Abstract
In today’s world, satellites are vital nodes in collecting and sharing information. Such information is in the form of images and is accurate. It is of use only if analyzed properly. However, the sheer volume of information in the form of images discourages analysis by manual scrutiny. Therefore, computers are taking over the job of image processing and analysis. First step in image processing is image detection and identification. Next step is tracking the image if need be. The algorithms developed so far have inherent disadvantage of dependency on white or plain background as edge tracking was dependent upon pixel delineation. So, these algorithms enjoyed limited success. ‘Detection of windmills using satellite images through neural networks’ is an attempt to detect windmills in satellite images. It is known that the satellite images set do change in every one to three months. This will be beneficial to get the data of any new windmill installed in any area and subsequently predict the energy production. This project can further be implemented to detect solar power panels and further predict their energy production as well.
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Mridula, A., Sharma, S. (2021). WMDeepConvNets Windmill Detection Using Deep Learning from Satellite Images. In: Kotecha, K., Piuri, V., Shah, H., Patel, R. (eds) Data Science and Intelligent Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 52. Springer, Singapore. https://doi.org/10.1007/978-981-15-4474-3_19
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DOI: https://doi.org/10.1007/978-981-15-4474-3_19
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