Abstract
In recent years, the convolutional neural network has been demonstrated to be effective in classifying data from animals to objects and as well as the human hand signs. Convolutional Neural Network (CNN) shows high performance on image classification. Recent trends in CNN that are used extensively are transfer learning and data augmentation. In this paper, the classification of Indian Government rural projects such as check dams, farm ponds, soak pits, etc. that promote agricultural activities are classified based on image. These projects built in the rural parts of India are of similar features and hence a challenging task to classify them. Remote-Sensing (RS) model has been proposed for the classification of these projects which is further compared with DenseNet-121 model over the same task on the basis of different data sizes and number of layers. Moreover, checking their influence on classifications of these images has been performed. Using this proposed RS model, a test accuracy of 0.9150 has been achieved.
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Mangla, A., Briskilal, J., Senthil Kumar, D. (2022). Image Classification of Indian Rural Development Projects Using Transfer Learning and CNN. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Expert Clouds and Applications. Lecture Notes in Networks and Systems, vol 444. Springer, Singapore. https://doi.org/10.1007/978-981-19-2500-9_2
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