Deep Learning Techniques for Automated Classification of Satellite Imagery and Land-Use Patterns

Eva Novaković

Deep Learning Techniques for Automated Classification of Satellite Imagery and Land-Use Patterns

Author(s)	Eva Novaković
Country	Slovenia
Abstract	Satellite imagery provides large-scale spatial information that can support land-use monitoring, environmental assessment, urban analysis, agricultural observation, and infrastructure planning. Traditional image-classification approaches generally depend on handcrafted spectral, textural, and spatial descriptors. Deep learning offers an alternative by learning hierarchical image representations directly from training data. Convolutional neural networks are particularly relevant because land-use patterns are represented not only by pixel values but also by spatial arrangements of roads, buildings, vegetation, water, and other landscape elements. This hypothetical methodological study examines deep learning techniques for automated classification of satellite imagery into eight land-use categories. A fictional dataset containing 6,000 optical satellite-image patches is divided into training, validation, and independent testing subsets. Three conceptual approaches are compared: support vector machine classification using handcrafted visual descriptors, a convolutional neural network trained specifically for the satellite dataset, and a transfer-learning convolutional network initialized from a large general-image recognition model. The illustrative analysis evaluates overall accuracy, macro precision, macro recall, and macro F1 score. Under the assigned hypothetical values, the transfer-learning configuration produces the strongest overall classification performance. A second analysis examines class-specific precision, recall, and F1 score across urban built-up land, agricultural land, forest, water bodies, barren land, grassland, transportation corridors, and mixed residential-commercial patterns. Classification difficulty differs across categories according to hypothetical visual similarity and spatial heterogeneity. The study concludes that deep convolutional representations provide a useful methodological framework for automated interpretation of satellite imagery, particularly where spatial patterns are complex and handcrafted descriptors are insufficient. However, model reliability depends on representative training imagery, geographic independence of test data, seasonal variation, image resolution, class-definition quality, and domain shift. All numerical findings reported in this paper are illustrative and require verification using real remote-sensing imagery before practical conclusions can be established.
Keywords	: Deep Learning, Satellite Imagery, Land-Use Classification, Remote Sensing, Convolutional Neural Networks, Transfer Learning, Image Classification, Earth Observation, Land-Cover Analysis, Geospatial Artificial Intelligence.
Field	Engineering
Published In	Volume 2, Issue 3, May-June 2026
Published On	2026-05-29

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International Journal of Machine Learning, AI & Data Science Evolution

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Deep Learning Techniques for Automated Classification of Satellite Imagery and Land-Use Patterns

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