Huiyu1999/African-mining

# Mining Mines: A Case Study on Detecting Mining Activities in Eastern Congo Using Satellite Imagery and Scalable Computing Methods ## Introduction The Eastern Congo is a region rich in minerals such as gold, tantalum, and tin, which are vital for various industries worldwide. However, the artisanal mining activities in this area are often unregulated, leading to significant environmental degradation and severe social issues, including human rights abuses and armed conflicts. Monitoring and regulating these activities is crucial for mitigating their negative impacts. Traditional methods of monitoring, such as on-ground inspections, are often impractical due to the region's remote and conflict-prone nature. Therefore, leveraging advanced technologies like satellite imagery and scalable computing methods offers a promising solution to this challenge. Our project focuses on detecting artisan mining activities in Eastern Congo using satellite imagery data from the Landsat dataset hosted on AWS. The primary research problem is to develop an accurate and efficient method for identifying active mining areas using remote sensing techniques. This task involves processing vast amounts of satellite data, which requires substantial computational resources. By employing scalable computing methods, we aim to create a model that can be used for continuous monitoring and regulation of mining activities, ultimately contributing to the region's sustainable development. ## Justification for Using Scalable Computing Methods 1. Volume of Data The Landsat dataset comprises decades of satellite imagery, capturing vast geographical areas with high temporal frequency. Analyzing this data manually is not feasible due to its sheer volume. Scalable computing methods, such as cloud computing and distributed processing, enable us to handle and analyze large datasets efficiently. 2. High-Resolution Analysis Detecting mining activities requires high-resolution analysis to distinguish betwee …