Space Technologies for Ghanaian Development - Research

Abstract

Effective management of natural resources is paramount for Ghana's sustainable development. Space technologies, particularly Earth Observation (EO) satellites, provide a powerful, macro-level view to monitor agriculture, water resources, and the impacts of illegal mining ("galamsey"). This Masters in Computer Science proposal outlines a research agenda to develop a machine learning framework that fuses multi-spectral satellite imagery from publicly available sources (like Sentinel-2 and Landsat) with ground-truth data to create high-resolution, actionable maps for Ghanaian policymakers. The research will focus on a critical case study: detecting and monitoring the extent of galamsey activities and their impact on water bodies and forest cover in Ghana.

Key Research Questions for Space Tech in Ghana

Change Detection with Inconsistent Data: How can we build a robust change detection model that can accurately identify new galamsey sites using satellite images of varying resolutions, from different times of day, and with frequent cloud cover, which is a major issue in Ghana?
AI for Spectral-Spatial Analysis: Can a convolutional neural network (CNN) or a Vision Transformer (ViT) be trained to effectively distinguish the unique spectral signature of galamsey-induced water turbidity and land degradation from other forms of land use change, such as legal mining or farming?
Data Fusion for Improved Accuracy: How can we fuse optical satellite data with Synthetic Aperture Radar (SAR) data, which can penetrate clouds, to create an all-weather monitoring system for illegal mining activities in Ghana?
Model Validation and Deployment: How can the AI model be validated at scale with limited on-the-ground resources? What is the most effective way to disseminate the findings—for example, via an interactive web dashboard—to stakeholders like the Environmental Protection Agency (EPA) of Ghana and local district assemblies?

Proposed Masters in Computer Science Research: An AI-Powered Galamsey Monitoring Platform

The core of this research is to build, train, and validate a deep learning model for the automated detection of illegal mining activities and their environmental impact from satellite data.

Dataset Creation: A large dataset will be created by acquiring historical satellite imagery over known galamsey hotspots in Ghana (e.g., the Pra River Basin). These images will be manually annotated, with the help of local experts, to create ground-truth labels for training the AI model.
Model Development: A U-Net style CNN, a popular architecture for image segmentation, will be developed. The model will be trained to take a multi-spectral satellite image as input and output a segmentation map that classifies each pixel as "Forest," "Water," "Galamsey Site," or "Other."
Temporal Analysis: The model will be applied to a time-series of satellite images to track the expansion of galamsey sites and quantify the rate of deforestation and water pollution over several years.
Web-Based Dashboard: A prototype web application will be developed to visualize the results. This dashboard will allow users to explore a map of Ghana, view the detected galamsey sites, and see time-lapse animations of their environmental impact.

Impact for Ghana and Africa

This research will provide Ghanaian authorities with a powerful, low-cost, and scalable tool to combat a major environmental and economic threat. By automating the analysis of freely available satellite data, it democratizes access to advanced environmental monitoring. The methodologies and open-source platform developed could be readily adapted to address other critical challenges across Africa, such as tracking desertification in the Sahel, monitoring urban sprawl in rapidly growing cities, or assessing crop health to predict and prevent food shortages. It empowers African nations to use space assets for their own sovereign resource management and sustainable development goals.