# Satellite Image Categorization Using Scalable Deep Learning
## 2 Related Work  
Satellite remote sensing technology has enabled the acquisition of high-resolution imagery that can provide a wealth of information about the Earth's surface. One of the most common applications of satellite imagery is land cover classification, which involves the identification and mapping of different types of land cover, such as forests, urban areas, water bodies, and agricultural fields. Accurate land cover classification is critical for a range of applications, including natural resource management, urban planning, and disaster management. In this literature review, we aim to explore the latest advances in satellite image land cover classification, including the methods and algorithms used, the challenges involved, and the potential for further improvement. Satellite images were once considered the property of research and development giants. However, nowadays, they are becoming increasingly more accessible to research communities working in a variety of domains. Experts in these domains require an automatic interpretation of these satellite images to perform high-level analyses and to support the decision-making process. This is because the manual interpretation of such massive data is not feasible in many scenarios and can lead to inefficient solutions based on these interpretations. So, these domain experts seek support from the computer vision community to provide some solutions for the automatic interpretation and classification of satellite images. Land cover classification using satellite images can be significant for monitoring, analyses, and planning for the improved utilization of resources. The satellite image interpretation process is dependent upon two basic steps: feature extraction and classification. Traditionally, handcrafted features have been utilized, and then these features are fed to conventional machine learning classifiers [28; 29; 30; 31; 32]. Feature extraction in traditional setupshas many associated and inherited challenges, affecting the quality of classification [33]. Conventional machine learning methods, such as decision trees, support vector machines (SVMs), and random forests, have been widely used for land cover classification [31]. These methods require manual feature engineering, where domain experts must carefully choose a set of relevant features to represent the data. The accomplishment of these techniques greatly relies on the quality of the selected features. Conventional machine learning methods can achieve a good accuracy for simple classification tasks, but they may struggle when dealing with complex and high-dimensional challenging data, such as satellite images. In the recent past, these challenges have been well dealt with due to the dawn of deep learning approaches. Deep learning has provided solutions to many challenging problems [34]. Handcrafted feature extraction has been replaced with automatic feature extraction. This approach has demonstrated its robustness in a variety of computer vision problems [31]. Satellite images are no exception, and researchers have employed deep learning approaches to provide satellite image classification, object detection, change detection, shoreline and landslide detection, etc. [33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. These approaches greatly depend on the amount of data and the distribution of data. [PERSON] et al. [44] employed CNNs for ship detection in satellite images, with state-of-the-art models, such as Inception-Resnet, pre-trained on the Image-Net dataset, showing a high accuracy of over 99%. [PERSON] et al. used Dataset-RSI-CB256 to analyze the interpretation of weak labeling on satellite image classification and interpretation [45]. They employed deep learning and adapted the dataset for this analysis. [PERSON] et al. worked on change detection using Satellite Image Classification Dataset-RSI-CB256 [46]. They utilized the deep visual features of these satellite images to analyze changes in the landscape. [PERSON] et al.