Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{179780,
        author = {Mr. Sarthak S. Kute and Mr. Tanmay D. Patil and Mr. Vinayak N. Sonawane and Ms. Sakshi S. Zanzane and Dr. V. S. Bhende},
        title = {Optimal Path Detection Through Forest Region Using Image Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8461-8466},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179780},
        abstract = {Expanding the current road networks as 
part of development schemes often stands in the way of 
forest conservation and ecological sustainability. This 
research tries to find a solution using Optimal Path 
Detection through Forest Regions using Image 
Analytics, which uses advanced machine learning 
algorithms and satellite imagery. The proposed 
solution employs deep learning frameworks such as 
PyTorch as part of YOLOv11 for tree enumeration 
and optimal path prediction while integrating 
geospatial data via APIs like Google Maps for planning 
in real-time. For high-resolution forest analysis, image 
processing tools like OpenCV are utilized improving 
the accuracy of the results. 
Path detection algorithms such as A* algorithm are 
used to detect optimal routes through forest which 
ensures minimal disturbance to the ecology and 
reduces fragmentation of forest regions maintaining 
their integrity. This methodology offers a scalable 
solution for environmentally conscious urban planning 
and supports decision-making in sustainable 
infrastructure development.},
        keywords = {Path Detection, Image Analytics,  Classification/Segmentation of Satellite Images, Tree  Enumeration, Convolutional Neural Networks (CNN),  YOLOv11.},
        month = {May},
        }