of 463.83 

meters, spanning from 2014 to 2023 in Google Earth Engine. This dataset was provided by the Earth 

Observation Group (Payne Institute for Public Policy, Colorado School of Mines). 

The final dataset comprised a set of randomly selected sample points for both built-up and non-built-up 

areas within each study area for each observation year (1992, 2007, and 2022), as presented in Table 1 and 

spatially visualized in Figure 3. The process of selecting sample points involved gradually adding samples until 

the resulting land cover map accurately represented the actual Earth conditions, as observed from natural color 

Landsat imagery. These sample points were then divided into two sets: 70% for training purposes and 30% for 

testing (Abdi 2020; Chenli Liu et al. 2020). This division was achieved using the randomColumn() and filter() 

functions. The training points were utilized for image classification, while the testing points were used to assess 

the accuracy of the image classification results. 

 

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Table 1. Numbers of Sample Points for Built-Up (BU) and Non-Built-Up (NBU) Areas in each region 

Year 
Tri-City Semarang 

BU NBU BU NBU 
2022 179 582 368 550 

2007 240 268 218 218 
1992 192 157 283 127 

To be noted, the number of training samples from 2022 to 1992 decreased due to smaller areas to be 

classified. This reduction was made taking into consideration the possibility that misclassification could result 

in a built-up area in a previous year that does not exist in the following year. To anticipate this, the area was 

limited that would be classified using the boundary of classified built-up areas in the following year of 

observation. However, the downside is that the number of training samples was up to hundreds, making it not 

time-efficient for purposes requiring fast processing. 

 

Figure 3. Spatial Distribution of Training Samples in Each Study Area  
(Left: Semarang Metropolitan Region; Right: Tri-City Metropolitan Region) 

2.3. Methods 

This section describes the methodology used in this study, which generally includes initial image 

processing (filtering and masking), data fusion, and object-based image classification (OBIA). The methodology 

was predominantly implemented using Google Earth Engine (https://code.earthengine.google.com/), and 

QGIS Desktop 3.32.1 (https://qgis.org/) for the map layouting. The workflow of this methodology is depicted 

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in Figure 4. The final output of this process comprises land cover maps delineating built-up areas, non-built-up 

areas, and water bodies within the two study regions for the years 1992, 2007, and 2022. 

 

Figure 4. Methodological Workflow Implemented in Google Earth Engine  

2.3.1. Initial Image Processing 

The processes in the initial phase of the methodology involved filtering the Landsat image collections, 

applying a scale factor for both Landsat 5 and Landsat 8, performing cloud masking, and subsequently applying 

water masking using MNDWI (Modified Normalized Difference Water Index). Initially, the filter date selection 

for both locations (Tri-City and Semarang) took into account when both locations are in the summer/dry season. 

For example, it was referenced in a previous study that indicated the dry season in the Java province (where the 

Semarang metropolitan region is located) spans from June to September, with the peak of rainfall in December 

and January (Berliana et al., 2021). On the other hand, another study mentioned that Torun (a city located 185 

kilometers south of the Tri-City) experiences spring from April to October (Kejna & Pospieszyńska, 2023). In 

this case, the analyzed period was utilized from April 1st to October 31st for each observation year in both 

locations to filter the Landsat images. Secondly, before using the LANDSAT/LT05/C02/T1_L2 and 

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LANDSAT/LC08/C02/T1_L2 for calculations, the scale factor must be applied using Equation 1 and Equation 

2:  

𝑜𝑝𝑡𝑖𝑐𝑎𝑙 𝑏𝑎𝑛𝑑 =  𝑏𝑎𝑛𝑑 𝑆𝑅_𝐵 × 0.0000275 + (−0.2)………..(Eq.1) 
 

𝑡ℎ𝑒𝑟𝑚𝑎𝑙 𝑏𝑎𝑛𝑑 =  𝑏𝑎𝑛𝑑 𝑆𝑇_𝐵 × 0.00341802 +  149)………..(Eq.2)  

where “band SR_B” refers to all optical bands (B1, B2, B3, B4, B5, B7 in Landsat 5; B1, B2, B3, B4, B5, B6, B7 in 

Landsat 8), and “band ST_B” refers to" thermal bands (B6 in Landsat 5 and B10 in Landsat 8). 

Thirdly, cloud masking was applied to both dataset using the bitmask for the QA_PIXEL band to 

eliminate clouds, cloud shadows, and snow. The specific bitmask used in this step is detailed in Table 2. This 

process resulted in image collections with clear terrain. Furthermore, the median value of each pixel was selected, 

and the image was clipped to the area of interest. 

