Identification of Empty Land Based on Google Earth Using Convolutional Neural Network Algorithm
Keywords:CNN, Google Earth, Vacant Land Classification
The development of digital image technology has experienced rapid development, both in terms of the development of models and algorithms used as well as the quality and results of the management process carried out. Utilization of digital image management can be used in classifying the condition of vacant land in certain areas. A high level of urbanization causes an increase in population growth and uneven development in certain areas. Advanced technology has resulted in a vast constellation of satellites and aerial platforms. In general, many remote sensing images with an excellent spatial resolution (VFSR) are commercially available to the general public, like google earth. This platform provides much information regarding spatial conditions. So, data available on the platform allows it to be used as a medium for analyzing and classifying the availability of vacant land in certain areas. To support good regional and city planning and overcome problems due to high levels of urbanization, a model that can automatically classify vacant land in certain areas is needed using data that is openly available on Google Earth. Thus, this study experimented by classifying vacant land based on images from google earth using the Deep Learning model, namely Convolutional Neural Network (CNN). The CNN method is used because of its superiority in classifying images. The experiment results have an optimal for image classification using the CNN algorithm.
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