Performance of different machine learning algorithms on satellite image classification in rural and urban setup

Rahman, Ashikur; Abdullah, Hasan Muhammad; Tanzir, Tousif; Hossain, Jakir; Khan, Bhoktear M.; Miah, Giashuddin; Islam, Imranul

doi:10.1016/j.rsase.2020.100410

Cited by 31 publications

(19 citation statements)

References 35 publications

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“…The support vector machine (SVM) approach has been widely used for LULC classification with multisource remote sensing data and has exhibited its capacity to obtain accurate results [31][32][33]. The SVM classifier with a radial basis function kernel, and a penalty hyperparameter of 100 was used here to classify the land cover into four broad types: built-up area, bare land, water bodies, and vegetation.…”

Section: Land Use/land Cover Classificationmentioning

confidence: 99%

“…It is necessary to comprehensively investigate the spatiotemporal changes of the urban thermal environment in the Karachi metropolitan region. The reliability on support vector machines for remote sensing image classification and the LULC change analysis has been demonstrated in various studies [31][32][33]. In this study, the LULC was classified using the support vector machine method, and the LST datasets were derived from Landsat images in Karachi.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Baqa

Fang

et al. 2022

Remote Sensing

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Understanding the spatiotemporal patterns of urban heat islands and the factors that influence this phenomenon can help to alleviate the heat stress exacerbated by urban warming and strengthen heat-related urban resilience, thereby contributing to the achievement of the United Nations Sustainable Development Goals. The association between surface urban heat island (SUHI) effects and land use/land cover features has been studied extensively, but the situation in tropical cities is not well-understood due to the lack of consistent data. This study aimed to explore land use/land cover (LULC) changes and their impact on the urban thermal environment in a tropical megacity—Karachi, Pakistan. Land cover maps were produced, and the land surface temperature (LST) was estimated using Landsat images from five different years over the period 2000–2020. The surface urban heat island intensity (SUHII) was then quantified based on the LST data. Statistical analyses, including geographically weighted regression (GWR) and correlation analyses, were performed in order to analyze the relationship between the land cover composition and LST. The results indicated that the built-up area of Karachi increased from 97.6 km² to 325.33 km² during the period 2000–2020. Among the different land cover types, the areas classified as built-up or bare land exhibited the highest LST, and a change from vegetation to bare land led to an increase in LST. The correlation analysis indicated that the correlation coefficients between the normalized difference built-up index (NDBI) and LST ranged from 0.14 to 0.18 between 2000 and 2020 and that NDBI plays a dominant role in influencing the LST. The GWR analysis revealed the spatial variation in the association between the land cover composition and the SUHII. Parks with large areas of medium- and high-density vegetation play a significant role in regulating the thermal environment, whereas the scattered vegetation patches in the urban core do not have a significant relationship with the LST. These findings can be used to inform adaptive land use planning that aims to mitigate the effects of the UHI and aid efforts to achieve sustainable urban growth.

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Section: Land Use/land Cover Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Baqa

Fang

et al. 2022

Remote Sensing

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“…The continuous and accurate analysis of LULC is an integral part of the sustainable development activities undertaken in any given area. Detailed LC maps are an important input for a variety of scientific studies involving climate change effects on streamflow and water budgets [1,2], geomorphology [3], groundwater management [4][5][6][7], social knowledge management of natural resources [8], and agricultural land monitoring [9][10][11]. LULC maps can help determine which types of lands are suitable for agriculture and which can be useful in watershed management in general [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Kolli et al (2020) [26] mapped land use changes around Kolleru Lake, India, using an RF classifier and obtained an overall accuracy of 95.9% and a kappa coefficient of 0.94. Rahman et al (2020) [10] analyzed the performance, via accuracy levels, of RF and SVM on the classification of urban and rural areas in Bangladesh. They achieved a maximum SVM accuracy of 96.9% for Bhola and 98.3% for Dhaka.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India

2021

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The growing human population accelerates alterations in land use and land cover (LULC) over time, putting tremendous strain on natural resources. Monitoring and assessing LULC change over large areas is critical in a variety of fields, including natural resource management and climate change research. LULC change has emerged as a critical concern for policymakers and environmentalists. As the need for the reliable estimation of LULC maps from remote sensing data grows, it is critical to comprehend how different machine learning classifiers perform. The primary goal of the present study was to classify LULC on the Google Earth Engine platform using three different machine learning algorithms—namely, support vector machine (SVM), random forest (RF), and classification and regression trees (CART)—and to compare their performance using accuracy assessments. The LULC of the study area was classified via supervised classification. For improved classification accuracy, NDVI (normalized difference vegetation index) and NDWI (normalized difference water index) indices were also derived and included. For the years 2016, 2018, and 2020, multitemporal Sentinel-2 and Landsat-8 data with spatial resolutions of 10 m and 30 m were used for the LULC classification. ‘Water bodies’, ‘forest’, ‘barren land’, ‘vegetation’, and ‘built-up’ were the major land use classes. The average overall accuracy of SVM, RF, and CART classifiers for Landsat-8 images was 90.88%, 94.85%, and 82.88%, respectively, and 93.8%, 95.8%, and 86.4% for Sentinel-2 images. These results indicate that RF classifiers outperform both SVM and CART classifiers in terms of accuracy.

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“…The findings were evaluated and compared to field data and geological maps from the study region. Rahman et al, (2020) assessed rural and urban extents using Random forest and Support Vector Machine (SVM) algorithms on Landsat-8 and Sentinel-2 images, with overall accuracy of 96.9%, 98.3%, and kappa values of 0.948 and 0.968, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Limestone mining area mapping and assessment at the Cement Industrial area using Spectral Index and Machine Learning Algorithms

C¹

2022

Preprint

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Limestone mining contributes significantly to the Gross Domestic Product (GDP) of any country but it comes with some adverse impacts on the environment. The objective of this study is to determine the spatial distribution area of limestone mines using remote sensing, spectral index, and machine learning algorithms and compare their area estimation with industrial data for the financial year 2019. The study area includes a limestone mining area, of approximately 2226.16 ha with an excavation mining area of 487.10 ha at the Yerraguntla cement industrial region, YSR Kadapa district, Andhra Pradesh, India. In this study, we used the normalized vegetation index (NDVI), iterative self organizing data analysis technique (ISODATA), K-Nearest Neighbors (KNN), and random forest (RF) algorithms to analyze multispectral Sentinel-2A satellite data in QGIS 3.18 software tool. The RF classifier estimated a limestone mine area of 379.57 ha with best user accuracy (UA) 97.25% and producer accuracy (PA) 99.18% with a kappa coefficient value of 0.957. The mine area estimated 417.47 ha with UA of 98.99% and PA of 99.10% and kappa value 0.947 of the KNN method, The NDVI method estimated 469.92 ha with UA of 93.63% and PA of 92.04% and kappa value 0.685. This study confirmed that RF classifier well performed in classification with overall accuracy (OA) of 95.79% than KNN (OA of 94.78%), and NDVI (OA of 79.84%) classifiers, and ISODATA is poor in classification with OA of 64.16%. This study supports environmentally sustainable decisions, eco-friendly mine planning and monitoring for limestone mine owners and environmental engineers.

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Performance of different machine learning algorithms on satellite image classification in rural and urban setup

Cited by 31 publications

References 35 publications

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India

Limestone mining area mapping and assessment at the Cement Industrial area using Spectral Index and Machine Learning Algorithms

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