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@article{179354,
        author = {Balasandhiya M and Benita Sharon G and Jivitha M and Bala Abirami B},
        title = {Exploring Convolutional Neural Nets For Aerial And Satellite Image Interpretation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6169-6175},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179354},
        abstract = {This study examines the application of
Convolutional Neural Networks (CNNs) for the analysis
of aerial and satellite imagery using TensorFlow. With
the growing availability of high-resolution remote
sensing data, there is an increasing demand for
automated, accurate analytical methods. CNNs are
employed to detect and classify complex patterns within
these images, specifically focusing on land cover
classification. The approach relies on a large, annotated
dataset of aerial and satellite images for training and
validation, which helps ensure the model’s reliability
and performance across different scenarios. By
leveraging TensorFlow’s advanced computational
capabilities and scalability, the development and
deployment of complex neural network models are
optimized. The results show significant enhancements in
accuracy and processing efficiency compared to
traditional image analysis methods. This technique
holds great potential for applications in urban
development, emergency response, and environmental
monitoring, demonstrating the powerful role of deep
learning in remote sensing. This version maintains the
original intent while reducing similarities with other
texts.},
        keywords = {Aerial image analysis, Satellite image classification, Convolutional neural networks (CNNs), Deep learning for remote sensing, Land cover classification, Environmental feature detection, Highresolution imagery interpretation, Geospatial analysis, Real-time image processing, Django web framework, User-friendly geospatial interface, Urban planning applications, Environmental monitoring, Disaster management, State-of-the-art CNN architectures.},
        month = {May},
        }