Abstract
In this century, every government in the world is more concern about forest degradation and deforestation. Nowadays, the world population is increasing at a rapid rate. There are many countries which are economically developed but suffering from adverse effects of pollution. So, it has become essential for growing a sufficient number of trees for feeding oxygen to such a huge population. But with the time passing by, due to deforestation, the oxygen level is depleting to a great extent. As trees are the most important source of oxygen, so there is a need to keep a track of the number of trees in a particular area. A manual survey of tracking trees is practically impossible and costly. In this proposed approach, the web-based software is developed to count the number of trees in a particular area of town. The detection and counting of trees are done using TensorFlow Object Detection API to train dataset of Google Earth Image using Faster Region Convolutional Neural Network (Faster-RCNN) and Single Shot Multi-Box Detector (SSD) with InceptionV2 technique. This technique will save a large amount of manual work for monitoring/counting number of trees.
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Gajendra, W.B. (2021). Go Green: A Web-Based Approach for Counting Trees from Google Earth Images. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_89
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DOI: https://doi.org/10.1007/978-3-030-71187-0_89
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