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<p>Remote sensing has rapidly advanced with the integration of deep learning, enabling more accurate and scalable detection of land use and land cover (LULC) changes, particularly with the increasing availability of Sentinel-1 and Sentinel-2 multispectral imagery. This review traces the evolution of classical machine learning approaches toward modern deep learning architectures, including Convolutional Neural Networks (CNNs), encoder–decoder models, Siamese and dual-stream networks, attention-based frameworks, and, more recently, Transformer-based models. Recent developments in Earth observation foundation models, trained on large-scale, multimodal datasets, have introduced new capabilities, including zero-shot inference, cross-sensor transferability, and improved generalization across diverse geographic regions. Despite these advances, significant challenges remain. The fusion of multimodal data, including optical, SAR, and ancillary sources, is complicated by differences in spatial, spectral, and temporal characteristics. Furthermore, domain adaptation, label noise, and limited geographic transferability continue to constrain the robustness of change detection pipelines. The quantification of uncertainty and model interpretability has also become increasingly important for operational applications in urban planning, agriculture, ecosystem monitoring, and disaster response. In addition, the growing computational and environmental costs of large-scale model pretraining underscore the need for more sustainable AI practices. Future research should therefore focus on advancing the Earth observation foundation and generative models, developing temporal AI methods for long-term sequence analysis, and promoting responsible, energy-efficient geospatial artificial intelligence. Integrating advances in remote sensing, machine learning, and environmental science will be essential for building practical, scalable, and reliable planetary monitoring systems.</p>

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Machine and deep learning for sentinel-based land use and land cover change detection: a systematic review and future outlook

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Anam Nigar,
  • Yang Li,
  • Muhammad Yousuf Jat Baloch,
  • Sedra Shafi,
  • Abdullahi Garba Usman

摘要

Remote sensing has rapidly advanced with the integration of deep learning, enabling more accurate and scalable detection of land use and land cover (LULC) changes, particularly with the increasing availability of Sentinel-1 and Sentinel-2 multispectral imagery. This review traces the evolution of classical machine learning approaches toward modern deep learning architectures, including Convolutional Neural Networks (CNNs), encoder–decoder models, Siamese and dual-stream networks, attention-based frameworks, and, more recently, Transformer-based models. Recent developments in Earth observation foundation models, trained on large-scale, multimodal datasets, have introduced new capabilities, including zero-shot inference, cross-sensor transferability, and improved generalization across diverse geographic regions. Despite these advances, significant challenges remain. The fusion of multimodal data, including optical, SAR, and ancillary sources, is complicated by differences in spatial, spectral, and temporal characteristics. Furthermore, domain adaptation, label noise, and limited geographic transferability continue to constrain the robustness of change detection pipelines. The quantification of uncertainty and model interpretability has also become increasingly important for operational applications in urban planning, agriculture, ecosystem monitoring, and disaster response. In addition, the growing computational and environmental costs of large-scale model pretraining underscore the need for more sustainable AI practices. Future research should therefore focus on advancing the Earth observation foundation and generative models, developing temporal AI methods for long-term sequence analysis, and promoting responsible, energy-efficient geospatial artificial intelligence. Integrating advances in remote sensing, machine learning, and environmental science will be essential for building practical, scalable, and reliable planetary monitoring systems.