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Tree Segmentation using Deep Learning — This project leverages advanced deep learning models for precise tree segmentation from satellite imagery. By combining computer vision and convolutional neural networks, it accurately identifies and isolates tree regions, enabling applications in forestry management, environmental monitoring, and urban planning.
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Ship Detection using Deep Learning — This project applies deep learning techniques to detect and localize ships in RGB(optical) satellite images. Using convolutional neural networks and image processing methods, it efficiently identifies ships across varying sea and weather conditions, supporting applications in maritime surveillance, traffic monitoring, and environmental analysis.
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Ship Detection using SAR Images and Deep Learning — This project utilizes deep learning models to detect and classify ships from Synthetic Aperture Radar (SAR) imagery. By leveraging the unique features of SAR data, the system achieves robust performance under varying weather and lighting conditions, supporting applications in maritime surveillance, defense, and ocean monitoring.
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Change Detection in SAR Images using Deep Learning — This project employs deep learning models to detect and analyze changes in Synthetic Aperture Radar (SAR) imagery over time. By leveraging the high sensitivity of SAR data to surface variations, the system accurately identifies structural and environmental changes, supporting applications in disaster assessment, urban development monitoring, and environmental management.
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Optical and SAR Image Fusion using Deep Learning — This project focuses on fusing optical and Synthetic Aperture Radar (SAR) images using deep learning techniques to enhance image quality and information content. By combining the spectral details of optical imagery with the structural and textural features of SAR data, the model produces more comprehensive representations for applications in remote sensing, land cover analysis, and environmental monitoring.
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Urban Masking using Deep Learning — This project leverages deep learning techniques to identify and mask urban areas from satellite or aerial imagery. By training convolutional neural networks on geospatial datasets, the model accurately distinguishes built-up regions from natural landscapes, enabling efficient urban mapping, land-use analysis, and environmental monitoring.
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