# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 1 Introduction  
Smartphones are becoming an increasingly important part of people's lives, accompanying us through 24 h a day [1, 2]. With the construction of smart cities, smartphones play a vital role as device terminals, providing people with accurate positioning application services, such as seamless indoor and outdoor switching, tracking, smart shopping malls, smart car parks, and health monitoring. However, accurate, continuous, and reliable positioning services often cannot be achieved by a single source of positioning signals and always rely on a wide range of positioning methods, such as satellite positioning, Bluetooth positioning, Wi-Fi positioning, and various other methods. For example, to obtain more accurate, continuous, and reliable positioning services, these positioning methods are usually combined, resulting in multi-source fusion positioning methods. However, due to the different environments in which they are located, the types of sensors used can also vary significantly, making accurate scene judgment essential for determining reliable positioning [3, 4, 5]. In our daily lives, smartphones can often receive the main sources of signals--global positioning satellite signals, Bluetooth signals, Wi-Fi, light sensors, inertial navigation, and cellular networks--which enable the accurate identification of indoor and outdoor scenes. Smartphone users can invoke location services at any time and in any place, which could be in a playground at noon, an office building in the early hours of the morning, or a shopping mall on a Saturday or Sunday. In short, the varying times and places have a significant impact on location services [6, 7, 8]. According to the different types of sensors, scene classification algorithms can be divided into single-sensor scene classification algorithms and multi-sensor scene classification algorithms [9; 10]. Among them, some studies have used single-sensor classification algorithms; single-sensor algorithms are simple in origin, and the classification model algorithms are relatively lightweight, but they are more restricted with poorer accuracy and poorer scene adaptation ability. SatProbe directly uses the number of visible satellites in the original satellite data to judge an IO scene, which contains 79 original satellite trajectories and 2595 detected points [11], and [PERSON] et al. used machine learning to classify scenes for (2D) light detection and ranging (LiDAR) sensors on the FR079 public dataset as well as a laboratory dataset, both of which achieved better results [12]. [PERSON] et al. extracted the number of visible satellites with carrier-to-noise ratios exceeding 25 dB and the sum of the signal strengths of all visible satellites with carrier-to-noise ratios exceeding 25 dB as features. Then, a machine learning model was used to infer the current scene type [13]. [PERSON] et al. used the number of cells and base station signal strengths of cellular network base stations and exploited the variability of the cellular network distribution in indoor and outdoor scenes to achieve four types of scene classification [14]. Researchers have conducted a lot of research on single-sensor classification algorithms, but single sensors have limited access to information, and indoor and outdoor scene classification algorithms based on a single source are challenging. In order to obtain higher-accuracy scene resolution, researchers have added more sensors and used multi-sensors to obtain more source information. SenseIO uses multiple sensors such as cellular networks, Wi-Fi, accelerometers, and lights in mobile phone sensors to build a SenseIO framework, and finally, it carries out detection on multiple application scenarios to achieve 92% accuracy [15]. [PERSON] et al. proposed a behavior-assisted hidden Markov model and classifier combination to achieve the best environmental detection accuracy of 94.22% on a complex building dataset [16]. [PERSON] et al.