Deep Learning and Machine Learning Approaches for Satellite-Based Environmental Monitoring: A Comprehensive Survey

Abstract

The proliferation of satellite imagery and environmental
monitoring systems has generated unprecedented volumes
of geospatial data, necessitating advanced computational methods
for effective analysis and interpretation. This comprehensive
review examines recent developments in machine learning techniques
applied to satellite image analysis, with particular emphasis
on three critical domains: deep learning approaches for cloud
detection and segmentation, spatial clustering methodologies for
geospatial data analysis, and time series forecasting models for
environmental prediction. Through systematic analysis of twelve
recent research contributions, this paper identifies key technological
advances, methodological innovations, and emerging
trends in each domain. Deep learning segmentation approaches,
particularly U-Net variants enhanced with attention mechanisms
and ensemble methods, demonstrate superior performance in
cloud detection tasks with accuracy rates exceeding 95%. Spatial
clustering techniques incorporating DBSCAN algorithms and
hierarchical mixture models show significant improvements in
urban delineation and environmental pattern recognition. Time
series forecasting models, especially transformer-based architectures
and fuzzy-enhanced LSTM networks, achieve remarkable
accuracy in long-term environmental prediction with reduced
computational overhead. The integration of these methodologies
presents substantial opportunities for advancing automated environmental
monitoring, climate research, and disaster management
systems.