As the demand for efficient and lightweight models in image classification grows, knowledge distillation has emerged as a promising technique to transfer expertise from complex teacher models to simpler student models. However, the efficacy of knowledge distillation is intricately linked to the choice of datasets used during training. Datasets are pivotal in shaping a model’s learning process, influencing its ability to generalize and discriminate between diverse patterns. While considerable research has independently explored knowledge distillation and image classification, a comprehensive understanding of how different datasets impact knowledge distillation remains a critical gap. This study systematically investigates the impact of diverse datasets on knowledge distillation in image classification. By varying dataset characteristics such as size, domain specificity, and inherent biases, we aim to unravel the nuanced relationship between datasets and the efficacy of knowledge transfer. Our experiments employ a range of datasets to comprehensively explore their impact on the performance gains achieved through knowledge distillation. This study contributes valuable guidance for researchers and practitioners seeking to optimize image classification models through kno-featured applications. By elucidating the intricate interplay between dataset characteristics and knowledge distillation outcomes, our findings empower the community to make informed decisions when selecting datasets, ultimately advancing the field toward more robust and efficient model development.

Rapid urbanization poses several challenges, especially when faced with an uncontrolled urban development plan. Therefore, it often leads to anarchic occupation and expansion of cities, resulting in the phenomenon of urban sprawl (US). To support sustainable decision–making in urban planning and policy development, a more effective approach to addressing this issue through US simulation and prediction is essential. Despite the work published in the literature on the use of deep learning (DL) methods to simulate US indicators, almost no work has been published to assess what has already been done, the potential, the issues, and the challenges ahead. By synthesising existing research, we aim to assess the current landscape of the use of DL in modelling US. This article elucidates the complexities of US, focusing on its multifaceted challenges and implications. Through an examination of DL methodologies, we aim to highlight their effectiveness in capturing the complex spatial patterns and relationships associated with US. This work begins by demystifying US, highlighting its multifaceted challenges. In addition, the article examines the synergy between DL and conventional methods, highlighting the advantages and disadvantages. It emerges that the use of DL in the simulation and forecasting of US indicators is increasing, and its potential is very promising for guiding strategic decisions to control and mitigate this phenomenon. Of course, this is not without major challenges, both in terms of data and models and in terms of strategic city planning policies.

Urban sprawl, a common phenomenon characterized by uncontrolled urban growth, has far-reaching socio-economic and environmental implications. It’s a complex phenomenon, and finding a better way to tackle it is essential. Accurate simulation and prediction of urban sprawl features would facilitate decision-making in urban planning and the formulation of city growth policies. This article provides an overview of the techniques used to this end. Initially, it highlights the use of a certain category of so-called traditional methods, such as statistical models or classical machine learning methods. It then focuses particularly on the intersection of deep learning and urban sprawl modelling, examining how deep learning methods are being exploited to simulate and predict urban sprawl. I finally studies hybrid approaches that combine deep learning with agent-based models, cellular automata, or other techniques offer a synergistic way to leverage the strengths of different methodologies for urban sprawl modelling.

Accurate mapping of land use and land cover (LULC) is crucial for various applications such as urban planning, environmental management, and sustainable development, particularly in rapidly growing urban areas. African cities such as Yaoundé, Cameroon, are particularly affected by this rapid and often uncontrolled urban growth with complex spatio-temporal dynamics. Effective modeling of LULC indicators in such areas requires robust algorithms for high-resolution images segmentation and classification, as well as reliable data with great spatio-temporal distributions. Among the most suitable data sources for these types of studies, Sentinel-2 image time series, thanks to their high spatial (10 m) and temporal (5 days) resolution, are a valuable source of data for this task. However, for an effective LULC modeling purpose in such dynamic areas, many challenges remain, including spectral confusion between certain classes, seasonal variability, and spatial heterogeneity. This study proposes a hybrid deep learning architecture combining U-Net and Convolutional Long Short-Term Memory (ConvLSTM) layers, allowing the spatial structures and temporal dynamics of the Sentinel-2 series to be exploited jointly. Applied to the Yaoundé region (Cameroon) over the period 2018–2025, the hybrid model significantly outperforms the U-Net and ConvLSTM models alone. It achieves a macro-average F1 score of 0.893, an accuracy of 0.912, and an average IoU of 0.811 on the test set. These segmentation performances reached up to 0.948, 0.953, and 0.910 for precision, F1-score, and IoU, respectively, on the built-up areas class. Moreover, despite its better performance, in terms of complexity, the figures confirm that the hybrid does not significantly penalize evaluation speed. These results demonstrate the relevance of jointly integrating space and time for robust LULC classification from multi-temporal satellite images.