Worldwide urbanization drives rural-urban transformation (RUT) which has major consequences in many countries of the Global South where there is an urgent need to better understand and manage the underlying processes and consequences for ecosystem services. To fill existing knowledge gaps on the extent and time course of RUT in Morocco, this study focused on (i) analyzing the spatial patterns of rural-urban transformation in the Rabat-Sale-Kenitra (RSK) region from 1972 to 2020, (ii) identifying key mechanisms of change, and (iii) defining the main driving forces behind the spatial transformation patterns. To this end, we processed data of the Landsat free archive, historical grayscale Corona images, and nighttime lights datasets on Google Earth Engine (GEE) using machine learning classifiers and LandTrendr spectral-temporal segmentation algorithms. With an overall accuracy (OA) ranging from 88–95%, the results revealed that during the study period the RSK region experienced a 473% growth of horizontal built-up reflected in an area increase from 63.4 km²to 299.9 km². The main changes occurred along the Kenitra-Rabat-Temara axis and in central cities connected to the main road network. The horizontal expansion of large and medium-sized cities led to the formation of a Rural-Urban Interface (RUI) on the outskirts. The urban sprawl of some cities has affected the surrounding rural lands within the RUI. Environmental, social, economic, and political forces have interacted in shaping the changes in rural-urban landscapes.

This paper examines the use of image-to-image translation models to colorize grayscale satellite images for improved built-up segmentation of Agadir, Morocco, in 1967 and Les Sables-d’Olonne, France, in 1975. The proposed method applies advanced colorization techniques to historical remote sensing data, enhancing the segmentation process compared to using the original grayscale images. In this study, spatial data such as Landsat 5TM satellite images and declassified satellite images were collected and prepared for analysis. The models were trained and validated using Landsat 5TM RGB images and their corresponding grayscale versions. Once trained, these models were applied to colorize the declassified grayscale satellite images. To train the segmentation models, colorized Landsat images were paired with built-up-area masks, allowing the models to learn the relationship between colorized features and built-up regions. The best-performing segmentation model was then used to segment the colorized declassified images into built-up areas. The results demonstrate that the Attention Pix2Pix model successfully learned to colorize grayscale satellite images accurately, improving the PSNR by up to 27.72 and SSIM by 0.96. Furthermore, the results of segmentation were highly satisfactory, with UNet++ identified as the best-performing model with an mIoU of 96.95% in Greater Agadir and 95.42% in Vendée. These findings indicate that the application of the developed method can achieve accurate and reliable results that can be utilized for future LULC change studies. The innovative approach of the study has significant implications for land planning and management, providing accurate LULC information to inform decisions related to zoning, environmental protection, and disaster management.

Coastal urbanization is increasingly constrained by legacy land-use patterns and escalating climate risks, yet long-term morphological trajectories remain poorly quantified due to the absence of multispectral data in pre-satellite archives. This study introduces a scalable deep learning pipeline that bridges a century-scale domain gap through an attention-enhanced Pix2Pix colorization stage and a few-shot U-Net++ segmentation stage, enabling automated reconstruction of urban expansion from panchromatic historical aerial imagery (1920–1971) and digital aerial photographs (1997) to contemporary very-high-resolution satellite data (2024) in Les Sables-d’Olonne, France. The novelty of the approach lies in coupling generative colorization with epoch-specific fine-tuning to overcome radiometric and annotation bottlenecks that have historically prevented quantitative urban reconstruction from pre-satellite archives. The colorization stage achieved high spectral fidelity (PSNR 35.21 dB, SSIM 0.9762), and segmentation performed strongly on modern imagery (mIoU 0.9789). While the segmentation model performed strongly on modern imagery, direct transfer to historical data exhibited substantial domain shift due to radiometric discrepancies. Few-shot adaptation on year-specific calibration sets recovered reliable building footprints (mIoU 0.53–0.65) across the full timeline. Multi-scalar analysis of the reconstructed footprints revealed constrained anisotropic expansion: early saturation of the coastal historic core, followed by rapid inland peri-urbanization post-1971 driven by geographic barriers. This spatiotemporal shift has entrenched spatial lock-in, placing recent development in retro-littoral zones that are vulnerable to submersion and characterized by severe vegetation loss. The framework unlocks previously inaccessible historical archives for quantitative urban monitoring, providing critical insights into legacy effects of unconstrained growth and informing resilient coastal planning under climate change.

Coastal urbanization is increasingly constrained by legacy land-use patterns and escalating climate risks, yet long-term morphological trajectories remain poorly quantified due to the absence of multispectral data in pre-satellite archives. This study presents a scalable deep learning pipeline that bridges this century-scale domain gap, enabling automated reconstruction of urban expansion from panchromatic historical aerial imagery (1920–1971) and digital aerial photographs (1997) to contemporary very-high-resolution satellite data (2024) in Les Sables-d’Olonne, France. Spectral restoration was performed using an attention-enhanced Pix2Pix generative adversarial network with hybrid inference, achieving high fidelity (PSNR 35.21 dB, SSIM 0.9762). Semantic segmentation was conducted with U-Net++, yielding strong performance on modern data (mIoU 0.9789). However, direct transfer to historical periods suffered from severe domain shift due to radiometric variations.To overcome this limitation without extensive manual annotation, few-shot adaptation was applied on year-specific calibration sets, producing reliable building footprints (mIoU 0.53–0.65) despite degradation. Multi-scalar analysis of the reconstructed footprints revealed constrained anisotropic expansion: early saturation of the coastal historic core, followed by rapid inland peri-urbanization post-1971 driven by geographic barriers. This spatiotemporal shift has entrenched spatial lock-in, placing recent development in retro-littoral zones vulnerable to submersion and characterized by severe vegetation loss. This framework unlocks previously inaccessible historical archives for quantitative urban monitoring, providing critical insights into legacy effects of unconstrained growth and informing resilient coastal planning under climate change.