AccuLandNet: Enhancing Land Cover Detection with Deep Integrated Learning

Now-a-days, population growth is increasing more and more in all the places of the world. Specifically, this increase is in urban development based on economic and industrial improvement. It shows the massive impact on Land Use/Land Cover (LULC) and may change many times. The most popular use of land cover categorization is to analyze satellite imagery to categorize different land surface types, such as urban areas, agricultural fields, forests, and aquatic bodies. With the help of several land cover images, a unique classification model (UCM) based on satellite image classification will be developed in this study. The proposed approach implements the following stages. In the first stage, the pre-trained model U-Net was used to train the satellite images. In the second stage, the preprocessing techniques, including data acquisition and noise reduction, such as Adaptive Noise Removal (ANR) and Histogram Equalization (AHE), were used to preprocess the images. The third stage focused on extracting the features using Multi-Sensor Data Fusion (MSDF) to extract features like water bodies, roads, urban areas, edges, boundaries, and shapes. The final step uses the Maximum Likelihood Classification (MLC) combined with Support Vector Machines (SVM) to give the advanced classification results. Experimental results explain that the proposed approach outperformed the existing models in terms of better outcomes.

@article{Guthikonda2025,
  title     = {AccuLandNet: Enhancing Land Cover Detection with Deep Integrated Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Geetha Guthikonda and M. Senthil Kumaran},
  doi       = {10.14569/IJACSA.2025.0160662},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160662}
}