Wildland fire is one of the most causes of deforestation, and it has an important impact on atmospheric emissions, notably CO2. It occurs almost every year in Indonesia, especially during the dry season. Therefore, it is necessary to identify the burned areas from remote sensing images to establish the zoning map of areas prone to wildland fires. Many methods have been developed for mapping burned areas from low-resolution to medium-resolution satellite images. One of the popular approaches for mapping tasks is a deep learning approach using U-Net architecture. However, it needs a large amount of representative training data to develop the model. In this paper, we present a new dataset of burned areas in Indonesia for training or evaluating the U-Net model. We delineate burned areas manually by visual interpretation on Landsat-8 satellite images. The dataset is collected from some regions in Indonesia, and it consists of 227 images with a size of 512 × 512 pixels. It contains one or more burned scars or only the background and its labeled masks. The dataset can be used to train and evaluate the deep learning model for image detection, segmentation, and classification tasks related to burned area mapping.
Indonesians parent widely use private school shuttle services for their schoolchild due to their lack of time and effectiveness, unfortunately mostly of those shuttle vehicles (car or motor cycle) services currently cannot be tracked. From a security point of view, the parent's need a system that can identified the location of the vehicle in real-time. With rapid technological development today, parents' skepticism can be overcome by tracking the shuttle vehicles through a mobile applications that connected to Global Positioning System (GPS). This research presents the design of a prototype, called "AS-OJEK", an android-based mobile apps and web technology for schoolchild shuttle applications that used several technology such as Web-services, JSON, PHP, MySQL and bootstrap framework as application builders. The application could be installed on any android smartphone version, it will be able to send the location and displaying the vehicle shuttle location on the smartphone screen and display historical location of the tracked vehicle. Rapid Application Development (RAD) framework was used as a software development method, with its 4 phases; phase 1: requirements planning and specifications, phase 2: user design, phase 3: construction, phase 4: cutover. The application was already appropriate with user's needs, proven by performing functional testing and User Acceptance Test (UAT). Based on the results of the UAT, this application has been running well and succeed sending vehicle location to the server, and can tracked through mobile-apps or web applications.
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