Business-Oriented Information System for Detecting Deforestation Points Using Satellite Data and Deep learning

Authors

  • Frey Sylvestre
  • Musas A musas
  • Anzola Kibamba Nestor
  • Mwamba Kande Franklin
  • Kafunda Katalay Pierre
  • Biaba Kuya Jirinse
  • Oshasha Fiston

DOI:

https://doi.org/10.5281/zenodo.13367432

Keywords:

Deforestation, Machine Learning, Business-Oriented Software, Real-Time Monitoring, Satellite Data

Abstract

This study aims to develop a business-oriented software, EcoWatchtower, for real-time monitoring of deforestation areas using satellite data and machine learning. The objective is to create a system capable of detecting deforestation activities by analyzing satellite images with a deep learning model.

The methodology is based on comparing captured images with reference models to identify anomalies. The results show that the system can automatically generate geolocated alerts, facilitating the rapid intervention of eco-guards, especially in areas where physical surveillance is limited. The main innovation of EcoWatchtower lies in its ability to provide proactive and automated monitoring, precisely marking GPS points of at-risk areas. This system overcomes the challenges of traditional methods by offering an intelligent and continuous solution for forest protection.

In conclusion, EcoWatchtower represents a significant advancement in the fight against deforestation, despite challenges related to data complexity and the continuous improvement of deep learning algorithms.

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Published

2024-08-23

How to Cite

Frey Sylvestre, Musas A musas, Anzola Kibamba Nestor, Mwamba Kande Franklin, Kafunda Katalay Pierre, Biaba Kuya Jirinse, & Oshasha Fiston. (2024). Business-Oriented Information System for Detecting Deforestation Points Using Satellite Data and Deep learning. Revue Internationale De La Recherche Scientifique (Revue-IRS), 2(4), 2019–2030. https://doi.org/10.5281/zenodo.13367432

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