# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
###### Abstract  
The pace of Land Use/Land Cover (LULC) change has accelerated due to population growth, industrialization, and economic development. To understand and analyze this transformation, it is essential to examine changes in LULC meticulously. LULC classification is a fundamental and complex task that plays a significant role in farming decision making and urban planning for long-term development in the earth observation system. Recent advances in deep learning, transfer learning, and remote sensing technology have simplified the LULC classification problem. Deep transfer learning is particularly useful for addressing the issue of insufficient training data because it reduces the need for equally distributed data. In this study, thirty-nine deep transfer learning models were systematically evaluated alongside multiple deep transfer learning models for LULC classification using a consistent set of criteria. Our experiments will be conducted under controlled conditions to provide valuable insights for future research on LULC classification using deep transfer learning models. Among our models, ResNet50, EfficientNetV2B0, and ResNet152 were the top performers in terms of kappa and accuracy scores. ResNet152 required three times longer training time than EfficientNetV2B0 on our test computer, while ResNet50 took roughly twice as long. ResNet50 achieved an overall f1-score of 0.967 on the test set, with the Highway class having the lowest score and the Sea Lake class having the highest. land use and land cover; image classification; deep learning; transfer learning; EuroSAT; earth observation; satellite image classification [style=]  
Recent advancements in LULC classification have improved noise reduction, cloud shadow suppression, segmentation, and classification techniques [10; 11; 12; 13]. However, the classification remains a complex process due to the increased level of abstraction from pixels to items to landscapes and the diverse geographic distribution of various land cover categories [12; 14]. The aim of this study is to compare thirty-nine open-source deep transfer learning methods for LULC classification using various accuracy metrics and training times, given the importance of this task. This is a novel contribution, as no previous studies have systematically evaluated multiple deep transfer learning models for LULC classification using a consistent set of criteria. By conducting rigorous experiments on these 39 methods under controlled conditions, we can provide valuable insights for future research on LULC classification using deep transfer learning models. These insights will help to reduce the time required to choose the best model for the dataset size and thus enable more efficient and effective LULC classification. Recent studies have demonstrated that Convolutional Neural Networks (CNNs), which are deep learning methods, are commonly utilized in remote sensing image classification due to their remarkable accuracy and performance [15]. However, when training deep-learning models from scratch, the added complexity may lead to overfitting [12; 16]. To address this issue, deep transfer learning has emerged as a new learning framework [17]. By utilizing transfer learning, it is possible to significantly reduce the need for training data and training time in the target domain [18]. This is because the model does not need to be trained from scratch in the target domain, and the train and test sets do not need to be independent or have identical distributions [18]. The primary objective of transfer learning is to improve the performance of designated learners on specific domains by transferring the knowledge gained in various but related source domains [19]. Transfer learning's diverse application opportunities have made it a prominent and captivating subject in the realm of machine learning [19]. [PERSON] et al. [20] applied deep transfer learning techniques to the UC Merced dataset.