Research Article
Leveraging Machine Learning for Deforestation Detection and Sustainable Forest Management
@INPROCEEDINGS{10.4108/eai.19-12-2025.2366216, author={Mohammad Shaazan Muzaffar and Utkarsh Singh and Zahabiya Hamied and Bharat Bhushan and Shivam Tiwari}, title={Leveraging Machine Learning for Deforestation Detection and Sustainable Forest Management}, proceedings={Proceedings of the 1st International Conference on Computational Techniques in Data Science, IICTDS 2025, 19-20 December 2025, Chandigarh, India}, publisher={EAI}, proceedings_a={IICTDS}, year={2026}, month={11}, keywords={artificial intelligence deforestation monitoring biodiversity supervised learning unsupervised learning sustainability}, doi={10.4108/eai.19-12-2025.2366216} }- Mohammad Shaazan Muzaffar
Utkarsh Singh
Zahabiya Hamied
Bharat Bhushan
Shivam Tiwari
Year: 2026
Leveraging Machine Learning for Deforestation Detection and Sustainable Forest Management
IICTDS
EAI
DOI: 10.4108/eai.19-12-2025.2366216
Abstract
Deforestation a global phenomenon which has been affecting the whole ecosystem, by not just effecting the biodiversity but the livelihoods of people relying on it as well. Recent studies have shown how this problem just tends on growing by each passing day, hence the need for predicting and detecting the drivers for this phenomenon becomes a crucial requirement for developing some responsive policies. The integration of Machine Learning (ML) techniques with the traditional methods of deforestation like monitoring and detection presents a promising solution for the sake of addressing the deforestation, the use of ML can help in preventing the various impacts of deforestation. The aim of this review is to provide a collective review of various ML algorithms and there uses across various dimensions of deforestation, highlighting both their potential and current limitations.