KEYWORDS: Safety, Web 2.0 technologies, Roads, Visualization, Databases, Computer programming, System integration, Heart, Information visualization, Algorithm development
In a modern urban society with a high demand for mobilization, location-based personal navigation service plays a fundamental role in daily activities. With the help of real-time and accessible GPS technology, online navigation service such as Google Map and Street View have become more important and increasingly popular. Being designed for simplicity and scalability, most of these current service will suggest routes based on the fastest travel time or the shortest distance to a destination. However, in developing countries such as Indonesia, which is still struggling with crime rate issue, the requirement for safety become an undoubtedly crucial factor for human mobility. In this paper, we propose an integrated web-based system using the crime hotspot area based on crime history from the local government agency, existing geotagged social media crime news and user reported data. The users could further involve and contribute by reporting their personal safety experience to increase the recommendation accuracy in the future. Build on the free and opensource GraphHopper Routing API, our proposed personalized user feature also include rerouting option and crime contour map. We focus on Jakarta area as our case study, which served as the heart of citizen activity in Indonesia. The key result of the proposed framework is a personal navigation map that recommends the safest route to the user which bypass potential crime-prone areas.
Food crops monitoring in developing countries such as Indonesia plays an essential role to support national goals in food security and self-sufficiency. One of the fundamental challenges is plant phase classification task which could help to estimate yield before harvest. In contrast to the conventional field survey method which required a large amount of human and capital resources, we explore a more scalable, inexpensive and real-time method using publicly available remote sensing data, i.e. Landsat-8 satellite. Landsat-8 provides rich spatiotemporal features which could support the detection of numerous vegetation and crop-related indices. However, to accurately classify the plant phase, the existing features require additional spectral pattern from different seasons. We found out the existence of temporal autocorrelation among features of food crops plant phase. We propose a supervised random forest method to make features engineering to select best multitemporal features. In this study, we focus on the rice plant phase classification in Banyuwangi Regency, Indonesia as a case study. The ground truth data are the monthly frame area sampling of average rice plant phase at the regency level which officially released by BPS-Statistics Indonesia. The experimental result shows the accuracy of 0.573 with one temporal feature. Furthermore, incorporating four consecutive temporal features gives higher accuracy gain to 0.727 which shows the temporal autocorrelation. Based on the extensive evaluations, our findings and contributions in this study include: (1) insight to capture the temporal autocorrelation to increase the model accuracy (2) a machine learning classification model which is not sensitive to multicollinearity. Our proposed method provides the potential benefit for the government and statistical agencies towards a more scalable agricultural survey.
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