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

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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Publication Details

Journal
SPIRE - Sciences Po Institutional REpository
Published
2024-08-23
DOI
https://doi.org/10.5281/zenodo.13367432
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Kafunda Katalay, Oshasha Oshasha Fiston, Kuya Jirinse, Musas A Musas et al.
SPIRE - Sciences Po Institutional REpository
Remote Sensing and LiDAR Applications
article

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

Kafunda Katalay, Oshasha Oshasha Fiston, Kuya Jirinse, Musas A Musas, Mwamba Kande, Frey Sylvestre, Kibamba Nestor
article en

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.

SPIRE - Sciences Po Institutional REpository
University of Kinshasa (CD)
Climate action
Openalex Percentile: Top 88%
Remote Sensing and LiDAR Applications
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Business-Oriented Information System for Detecting Deforestation Points Using Satellite Data and Deep learning — Kafunda Katalay, Oshasha Oshasha Fiston, et al. · SPIRE - Sciences Po Institutional REpository (2024) | TGRS Research Map | TGRS