Automatic classification of remote sensing images for land use and land cover (LULC) applications encounter numerous challenges because of multi-resolution data, heterogeneous appearance and multi-spectral complexity. Existing deep learning models, especially conventional CNNs, struggle to include inherent features of objects because of their limited ability in modeling the long-range dependency and global contextual information. To address these limitations, this paper proposes a novel Heterogeneous Dual-Path Wide Residual Network (HDP-WRN) model for enhanced LULC classification. The initial feature extraction is performed using a post-activation residual block and two parallel paths using different convolutional operators with different attention mechanisms. Branch A integrates standard convolutions and Efficient Channel Attention (ECA) to extract fine-grained local textures, and Branch B merges Ghost convolutions and Convolutional Block Attention Module (CBAM) to boost global spatial channel feature extraction effectively and their complementary representations are fused for obtaining discriminative joint feature space effectively. Extensive experiments show that HDP-WRN achieves competitive performance on multiple benchmark datasets, with accuracy of 98.67% on EuroSAT, 95.11% on RSSCN, 96.13% on SIRI-WHU, and 97.24% on UC-Merced. Under our experimental setup, the proposed model outperforms the reported results of several existing methods, though direct comparisons should be interpreted with caution due to differences in training protocols across studies. Results verify the model for accurate and robust LULC classification for diverse spatial resolutions and remote sensing images.
Ijaz Hussain, Wei Chen, Y. Iqbal et al.· Remote Sensing· 0 citations
The overexploitation of groundwater resources is a significant concern due to the potential risks associated with a decline in freshwater availability. Future planning and policymaking should consider long-term groundwater availability and urban expansion patterns to understand urban growth. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the land surface temperature (LST) and vegetation change using Normalized Difference Vegetation Index NDVI analysis have been performed to find the spatial spread of urbanization and its impact on surface temperature in the area. For groundwater assessment, the Gravity Recovery and Climate Experiment (GRACE) data have been used, while for land cover, NDVI, and LST assessment, Landsat data have been used. The GRACE-based groundwater storage (GWS) anomaly has been correlated with Global Precipitation Measurement (GPM) data. An annual groundwater storage decline of ~7.01 mm/year was identified. Groundwater and land-cover changes were evaluated at five-year intervals from 1990 to 2025. The urban expansion from 838 to 1470 km2 coverage shows the rapid expansion and its impact on vegetation and groundwater recharge in the area. The results demonstrate a rapid increase in the urban area, which affected the vegetation and increased the surface temperature in the area. Urban expansion reduced vegetation cover and infiltration, contributing to elevated land surface temperature and groundwater depletion. This study focused on integrating the groundwater impacts due to other environmental variables, i.e., temperature increase and vegetation decrease. The temporal increase in urban expansion decreases the infiltration rate, which impacts the groundwater storage and depletion, as shown by the linear trend. These findings underscore the urgent need for effective groundwater management and vegetation management policies and integrated urban planning strategies to ensure the long-term sustainability of freshwater resources.
Muhammad Zeeshan Ali, M. Benaafi, Mahfuzur Rahman et al.· Earth· 0 citations
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