# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
[PERSON] and colleagues [21] presented an automated approach for detecting hidden waste dumps in Senegal, employing single-shot detector techniques for feature extraction, though facing challenges in regions with imprecise ground truths. [PERSON] and team [22] explored deep learning's generalization capabilities for early weed detection in sorghum fields, creating expert-curated datasets and achieving strong model generalization with an F1-score exceeding 89% on testing data, even in challenging conditions, including degraded captures with motion blur and occluded plants, surpassing existing research. These research studies highlight the application of deep learning and machine learning techniques for various tasks in the context of unmanned aerial vehicles (UAVs) and remote sensing. [PERSON] and colleagues [23] focus on the identification of crop diseases using UAV-based remote sensing, emphasizing the role of image processing methods, assessing the effectiveness of ML and DL techniques, and exploring future research directions in UAV-based crop disease detection and classification [24; 25]. [PERSON], [PERSON], and [PERSON] [26; 27] present lightweight CNN architectures for real-time segmentation and object extraction on IoT edge devices, achieving high performance on datasets suitable for urban and agricultural mapping, as well as road damage detection using YOLO algorithms. [PERSON] and team [28] discuss the use of face identification algorithms in drones for security, identity verification, disaster relief, and more, aiming to aid technologists in developing hybrid algorithms for real-time face recognition across diverse scenarios. These studies demonstrate the versatility of deep learning and UAV-based applications in diverse domains. [PERSON] and [PERSON] [29] presented YOLOv5, a one-shot detector trained with augmented data and pre-trained weights, achieving a 90.40% mean average precision. This represents a significant 21.57% improvement over the previous YOLOv4 model when tested on the same dataset. [PERSON] et al. [30] investigated split learning in IoD networks via simulations. Findings reveal that separation levels have minimal impact on accuracy, increasing client-side layers extends training time, communication overhead is a major bottleneck, client numbers insignificantly affect accuracy, and training time slightly rises with more clients. The methods described in the literature offer advancements in deep learning for drone image classification, but they are subject to various limitations. These include challenges related to data diversity, labeling accuracy, real-time processing, adverse conditions, privacy, benchmarking against existing methods, real-world deployment, and model interpretability. To mitigate these limitations, we reduced bias and domain gap through fine-tuning and domain adaptation techniques, considering a more diverse and representative target dataset, and developing model interpretability tools specific to the transfer learning context. The main findings of this study are as follows:  
* The primary objective of this study is the development of a highly efficient and reliable land-cover (LC) classification system. To achieve this, we strategically employ fine-tuned versions of Inception-v3 and DenseNet networks, emphasizing the transformative impact of transfer learning. The fine-tuning process enables the model to leverage pre-existing knowledge from extensive datasets, enhancing its adaptability to the intricacies of LC classification. This approach increases both the efficiency and reliability of the model, underscoring the vital role of transfer learning methodologies in optimizing the performance of our LC classification system. By aligning with these advanced techniques, our research not only contributes to the evolution of remote-sensing methodologies but also underscores the paramount importance of incorporating cutting-edge methodologies, such as fine-tuning, in the continual enhancement of LC classification systems.