Machine Learning-Based Environmental, Social, and Scientific Studies Using Satellite Images: A Case Series

Introduction

Natural calamities like wild-fires, floods, earthquakes, volcanoes etc. and man-made disasters like nuclear, thermal or chemical accidents cannot be fully prevented (Demir et al., 2018). But with the sagacious design combined with the judicious use of technologies like real-time image segmentation (Ratnakumar et al., 2021) and allied architectures, quick actions can be executed to reduce their disastrous impact on humanity.

The Earth's vast canvas, once deciphered solely by trained eyes, is now being unravelled in intricate detail by the powerful duo of satellite imagery (Demir et al., 2018) and machine learning (Doshi, 2018). This ground-breaking marriage is revolutionizing our understanding of environmental disasters, from the destructive attacks of wildfires to the silent creep of deforestation. Traditional methods of monitoring wildfire and its spread rely on ground observation, often hampered by inaccessibility and danger. Satellite images, however, capture the inferno's fury from miles above, providing a real-time view of its path. Machine learning algorithms then step in, analysing the intricate patterns of heat, smoke, and vegetation change to predict the fire's trajectory with startling accuracy. This empowers firefighters to strategize their response, potentially saving lives and property. Beyond the immediate threat of wildfires, machine learning scours satellite images for the subtle scars of deforestation. By identifying changes in vegetation cover over time, algorithms (He, Kaiming et al., 2015) can pinpoint areas where trees have vanished, revealing the extent of illegal logging or the encroachment of agriculture into pristine forests. This information empowers policymakers to crack down on environmental crimes and implement sustainable forest management practices. The applications extend far beyond land-based disasters (Amit et al., 2017). Floods, for instance, leave their mark on the landscape in the form of altered water bodies and inundated vegetation. Machine learning algorithms trained on vast datasets of flood-affected areas can now analyse satellite images to detect floods in near real-time, providing crucial early warnings to vulnerable communities. This satellite-powered revolution in environmental disaster studies is still in its early stages, but the potential is immense. As machine learning algorithms (Rahman et al., 2016) become more sophisticated and data sets continue to grow, we can expect even greater accuracy and insights into the complex interplay between human actions and environmental change. From predicting the next wildfire to safeguarding our dwindling forests, the sky is no longer the limit – it's the launchpad for a new era of environmental intelligence (Voigt et al., 2016) By harnessing the power of machine learning (He et al., 2015) and satellite imagery, we are gaining a deeper understanding of our planet's vulnerabilities and the environmental forces that shape it. This knowledge is not just a technological marvel; it's a beacon of hope in the face of a changing world, empowering us to protect our planet and its inhabitants for generations to come.

When it comes to social studies- Satellite images can procure images of crowded cities 24x7 and compare them, perform complex operations on them and gain valuable social, human-behavioural insights in very less time. Machine learning algorithms can analyze these patterns, revealing insights into population density, economic activity, and even social well-being (Duarte et al., 2018). By tracking changes in light intensity over time, we can map urban growth, identify areas of poverty or deprivation, and even predict disease outbreaks.

Beyond the urban landscape, satellite images are playing a crucial role in scientific research. From tracking the migration patterns of endangered animals to monitoring the health of coral reefs, these images provide a bird's-eye view of complex ecological systems. Machine learning algorithms can identify subtle changes in vegetation cover, water quality, and animal behaviour, helping us understand the impact of climate change and human activities on the environment.