Problem statement
Monitoring Earth’s ecosystems is essential, but traditional methods are slow and
expensive. Satellite imagery offers vast data, but manual analysis is impractical. The
challenge is to create an automated system for efficient and accurate satellite image
analysis to detect environmental changes like deforestation and land use shifts.
Abstract
This project aims to use advanced image analysis and machine learning on
satellite imagery to automate environmental monitoring. The system will identify
changes, like deforestation, through a scalable and robust platform. The goal is
to provide timely insights into environmental shifts by handling large datasets
efficiently.
Outcome
● Automated Change Detection:
Identify environmental changes in satellite images such as deforestation or land cover
alterations.
● Data Visualization:
Develop an intuitive interface for stakeholders with interactive maps and time-series
analysis.
● Scalable Architecture:
Design a scalable system to process data from multiple satellite sources and cover
large geographical areas.
● Machine Learning Integration:
Integrate machine learning algorithms to enhance the accuracy of environmental
change detection.
● Alerting System:
Implement a real-time alerting mechanism for prompt response to significant
environmental changes.