22,586 research outputs found
Viewfinder: final activity report
The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources.
The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation.
The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
Recent research in disaster informatics demonstrates a practical and
important use case of artificial intelligence to save human lives and suffering
during natural disasters based on social media contents (text and images).
While notable progress has been made using texts, research on exploiting the
images remains relatively under-explored. To advance image-based approaches, we
propose MEDIC (Available at: https://crisisnlp.qcri.org/medic/index.html),
which is the largest social media image classification dataset for humanitarian
response consisting of 71,198 images to address four different tasks in a
multi-task learning setup. This is the first dataset of its kind: social media
images, disaster response, and multi-task learning research. An important
property of this dataset is its high potential to facilitate research on
multi-task learning, which recently receives much interest from the machine
learning community and has shown remarkable results in terms of memory,
inference speed, performance, and generalization capability. Therefore, the
proposed dataset is an important resource for advancing image-based disaster
management and multi-task machine learning research. We experiment with
different deep learning architectures and report promising results, which are
above the majority baselines for all tasks. Along with the dataset, we also
release all relevant scripts (https://github.com/firojalam/medic).Comment: Multi-task Learning, Social media images, Image Classification,
Natural disasters, Crisis Informatics, Deep learning, Datase
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