Speaker
Description
Satellite imagery combined with artificial intelligence presents an innovative approach to monitoring deforestation levels in specific regions. The motivation behind this project stems from the urgent need to track and understand the impact of deforestation on our ecosystems and climate. Deforestation poses significant threats to biodiversity, soil health, and contributes to climate change by releasing stored carbon into the atmosphere.
The scope of this project encompasses the development and deployment of machine learning models trained on satellite imagery data. These models are designed to accurately detect and quantify deforested areas over time. By leveraging AI techniques and image segmentation algorithms, the project aims to achieve precise and scalable analysis of deforestation patterns.
The description of the project involves analyzing satellite images at regular intervals to identify changes in vegetation cover indicative of deforestation activities. The AI model processes large volumes of image data to generate statistical insights, including deforestation rates, affected areas, and trends over time. Visual examples derived from satellite imagery provide compelling evidence of deforestation impact, showcasing the extent of forest loss and its spatial distribution. This project holds promise for conservation efforts, enabling policymakers, researchers, and environmental organizations to make data-driven decisions to mitigate deforestation and preserve vital ecosystems.
