Generating Land Cover Maps in Semi-arid Regions Based on a 3D Semantic Segmentation Architecture Using Multi-temporal Sentinel-2 Satellite Images: A Case Study of Ludhiana District in Punjab, India

Using satellite imagery for land cover mapping is of utmost importance due to its role played in environmental management and protection, assessment of natural resources and human activities and planning for sustainable development. The objective of this research study is to generate high-resolution...

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Veröffentlicht in:Journal of the Indian Society of Remote Sensing Jg. 52; H. 2; S. 383 - 398
Hauptverfasser: Buttar, Preetpal Kaur, Sachan, Manoj Kumar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New Delhi Springer India 01.02.2024
Springer Nature B.V
Schlagworte:
Agricultural land
Algorithms
Arid regions
Arid zones
case studies
Classification
cropland
data collection
Datasets
Deep learning
Earth and Environmental Science
Earth Sciences
Environmental management
humans
image analysis
Image segmentation
India
Land cover
Land use
Mapping
Natural resources
Neural networks
Population decline
Population growth
Remote sensing
Remote Sensing/Photogrammetry
Research Article
Satellite imagery
satellites
Semantic segmentation
Semantics
Semi arid areas
Semiarid zones
shrinkage
Sustainable development
urban population
Urban populations
Punjab India
India
Land cover mapping
3D encoder–decoder architecture
Semantic segmentation
Semi-arid regions
Remote sensing
Sentinel-2
ISSN:0255-660X, 0974-3006
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Zusammenfassung:Using satellite imagery for land cover mapping is of utmost importance due to its role played in environmental management and protection, assessment of natural resources and human activities and planning for sustainable development. The objective of this research study is to generate high-resolution land use/land cover maps for the semi-arid regions. First, we introduce a novel district-scale land cover dataset for a semi-arid region spanning over an area of 4978 km 2 of Ludhiana district located in the state of Punjab, India. The dataset is composed of multi-temporal, multi-spectral Sentinel-2 satellite images for the year 2020 along with their ground truth mask. Eight major land use/land cover classes were mapped at the pixel level. Second, in order to exploit the spatio-temporal nature of satellite imagery, we employ a 3D encoder–decoder-based semantic segmentation architecture to produce the land cover maps. Third, we show the relevance of incorporating temporal features of satellite data for land use/land cover mapping through experimental results. Mean Intersection over Union (mIoU) and F1 score of 89.30% and 94.35%, respectively, were achieved. This study is vital from the perspective of understanding the dynamics of land use/land cover patterns in the context of agriculturally rich areas experiencing rapid urban population growth and shrinkage of cropland.
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ISSN:0255-660X
0974-3006
DOI:10.1007/s12524-024-01839-9