CHANGE DETECTION APPROACH FOR DETECTING DEFORESTATION

April 2025
Vol-11, Issue-3
Paper ID: 26391
ISSN: 2395-4396
Downloads: 0

Abstract & Details

Research Area
Computer Engineering
Keywords
Deforestation Detection Change Detection Deep Learning ResNet Convolutional Neural Networks Environmental Monitoring Remote Sensing Image Processing Transfer Learning AI for Sustainability.
Abstract
ABSTRACT Abstract Deforestation poses a severe threat to global biodiversity, contributes significantly to climate change, and accelerates habitat loss. Traditional methods of deforestation monitoring rely on manual observation and satellite imagery analysis, which are often time-consuming and prone to human error. To address this challenge, we propose an advanced Change Detection Approach for Detecting Deforestation using a ResNet-based deep learning model. The proposed method classifies forest regions into Deforested or Normal (Non-Deforested) based on aerial or satellite imagery. Upon detecting deforested regions, our approach further applies adaptive thresholding and contour detection to accurately locate and highlight affected areas. The system is trained on a diverse dataset of forest images and leverages transfer learning to enhance model performance. Experimental results demonstrate that the proposed framework achieves high classification accuracy while effectively detecting and visualizing deforested regions. By integrating deep learning and computer vision techniques, this approach offers a scalable, automated, and efficient solution for real-time deforestation monitoring, aiding conservation efforts and environmental policy-making.
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

# Name Institute / Affiliation
1 Abhishek Kailas Tekale P.E.S Modern College of Engineering
2 Diksha Santosh Ausekar P.E.S Modern College of Engineering
3 Atharvaa Santosh Ghasing P.E.S Modern College of Engineering
4 Patil Soham Avinash P.E.S Modern College of Engineering
5 v.v.nemade P.E.S Modern College of Engineering

How to Cite

Use the following formats to cite this article in your research.

APA Style
Tekale, Abhishek Kailas, Ausekar, Diksha Santosh, Ghasing, Atharvaa Santosh, Avinash, Patil Soham, & v.v.nemade (2025). CHANGE DETECTION APPROACH FOR DETECTING DEFORESTATION. International Journal of Advance Research and Innovative Ideas In Education, 11(3), 49-67.
MLA Style
Tekale, Abhishek Kailas, et al. "CHANGE DETECTION APPROACH FOR DETECTING DEFORESTATION." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 3, 2025, pp. 49-67.
IEEE Style
Abhishek Kailas Tekale, Diksha Santosh Ausekar, Atharvaa Santosh Ghasing, Patil Soham Avinash, and v.v.nemade, "CHANGE DETECTION APPROACH FOR DETECTING DEFORESTATION," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 3, pp. 49-67, 2025.
Vancouver Style
Tekale Abhishek Kailas, Ausekar Diksha Santosh, Ghasing Atharvaa Santosh, Avinash Patil Soham, v.v.nemade. CHANGE DETECTION APPROACH FOR DETECTING DEFORESTATION. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(3):49-67.
Harvard Style
Tekale, Abhishek Kailas, Ausekar, Diksha Santosh, Ghasing, Atharvaa Santosh, Avinash, Patil Soham, & v.v.nemade (2025) 'CHANGE DETECTION APPROACH FOR DETECTING DEFORESTATION', International Journal of Advance Research and Innovative Ideas In Education, 11(3), pp. 49-67.
Chicago Style
Tekale, Abhishek Kailas, et al. "CHANGE DETECTION APPROACH FOR DETECTING DEFORESTATION." International Journal of Advance Research and Innovative Ideas In Education 11, no. 3 (2025): 49-67.
Turabian Style
Tekale, Abhishek Kailas, et al. "CHANGE DETECTION APPROACH FOR DETECTING DEFORESTATION." International Journal of Advance Research and Innovative Ideas In Education 11, no. 3 (2025): 49-67.

Export Citation

Related Research

Comprehensive Review of Existing Chatbot Systems for Career Assistance, Resume Support, and ATS-Aware Guidance
Amruta Bargal et al. 2026 Data science
PDF Unavailable
Development of an AI-Powered Multimodal Web Assistant with Intelligent Resume Building and ATS Enhancement
Amruta Bargal et al. 2026 Data science
PDF Unavailable
A Deep Learning-Based Framework for Mood-Oriented Music Recommendation Using Facial Expression Analysis
Vaibhav Ashok Bhangare et al. 2026 Computer Engineering
PDF Unavailable
Survey On : Intelligent Payroll and Human Resource Management Systems: A Systematic Review of Automation, Security, and Analytics
Vishakha Jadhav et al. 2026 Human Resource Management, Artificial Intelligence, Machine Learning, Payroll Systems, Cybersecurity, Business Intelligence, Robotic Process Automation, Employee Analytics, Digital Transforma
PDF Unavailable
Civic Engagement & Empowerment Platform
Supriya Dadaso Bankar et al. 2026 Computer engineering
PDF Unavailable
Recent Developments in Microneedle Technology and Its Diverse Biomedical Applications
Diksha Patil 2026 Technology
PDF Unavailable
RAG System Development with Pydantic AI ChromaDB & Groq
Prof. Priyanka P. Kakade et al. 2026 Computer Engineering
PDF Unavailable
Machine Learning Based Early Stage Diabetes Detection System
Rohan Mulik et al. 2026 Computer Engineering
PDF Unavailable
A Survey on Skillsense:AI Career Analyzer App
Kirti Datir et al. 2026 Computer Engineering
PDF Unavailable
Employee Performance Portal
P.Harika et al. 2026 Computer science and engineering
PDF Unavailable