Table 2. Bitmask for QA_PIXEL in Landsat 5 and Landsat 8 

Bit Landsat 5  Landsat 8 
1 Dilated Cloud Dilated Cloud 
2 Unused Cirrus 
3 Cloud Cloud 
4 Cloud Shadow Cloud Shadow 
5 Snow Snow 

 

In the final stage of the initial processing, water masking was executed using MNDWI (see Equation 3), 

an index proposed by Xu (2006) to identify water bodies, where MNDWI greater than zero indicates water. In 

this case, MNDWI less than or equal to zero was used to filter the image collections that were previously cloud-

masked. The calculation of MNDWI was performed using the Equation 3, where Green and MIR represent band 

2 and band 5 in Landsat 5, and band 3 and band 6 in Landsat 8, respectively. 

𝑀𝑁𝐷𝑊𝐼 =
𝐺𝑟𝑒𝑒𝑛−𝑀𝐼𝑅

𝐺𝑟𝑒𝑒𝑛+𝑀𝐼𝑅
)………...(Eq.3) 

2.3.2. Data Fusion 

After obtaining the cloud- and water-masked Landsat image collection, the next step was to integrate this 

collection with nighttime light data and BLFEI. For the nighttime light data, the median values of pixels within 

the period from January 1st to December 31st were selected and cropped using the boundary of the water-masked 

image of the study area. However, the datasets used in this step had different spatial resolutions (927.67 meters 

for NOAA/DMSP-OLS/NIGHTTIME_LIGHTS and 463.83 meters for 

NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG). 

Then, BLFEI proposed by Bouhennache et al. (2019) which resulted in higher accuracy than some other 

built-up indexes was calculated. The calculation of BLFEI was performed using the Equation 4: 

𝐵𝐿𝐹𝐸𝐼 =
(

𝐺𝑟𝑒𝑒𝑛+𝑅𝑒𝑑+𝑆𝑊𝐼𝑅2

3
−𝑆𝑊𝐼𝑅1)

(
𝐺𝑟𝑒𝑒𝑛+𝑅𝑒𝑑+𝑆𝑊𝐼𝑅2

3
+𝑆𝑊𝐼𝑅1)

 )………...(Eq.4) 

where the bands used in the formula for Landsat 5 and Landsat 8 were shown in Table 3. Finally, Landsat images, 

nighttime light images, and BLFEI were combined using the "addBands" function in Google Earth Engine. 

 

 

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Table 3. Bands in Landsat 5 and Landsat 8 used for calculating BLFEI 

 Landsat TM Landsat OLI 

Band 
Wavelength 

(micrometers) 
Band 

Wavelength 
(micrometers) 

Green 2 0.52-0.60 3 0.53-0.59 
Red 3 0.63-0.69 4 0.64-0.67 

SWIR 1 5 1.55-1.75 6 1.57-1.65 
SWIR 2 7 2.08-2.35 7 2.11-2.29 

Source: https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites 

2.3.3. Object-Based Image Analysis 

The final step was to conduct object-based image analysis (OBIA) to monitor urban development patterns. 

Initially, image segmentation was performed to cluster similar pixels from the result of data fusion into polygons, 

which is an essential step in OBIA. In this case, it was used a size of 15 and "hex" as the grid type when 

performing super pixel clustering based on Simple Non-Iterative Clustering (SNIC) in Google Earth Engine. 

The result of image segmentation is illustrated in Figure 5. 

 

Figure 5. Segmentation result (left) and Google satellite imagery (right) in an area located in the northern 
Tri-City, Poland 

And then, some polygons were selected as training regions based on the previously prepared random 

training points. Using these training regions, classification using the random forest algorithm was performed to 

simulate LULC maps. In this case, the number of trees set for the random forest algorithm was 50, referring to 

Junaid et al., (2023). Since the water bodies were excluded from the initial stage, blank data in the study area was 

set as water bodies and combined with the resulted LULC from the OBIA. Finally, all results of calculations in 

Google Earth Engine were exported to Google Drive to be further downloaded, visualized, and analyzed in 

QGIS Desktop 3.32.1. 

3. Result and Discussion 

This section comprises a description of the results from the analysis between urban development in the a 

medium-sized coastal metropolitan region in Poland and Indonesia. The results include interpretations of the 

data used during the data fusion process, such as Landsat images, the NTL, and the BLFEI. Additionally, there 

are discussions about the LULC maps resulting from the Object-Based Image Analysis (OBIA) method in Google 

Earth Engine, representing urban development in both study areas. This section is followed by a discussion of 

these results in relation to other relevant studies to understand how they correlate with the existing body of 

knowledge. 

