## 1 Introduction  
Conventional computer algorithms in the geoscience field have been applied mainly for spatial data processing such as stereo analysis of topography, multispectral/hyperspectral analysis, and other remote sensing data compiling. These days, novel approaches combined with contemporary machine learning algorithms are changing the trend of computational geoscience. This is because several ground and remote sensors are producing gigantic geoscientific datasets, which cannot be managed effectively by manual interpretation in terms of precision and speed. The active dynamics of the terrestrial surface provide an optimal ground, where a massive data collection scheme and machine leaning play a significant role. In [1; 2], a digital terrain model (DTM) of a wide area with very high resolution was constructed using an advanced stereo machine vision algorithm on the Advanced Land Observing Satellite (ALOS) Panchromatic Remote-Sensing Instrument for stereo mapping (PRISM) and WorldView satellite data respectively. These trends, such as rapid interepretation of remote sensing data and its automated processing, combined with machine learning, are not limited to only the terrestrial surface. The continuing success of planetary missions for the last few decades has naturally resulted in an enormous number of planetary surface datasets. For instance, the Venus Magellan Mission in 1990 generated more surface image datasets than the total datasets of all previous planetary missions [3]. The 21 st century's planetary missions, such as the Mars Reconnaissance Orbiter (MRO) High-Resolution Imaging Science Experiment (HiRISE) observe with higher spatial resolution than the highest resolution commercial satellite sensors available in Earth's orbit. [4]. Moreover, coverage of the planetary mission sensor often includes the entire planet's or satellite's surface. With the advancement in powerful analytical approaches such as stereo topographic construction [5], the research communities would be benefited enormously by tracing the shape of and changes in the planetary surface. This enables the introduction of more sophisticated study tools such as virtual reality [6; 7], numerical modeling [8; 9], and surface dating [10; 11]. The recent developments in planetary surface research are moving towards the employment of high-end machine learning algorithms for many applications, such as the study of surface chronology based on automated crater counting [12], the classification of geomorphic features [13; 14], and the detection of surface changes [15; 16]. The clues on planetary geological evolution can be traced by deep and shallow subsurface data, which are extracted either by gravitational measurement [17; 18] or shallow surface electromagnetic scanning of orbital ground penetration radar (GPR) [19; 20], as well as surface image analysis. Although the information achieved by subsurface sensing of the planetary mission is highly valuable, it should be noted that the interpretation of orbital GPR equipped in a planetary mission is usually more complicated compared to that of the surface imaging. Data collection in track-wise scanning and an absence of reference information for the subsurface strata of planets are some of the highly challenging technical issues. Moreover, the noise level caused by the interactions between the high energy particles in the planetary ionosphere and the long-wavelength of GPR signal [21] brings a significant difficulty in manual interpretation even for experts. In that context, we aim to develop a machine learning approach for the proper interpretation of in-orbital planetary GPR and reconstruction of shallow subsurface structures in solid planets. We expect that the success of this effort will result in a highly valuable interpretation tool for future planetary missions as well as contemporary GPR sensors (for instance, SHARAD, in Martian orbit, which provides the target data in this study).