Earth Observation and Artificial Intelligence: Developing Intelligent Systems for Environmental Change Detection

Ceren Budak

Earth Observation and Artificial Intelligence: Developing Intelligent Systems for Environmental Change Detection

Author(s)	Ceren Budak
Country	United States
Abstract	Earth observation (EO) technologies have become fundamental to understanding environmental change across spatial and temporal scales. The rapid expansion of satellite constellations, unmanned aerial systems, hyperspectral sensors, synthetic aperture radar and high-resolution imaging platforms has generated unprecedented volumes of environmental data. However, extracting meaningful information from these datasets remains challenging because of their scale, heterogeneity, temporal complexity and atmospheric and observational uncertainties. Artificial intelligence (AI), particularly machine learning, deep learning, computer vision and multimodal learning, provides new approaches for transforming raw EO data into actionable environmental intelligence. This paper examines the integration of Earth observation and AI for intelligent environmental change detection, focusing on applications including deforestation, land-use and land-cover change, urban expansion, water-body dynamics, glacier and snow-cover changes, agricultural transformation, wildfire impacts and ecosystem degradation. An Integrated EO-AI Environmental Intelligence Framework is proposed, connecting multisource data acquisition, preprocessing, feature extraction, temporal modelling, change detection, uncertainty estimation and decision support. Particular attention is given to the integration of optical, radar, thermal, hyperspectral and LiDAR observations. The paper also discusses challenges involving cloud contamination, sensor differences, spatial and temporal resolution, limited labelled datasets, algorithmic bias, model interpretability and computational requirements. The analysis suggests that the next generation of environmental monitoring systems will move beyond conventional image classification toward continuously learning, multimodal and context-aware AI systems capable of detecting subtle environmental transformations. The paper concludes that the convergence of EO and AI can significantly improve the speed, scale and precision of environmental change monitoring while supporting climate adaptation, ecosystem conservation, disaster management and sustainable resource governance.
Keywords	Earth Observation, Artificial Intelligence, Environmental Change Detection, Satellite Remote Sensing, Machine Learning, Deep Learning, Computer Vision, Geospatial Intelligence, Environmental Monitoring, Climate Change.
Field	Engineering
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-01-28

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American Journal of Advanced Multidisciplinary Research and Innovation

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Earth Observation and Artificial Intelligence: Developing Intelligent Systems for Environmental Change Detection

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