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3.1. Results 

The coastal metropolitan regions exhibited gradual expansion, with green open spaces converted into 

built-up areas and the construction of toll roads further stimulating rapid urban growth. Overall, the 

development of built-up areas in both regions aligned with the primary transportation networks connecting 

other cities. This expansion is illustrated in Figure 6 and Figure 7, which depict each study area using BLFEI 

and NTL. The data shows a varying range across the years, with the variance in NTL attributed to advancements 

in satellite technologies that have resulted in higher spatial resolution, particularly noticeable when comparing 

the 2007 and 2022 data. BLFEI effectively highlights built-up areas through its reddish coloration on the map. 

Additionally, these maps reveal an expanding coastline in both regions due to harbor development and land 

reclamation. In the Semarang metropolitan region, however, land subsidence and coastal flooding are 

particularly pronounced in the northeastern coastal area. 

 

Figure 6. BLFEI and Nighttime Light Data in the Tri-City Metropolitan Region 

 

Figure 7. BLFEI and Nighttime Light Data in the Semarang Metropolitan Region 

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Figure 8. Identification of Built-Up Expansion in the Tri-City Metropolitan Region 

 

Figure 9. Land Cover Map vs. Google Satellite Image in Tri-City’s Cities 

Figure 8 and Figure 10, resulting from the application of OBIA to the combination of all Landsat bands, 

NTL, and BLFEI, illustrate the evolution of built-up areas in both study regions. In the Tri-City metropolitan 

area, Gdynia and Gdańsk act as primary drivers of urban development, with Sopot serving as a connector 

between these cities and forming a cohesive metropolitan zone. The presence of harbors in both Gdynia and 

Gdańsk, along with an international airport in Gdańsk, significantly fuels urban expansion. The dominant 

housing type in Poland, characterized by numerous apartments and multi-story buildings, contributes to more 

compact urban development. In contrast, in the Semarang metropolitan region, Semarang city is the main driver 

of growth, linking neighboring cities such as Kendal, Demak, and Ungaran. The harbor and domestic airport in 

Semarang also play crucial roles in spurring urban expansion. However, the prevalence of single-family houses 

in Indonesia, which is more prone to causing urban sprawl, contrasts with the denser housing patterns seen in 

Poland. Additionally, Figure 9 and Figure 11 provide a detailed comparison between the land cover map and 

Google satellite images for each city in the study areas. 

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Figure 10. Identification of Built-Up Expansion in the Semarang Metropolitan Region 

 

Figure 11. Land Cover Map vs. Google Satellite Image in Semarang 

The LULC maps resulting from this study exhibit varied overall accuracy (OA) statistics, following a 

similar pattern for both study areas: the OA of the subsequent year is higher than that of the preceding year. 

The methodology achieved an overall accuracy of 95% for the 2022 maps. The OA for the Tri-City metropolitan 

region in 1992 and 2007 stands at 81% and 87%, respectively, as shown in Table 4. Similarly, for the Semarang 

metropolitan region, the OA in 1992 and 2007 is 87% and 90%, respectively, presented in Table 5. Despite the 

expectation to increase the OA of an LULC map by masking it based on the built-up areas in the following year, 

the accuracy of the LULC maps in 1992 and 2007 still falls short compared to the OA in 2022. 

 

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Table 4. Overall Accuracy of Land Cover Maps in the Tri-City Metropolitan Region 

 1992 2007 2022 

BU NBU BU NBU BU NBU 

BU 42 5 60 9 43 7 

NBU 8 15 5 30 5 173 

OA 0.81 0.87 0.95 

Table 5. Overall Accuracy of Land Cover Maps in the Semarang Metropolitan Region 

 1992 2007 2022 

BU NBU BU NBU BU NBU 

BU 71 2 62 1 95 5 

NBU 9 5 8 18 7 149 

OA 0.87 0.90 0.95 

In both study areas, there is a general increase in built-up areas and a decrease in non-built-up areas, as 

illustrated in Table 6. In the Tri-City region, built-up areas increased by 32.75 square kilometers or 34.45% 

during 1992-2007 and by 68.88 square kilometers or 53.89% during 2007-2022. Conversely, in Semarang, built-

up areas expanded by 142.80 square kilometers or 58.60% in the 1992-2007 period and by 99.31 square 

kilometers or 25.70% in the 2007-2022 period. This indicates a faster rate of built-up development in the Tri-

City region during the 2007-2022 period, while in the Semarang metropolitan region, the acceleration was 

observed in the 1992-2007 period. However, in the Semarang metropolitan region, there is an increase in the 

area of waterbodies due to the well-known issues of coastal flooding and land subsidence. 

