# Satellite Image Categorization Using Scalable Deep Learning
## 1 Introduction  
Earth images collected by satellite are called satellite image sources. These images are also referred to as spaceborne photographs [1]. Satellite companies provide these images for utilization in a variety of application domains.  
Satellite images can be helpful in a diverse variety of application areas. Some of the key application domains are meteorology [2; 3; 4], land planning [5; 6; 7], academia [8], surveillance [9; 10; 11], monitoring, agriculture [12; 13; 14; 15], marine studies [16; 17; 18; 19; 20], conservation [21; 22], geology [23; 24], and warfare [25; 26; 27]. The time-series data of satellite images can be helpful in analyses and long-term planning in various domains. They can be utilized for anomaly detection by observing unusual or unexpected patterns. Satellite images can be presented in a variety of spectra, including visible colors.  
Domain experts from different walks of life are interested in analyzing satellite images for various applications. The computer vision community provides services to solve theapplication-specific analysis of satellite images. However, certain tasks in satellite image analyses are very frequently performed to satisfy application-specific services. Image classification, image interpretation, and object recognition in satellite images are the essential tasks that computer vision experts often employ when dealing with satellite images. Satellite image classification is primarily used for land cover classification, which entails classifying various land cover types, such as forests, croplands, urban areas, and water bodies, from satellite images. Precise land cover classification is crucial for a range of applications, including natural resource management, land use planning, agricultural management, urban planning, environmental impact assessment, and disaster management. It offers valuable insights into land cover changes, ecosystem health monitoring, and sustainable land management practices.  
This paper presents a satellite image interpretation method to categorize satellite images into four different areas, i.e., water bodies, green areas, cloudy areas, and deserts. This interpretation can then be utilized for a number of potential applications. The proposed model provides a better accuracy, as well as empirical results verify it. To show the supremacy of the proposed model, it is also compared with some deep learning models. This work introduces an EfficientNet architecture for image classification, which has been demonstrated to be more effective than other commonly used models. This innovative approach not only increases the accuracy of classification but also reduces the computational cost of training the model. By incorporating EfficientNet, the research demonstrates a major advancement in the field of satellite image classification, opening up possibilities for a more precise and efficient analysis of remote sensing data.  
The main contributions of the study can be summarized as follows:  
* It gives an in-depth analysis of existing techniques for satellite image classification.
* It proposes a scalable deep learning model for the interpretation of satellite images.
* It compares the proposed methodology with state-of-the-art techniques. Visualizations of the feature embeddings of different techniques are also presented.  
The remainder of this paper is arranged as follows: Section 2 briefly discusses the work in the literature, Section 3 introduces the proposed methodology, and Section 4 discusses the achieved results and the analysis of the proposed methodology.