## 1 Introduction
These features are subsequently used to train classifiers, such as support vector machines (SVMs), or neural networks to differentiate between different texture categories, and it finds wide applications including in medicine (e.g., identifying tumors in MRI images) and remote sensing (e.g., land cover classification). In permafrost research, these technologies have been used in geomorphic mapping, geomorphic characterization, and permafrost mapping. Based on borehole data, [PERSON] et al. [19] analyzed the influence of topographic and climatic factors on the distribution of permafrost by using 90 m digital elevation model (DEM) data and developed a binary logistic regression model and a multi-standard analysis model. The prediction probability of permafrost is in good agreement with the field investigation results. To map the permafrost spatial distribution in depth at the micro-scale, [PERSON] et al. [16] investigated another modeling approach and employed three classification methods of statistics and machine learning (logistic regression, SVM, and random forest (RF)). [PERSON] et al. [7] applied a logistic regression model and six multi-criteria analysis methods to assess the distribution and dynamic changes in permafrost on the Qinghai-Tibet Plateau from 1980 to 2010. [PERSON] and [PERSON] [20] calculated the probability of permafrost distribution in the Himalayan observation sample belt by using Sentinel-2A data and a logistic regression model. With the development of remote sensing cloud-computing technology, data production methods are becoming more diversified [21], and it is more convenient to comprehensively apply massive-source remote sensing data to data production [22]. Such conditions provide more ideas for permafrost mapping. The permafrost in Northeast China is mainly distributed in Greater Khingan Mountains (GKM), Lesser Khingan mountains (LKM), and Changbai Mountains, which are sensitive to climate change. Due to the influence of climate warming, the discontinuous permafrost in this region has degraded by 38% in recent decades, and the south boundary of the permafrost range has an obvious tendency to move northward [23]. The degradation of permafrost shows zonality in altitude and latitude, and the degradation order is from hillside to valley and from sunny slope to shady slope; environmental effect has an obvious influence on permafrost [24]. The degradation of permafrost increases carbon emissions [25], freezing and thawing disasters, and the risk of safe operation of infrastructure [26]. Arxan is located in the south boundary of the permafrost in Northeast China. Affected by climate change, the permafrost in this location has significantly degraded in recent years. Our observation results show that the permafrost in Arxan results from latitude and altitude zonality, but there is a lack of detailed research. The high-resolution permafrost data have stronger data availability, higher visualization, and wider application range. The refined permafrost mapping at the regional scale has practical significance for comprehensively understanding the distribution of permafrost and its response to climate warming. Among the existing permafrost distribution data in Northeast China, the highest resolution is 1 km. Higher resolution mapping of permafrost in Northeast China has not been conducted. However, in engineering construction, especially in road construction, the 1 km resolution permafrost distribution data cannot meet the requirements. Therefore, in order to serve the regional development, it is urgent to obtain a high-resolution permafrost data or mapping method. In the process of road construction, a large number of survey data along the road can be obtained. These data have strong spatial continuity. Due to cost reasons, continuous monitoring cannot be conducted on the time scale. However, these data can reflect the spatial difference of permafrost well. The combination of these survey data and statistical models may be an effective method to study the distribution of the permafrost in Northeast China.