Table 6. Changes in Area for Each Land Cover Class in Both Study Areas 

Year 
Tri-City Semarang 

Built up Non-Built up Waterbodies Built up Non-Built up Waterbodies 

1992 (km2) 95.06 1617.52 1303.09 243.69 2172.79 2342.57 

2007 (km2) 127.81 1588.27 1298.84 386.49 2003.31 2369.15 

2022 (km2) 196.69 1520.70 1297.30 485.80 1848.02 2425.29 

1992-2007 (Δ) 32.75 -29.25 -4.25 142.80 -169.48 26.58 

2007-2022 (Δ) 68.88 -67.57 -1.54 99.31 -155.29 56.14 

1992-2007 (%) 34.45 -1.81 -0.33 58.60 -7.80 1.13 

2007-2022 (%) 53.89 -4.25 -0.12 25.70 -7.75 2.37 

3.2. Discussion 

Similar data fusion techniques were employed by Goldblatt et al. (2018) who combined nighttime light 

data with a Landsat 8 image collection. Using pixel-based image analysis, they achieved accuracies ranging from 

80.5% to 87.2% for a nationwide built-up classification. On the other hand, while Liu et al. (2019) were able to 

produce built-up maps with an overall accuracy of at least 94.7%. Their approach relied on VIIRS nighttime light 

data, which offers higher spatial resolution and has been available since 2014. This is in contrast to the DMSP-

OLS nighttime light data, which was available from 1992 to 2014 and was used for generating the maps in 1992 

and 2007 in our research.  

Differing from the aforementioned studies, this research employed an Object-Based Image Analysis 

(OBIA) approach—a method designed to mitigate the salt-and-pepper effect when classifying high-resolution 

images. It involved the classification of fused images from Landsat, nighttime light data, and BLFEI, resulting 

in maps with accuracies ranging from 0.81 to 0.95. However, it's important to note that the fusion of these three 

images did not achieve the expected high accuracy for all years. Notably, it was successful only for the year 2022, 

despite our efforts to enhance accuracy by incorporating built-up masking from the 2022 map when classifying 

land covers in 2007 and built-up masking from the 2007 map when classifying land covers in 1992. Nevertheless, 

the utilization of the OBIA method in Google Earth Engine appeared promising, as it offers a simpler workflow 

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compared to implementing it in the Orfeo ToolBox (OTB) of the QGIS software, as demonstrated in a previous 

study (Zaki et al., 2022). 

In addition to the accuracy assessment, both the Tri-City and Semarang metropolitan regions have 

experienced rapid urban expansion, signifying an increase in urban density in the city centers and a sprawling 

process in their suburban areas. This comparison indicates that the distance between urban centers in a 

metropolitan region influences the dynamics of urban development. On one hand, the Tri-City metropolitan 

region comprises Gdańsk and Gdynia, situated only about 22 kilometers apart as two major urban centers in 

1992, with Sopot located in between, playing a significant role in driving urban growth. On the other hand, the 

Semarang metropolitan region primarily relies on Semarang as the sole main driver of urban development within 

its area. Kendal to the west and Demak to the east serve as neighboring cities, with both cities being 

approximately 30 kilometers away from Semarang. However, it's worth noting that, in general, urban 

development in both metropolitan regions follows transportation networks that connect urban centers to 

surrounding cities. In the case of Semarang, there is a phenomenon known as "desa kota" indicating a blurred 

distinction between suburban and urban areas (McGee, 2022). This phenomenon has not been observed in the 

Tri-City metropolitan region. 

It is worth noting that coastal urban areas are particularly susceptible to flood risks, especially with the 

increasing threat of climate change, exacerbated by land subsidence. A previous study analyzed land subsidence 

occurring in Gdańsk and Gdynia in 2018 and 2020 (Rajaoalison & Knez, 2021). However, based on our 

observations, the severity of land subsidence in these areas does not appear to be as pronounced as what has been 

observed on the coast of the Semarang metropolitan region. In the Semarang region, some residents' houses are 

already experiencing flooding, leading to forced migration or the necessity to raise the height of roads and homes 

over time (Buchori et al., 2018; Buchori et al., 2021). This trend is evident in the resulting maps from our study, 

showing that in the eastern part of the Semarang metropolitan region's coast, water bodies have been expanding 

since 1992 to 2022. In contrast, such a phenomenon has not been observed along the coast of the Tri-City 

metropolitan region. 

