Land use and land cover classification for change detection studies using convolutional neural network
Efficient land use land cover (LULC) classification is crucial for environmental monitoring, urban planning, and resource management. This study investigates LULC changes in Nanjangud taluk, Mysuru district, Karnataka, India, using remote sensing (RS) and geographic information systems (GIS). This p...
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Elsevier
2025-02-01
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| Series: | Applied Computing and Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197425000096 |
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| author | V. Pushpalatha P.B. Mallikarjuna H.N. Mahendra S. Rama Subramoniam S. Mallikarjunaswamy |
| author_facet | V. Pushpalatha P.B. Mallikarjuna H.N. Mahendra S. Rama Subramoniam S. Mallikarjunaswamy |
| author_sort | V. Pushpalatha |
| collection | DOAJ |
| description | Efficient land use land cover (LULC) classification is crucial for environmental monitoring, urban planning, and resource management. This study investigates LULC changes in Nanjangud taluk, Mysuru district, Karnataka, India, using remote sensing (RS) and geographic information systems (GIS). This paper mainly focuses on the classification and change detection analysis of LULC in 2010 and 2020 using linear imaging self-scanning sensor-III (LISS-III) remote sensing images. Traditional methods for LULC classification involve manual interpretation of satellite images, which provides lower accuracy. Therefore, this paper proposed the Convolutional Neural Network (CNN)-based deep learning method for LULC classification. The main objective of the research work is to perform an efficient LULC classification for the change detection study of the Nanjagud taluk using the classified maps of the years 2010 and 2020. The experimental results indicate that the proposed classification method is outperformed, with an overall accuracy of 94.08% for the 2010 data and 95.30% for the 2020 data. Further, change detection analysis has been carried out using classified maps and the results show that built-up areas increased by 8.34 sq. km (0.83%), agricultural land expanded by 2.21 sq. km (0.23%), and water bodies grew by 3.31 sq. km (0.35%). Conversely, forest cover declined by 1.49 sq. km (0.15%), and other land uses reduced by 11.93 sq. km (1.22%) over the decade. |
| format | Article |
| id | doaj-art-c1f0d831d6a44d609533f198d5e8103a |
| institution | Kabale University |
| issn | 2590-1974 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj-art-c1f0d831d6a44d609533f198d5e8103a2025-02-03T04:16:55ZengElsevierApplied Computing and Geosciences2590-19742025-02-0125100227Land use and land cover classification for change detection studies using convolutional neural networkV. Pushpalatha0P.B. Mallikarjuna1H.N. Mahendra2S. Rama Subramoniam3S. Mallikarjunaswamy4Department of Information Science and Engineering, JSS Academy of Technical Education (Affiliated to Visvesvaraya Technological University, Belagavi), Bengaluru, 560060, Karnataka, IndiaDepartment of Computer Science and Engineering, JSS Academy of Technical Education (Affiliated to Visvesvaraya Technological University, Belagavi), Bengaluru, 560060, Karnataka, India; Corresponding author.Department of Electronics and Communication Engineering, JSS Academy of Technical Education (Affiliated to Visvesvaraya Technological University, Belagavi,), Bengaluru, 560060, Karnataka, India; Corresponding author.Regional Remote Sensing Centre-South, NRSC, Indian Space Research Organization (ISRO), Bengaluru, 560037, Karnataka, IndiaDepartment of Electronics and Communication Engineering, JSS Academy of Technical Education (Affiliated to Visvesvaraya Technological University, Belagavi,), Bengaluru, 560060, Karnataka, IndiaEfficient land use land cover (LULC) classification is crucial for environmental monitoring, urban planning, and resource management. This study investigates LULC changes in Nanjangud taluk, Mysuru district, Karnataka, India, using remote sensing (RS) and geographic information systems (GIS). This paper mainly focuses on the classification and change detection analysis of LULC in 2010 and 2020 using linear imaging self-scanning sensor-III (LISS-III) remote sensing images. Traditional methods for LULC classification involve manual interpretation of satellite images, which provides lower accuracy. Therefore, this paper proposed the Convolutional Neural Network (CNN)-based deep learning method for LULC classification. The main objective of the research work is to perform an efficient LULC classification for the change detection study of the Nanjagud taluk using the classified maps of the years 2010 and 2020. The experimental results indicate that the proposed classification method is outperformed, with an overall accuracy of 94.08% for the 2010 data and 95.30% for the 2020 data. Further, change detection analysis has been carried out using classified maps and the results show that built-up areas increased by 8.34 sq. km (0.83%), agricultural land expanded by 2.21 sq. km (0.23%), and water bodies grew by 3.31 sq. km (0.35%). Conversely, forest cover declined by 1.49 sq. km (0.15%), and other land uses reduced by 11.93 sq. km (1.22%) over the decade.http://www.sciencedirect.com/science/article/pii/S2590197425000096Remote sensing: geographic information systemsConvolutional neural networksDeep learningChange detectionResourcesat-1Linear imaging self-scanning sensor-III |
| spellingShingle | V. Pushpalatha P.B. Mallikarjuna H.N. Mahendra S. Rama Subramoniam S. Mallikarjunaswamy Land use and land cover classification for change detection studies using convolutional neural network Applied Computing and Geosciences Remote sensing: geographic information systems Convolutional neural networks Deep learning Change detection Resourcesat-1 Linear imaging self-scanning sensor-III |
| title | Land use and land cover classification for change detection studies using convolutional neural network |
| title_full | Land use and land cover classification for change detection studies using convolutional neural network |
| title_fullStr | Land use and land cover classification for change detection studies using convolutional neural network |
| title_full_unstemmed | Land use and land cover classification for change detection studies using convolutional neural network |
| title_short | Land use and land cover classification for change detection studies using convolutional neural network |
| title_sort | land use and land cover classification for change detection studies using convolutional neural network |
| topic | Remote sensing: geographic information systems Convolutional neural networks Deep learning Change detection Resourcesat-1 Linear imaging self-scanning sensor-III |
| url | http://www.sciencedirect.com/science/article/pii/S2590197425000096 |
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