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This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019–2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km2 (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans. [ABSTRACT FROM AUTHOR]

The study probes into translation students' perception of the value of online peer feedback in improving translation skills. Students enrolled in a translation degree in Australia translated a 250-word text on two separate occasions. On each occasion, the students were given another fellow student's translation of the same text to mark and provide anonymous peer feedback. The original translations from all the students, together with any peer feedback, were uploaded onto an online forum. The students were encouraged to download their own translation to review the peer feedback in it. They were also encouraged to download and peruse other students' peer reviewed translations for comparison. Upon completion of the project, the students were surveyed about their perceptions and appreciation of their engagement in the process in the following three capacities: (i) as a feedback provider, (ii) as a feedback recipient, and (iii) as a peruser of other students' work and the peer feedback therein. Results suggest that translation students appreciate online peer feedback as a valuable activity that facilitates improvement. The students found receiving peer feedback on their own translation especially rewarding, as it offered alternative approaches and perspectives on tackling linguistic/translation issues. In comparing the three capacities, students perceived reviewing feedback on their own work and perusing other students' work as more beneficial than engaging in giving feedback to others. [ABSTRACT FROM AUTHOR]