The LULC classes employed in this study are limited to three categories: built-up, non-built-up, and 

waterbodies. Each yearly image resulted from the median value of Landsat images taken between April and 

October. However, due to variations in the agricultural cycle across years, there are instances when extensive 

harvested agricultural lands are observed. The fluctuating vegetation patterns throughout the years prompted 

our focus on a limited range of land cover types, predominantly built-up and non-built-up areas. In future 

research, conducting a more detailed LULC classification involving categories like bare lands, forest lands, and 

agricultural lands would facilitate a more comprehensive analysis. 

Secondly, green spaces (non-built-up areas) in 2022 were assumed to be the same in 2007 and 1992, 

implying that there were no afforestation activities within the two study areas. This assumption was made 

because of observed misclassifications of built-up areas in 2007 and 1992. Therefore, they opted to mask out non-

built-up areas in 2022 when classifying land covers in 2007 and to mask out non-built-up areas in 2007 when 

classifying land covers in 1992. This technique is primarily applicable for classifying land cover changes in 

developing countries, where it is common for urban areas to continuously expand, and instances of building 

deconstruction being replaced by vegetation are relatively rare. 

Thirdly, the BLFEI and NTL datasets were not normalized before they were merged with the cloud- and 

water-masked Landsat image collection in the methodology. Consequently, the obtained results suggest that 

future scholars investigate whether normalization of these two variables could lead to higher accuracy in the 

final LULC maps. This suggestion is supported by some research demonstrating that normalization positively 

affected the classification performance using machine learning algorithms (Raju et al., 2020; Singh & Singh, 

2020). 

Lastly, this research was limited to using the random forest algorithm for LULC classification. However, 

gaining a deeper understanding of the principles and concepts underlying each machine learning algorithm 

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would be advantageous for selecting the most suitable algorithm or even testing each to determine the one with 

the highest accuracy. In the future, exploring the use of the Segment Anything Model (SAM), an artificial 

intelligence-based image segmentation system developed by Meta AI, could be considered for analyzing urban 

area maps (Giannakis et al., 2023; Ren et al., 2023; Wang et al., 2023; Zhang et al., 2023). Moreover, future 

research could perform the projection of future LULC maps; and incorporate 3D building footprint data as it 

reveals the vertical structure of buildings in urban areas, serving as an additional variable for data fusion. 

4. Conclusion 

This study attempted to enhance the accuracy of Land Use and Land Cover (LULC) mapping in the coastal 

areas of metropolitan regions in Indonesia and Poland over three decades (1992, 2007, and 2022). The approach 

involved data fusion between nighttime light and Landsat image data, along with Object-Based Image Analysis 

(OBIA), conducted efficiently using the Google Earth Engine cloud computing platform. Despite efforts to 

improve accuracy through built-up masking in subsequent years, the accuracy assessment of LULC maps 

revealed varied overall accuracy patterns in both study areas. The methodology achieved an overall accuracy of 

95% for the 2022 maps, while maps in 1992 and 2007 fell short (overall accuracy ranging from 0.81 to 0.90) in 

comparison. Notably, the Semarang metropolitan region exhibited an increase in waterbody areas, attributed to 

coastal flooding and land subsidence challenges, highlighting the complex dynamics between urbanization and 

environmental factors. 

The analysis uncovered a gradual expansion of built-up areas in both regions, indicating urban 

development stimulated by primary transportation networks to surrounding cities. In the Tri-City metropolitan 

area, Gdynia and Gdańsk emerged as primary drivers, with Sopot acting as a crucial connector, forming a 

cohesive metropolitan zone. The presence of a harbor in Gdynia played a significant role in influencing urban 

expansion. Conversely, in the Semarang metropolitan region, Semarang city took the lead, linking neighboring 

cities (Kendal, Demak, Ungaran), with the harbor in Semarang contributing significantly to surrounding urban 

development. Generally, as a metropolitan region in a developing country, Semarang, with its larger population, 

has experienced a larger area converted into built-up areas in the same period compared to the more developed 

Tri-City in Poland.  

In conclusion, the study demonstrated a unique approach using OBIA and data fusion, providing insights 

into urban development dynamics. While successful in 2022, the fusion of images faced challenges in other years. 

The expansion of water bodies in the Semarang metropolitan region emphasizes the urgency of addressing 

climate-related risks in coastal urban planning and the need for adaptive strategies. 

5. Acknowledgments 

The authors would like to thank the editors and reviewers for their valuable comments, which have 

contributed to improving the quality of the manuscript. 

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