Satellite Based Land Cover Classification Using Ai
Year: 2025
Type: article
Abstract: Accurate land cover classification plays a crucial role in environmental monitoring and resource management. This project leverages artificial intelligence (AI) techniques to enhance land cover classification using satellite imagery, integrating deep learning and machine learning models for precise and automated analysis. Feature extraction techniques and classification algorithms, such as Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), are employed to categorize different land cover types with high accuracy. The system utilizes preprocessing methods like image enhancement and segmentation to refine classification results. Additionally, explainability tools such as Grad-CAM help interpret model predictions, making the system reliable for real-world applications. Its adaptability to various satellite datasets ensures robust performance across different geographical regions, reducing manual effort and enhancing decision-making in environmental management. The integration of multi-temporal and multimodal satellite imagery further improves classification accuracy and practical applicability.
Authors: Mr.C. Stalin Jose, K.Vishnuprasath, G.R.Velmurugan, D.Vasanthkuma, S.Varshan
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: green
OpenAlex ID: W6968256346
FWCI:
Cites:
Cited by:
Related to: