Green Mapper: An AI-Driven Initiative for Aerial Tree Mapping, Maintaining Environmental Balance

Meenakshi Thalor, Shahbaz Khan, Sampada Bhongale, Prayag Bhosale*, Ankita Giri and Shravani Shewale

Department of Information Technology, AISSMS Institute of Information Technology, Pune. India

Corresponding Author E-mail: mrprayag77@gmail.com

DOI : http://dx.doi.org/10.12944/CARJ.12.2.34

Abstract:

Green Mapper revolutionizes environmental conservation efforts with its advanced software solution for efficient tree mapping and analysis. Leveraging the YOLOv5 machine learning algorithm, the platform enables precise identification and quantification of trees in aerial imagery, facilitating strategic resource allocation. Collaborative features foster partnerships with NGOs, promoting coordinated sustainable forest management. YOLOv5's exceptional object detection capabilities justify its selection, ensuring both speed and accuracy in tree detection. Green Mapper's integration of advanced technology and collaboration empowers stakeholders to make informed decisions for sustainable forest management, contributing significantly to environmental conservation efforts.

Keywords:

Environmental conservation; Geographical Information Systems (GIS); Machine learning; Object detection; Tree mapping; YOLOv5

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