# An Application of Android Sensors and Google Earth
## 1 Introduction  
Developing countries lack suitable road maintenance, which leads to the deterioration of roads. The current maintenance program reveals that the system used is insufficient to reflect changing conditions and is lacking in its ability to assist in decision making during pavement design [1]. A good pavement management system is therefore necessary to reduce the rate of deterioration and maintain the pavement in a serviceable state [2]. To plan for maintenance properly, sums of data are needed--particularly data on road conditions. Road condition data change over time, especially since collecting them regularly requires a significant investment of resources and time. A pavement maintenance management system (PMMS) consists of a series of decision-based components used to determine where and how repairs should be carried out. Often, maintenance decision making is achieved by feedback from assessments conducted by the local authority. To attain the best utilization of available funds, it is essential to have convenient methods for assessing the road conditions and a database of these conditions so that analyses can be performed, which provides useful information for decision making. Unfortunately, this is not the case in many developing countries due to their limited budgets and less advanced technology. Therefore, most road maintenance decisions are made based on engineering judgments [3]. Road pavement roughness is an acceptable indicator for evaluating the overall condition of a pavement's surface. Road roughness can give agencies useful information for the decision-making processes of maintenance planning and programming [4]. Road roughness is usually measured by an index such as the international roughness index (IRI). Many approaches are available for measuring the IRI, although most of them require sophisticated profilers and tools that are highly accurate but costly to obtain, operate, and maintain, as well as skillful operators. On the other hand, a popular practice in many developing countries is visual inspection. Visual inspection is a relatively cheap option but is very labor-intensive and time-consuming. Therefore, it is necessary to investigate low-cost, practical methods for evaluating roughness. Nowadays, smartphones usually come with many useful and inexpensive embedded sensors, such as three-axis accelerometers, a gyroscope, and a GPS [5]. These sensors can measure the acceleration in m/s\({}^{2}\) along each of the x, y, and z axes, and can be used to recognize motion. Accelerometers in smartphones are primarily used for detecting the orientation of the screen. The embedded sensors in smartphones are capable of recording useful signals for road surface condition estimation, similar to those recorded by high-tech equipment [6]. The embedded sensors within smartphones have been studied with regard to their ability to measure road roughness. The \"SmartRoadSense\" system presented by [PERSON] et al. (2014) aimed to measure road surfaces using smartphones. They established a model for determining a road roughness index from data obtained by the system [7]. [PERSON] and [PERSON] (2014) estimated road conditions by using the intelligent vehicle monitoring system (IVMS) as a reference for establishing a road roughness index. They collected data via the AndroSensor smartphone application to determine road profiles and compute the IRI [6]. [PERSON] et al. (2014) processed integrated acceleration data using computer software. The study collected road profile and acceleration data with both an inertial profiler and a smartphone mounted on a vehicle. The results showed that smartphones measured road roughness with an acceptable accuracy when compared with an inertial profiler [8]. [PERSON] et al. (2015) determined that road roughness depends on a normalized acceleration index. The data were collected by mounting two tablets on a vehicle dashboard. Data were captured on the acceleration in three dimensions, the GPS coordinates, and the speed.