How to Cite: Chirkhare, G., Hablani, R., & Balamwar, S. (2022). Prediction of regional vegetation cover using spatial image features and semantic segmentation. International Journal of Health Sciences, 6(S4), 5425–5435. https://doi.org/10.53730/ijhs.v6nS4.9980 International Journal of Health Sciences ISSN 2550-6978 E-ISSN 2550-696X © 2022. Manuscript submitted: 27 March 2022, Manuscript revised: 18 May 2022, Accepted for publication: 9 June 2022 5425 Prediction of regional vegetation cover using spatial image features and semantic segmentation Ganesh Chirkhare Dept. of Computer Science and Engg., Shri Ramdeobaba College of Engineering & Management, Nagpur, India Corresponding author email: ganeshc.edu@gmail.com Dr. Ramchand Hablani Dept. of Computer Science and Engg., Shri Ramdeobaba College of Engineering & Management, Nagpur, India Dr. Sanjay Balamwar Maharashtra Remote Sensing Application Centre Nagpur, India Abstract---Vegetation is an essential part of our ecosystem; it also determines health of our planet. According to “The State of the World’s Forests 2020” only 31% of the global land area is covered with vegetation. This paper presents a superior way of representing vegetation cover in a region by utilizing Atmospherically Resistant Vegetation Index (ARVI) as vital features out of multispectral satellite images. These images comprise Green, Blue, Red and Near Infrared bands data which was further utilized by U-Net to efficiently segment satellite images. In remote sensing greenery of environment is traditionally determined using NDVI, one of the critical imperfections with this method is that it is liable to compute inaccurate values as a consequence of variations in soil, air moisture and shadowing affected by varying incidence angle of sunlight. On the other hand, ARVI is immune to such flaws and integration with deep learning provides a better solution for segmenting regional vegetation and predicting its coverage up to some extent. This method can be employed for predicting vegetation in any region along with assisting in events such as repopulating trees and urban planning while conserving beauty of our nature. Keywords---multispectral, atmospherically resistant vegetation index (ARVI), normalized difference vegetation index (NDVI), near infrared (NIR), maharashtra remote sensing application centre (MRSAC), U- Net, remote sensing.