Crisis Analytics: Big Data Driven Crisis Response

Disasters have long been a scourge for humanity. With the advances in technology (in terms of computing, communications, and the ability to process and analyze big data), our ability to respond to disasters is at an inflection point. There is great optimism that big data tools can be leveraged to process the large amounts of crisis-related data (in the form of user generated data in addition to the traditional humanitarian data) to provide an insight into the fast-changing situation and help drive an effective disaster response. This article introduces the history and the future of big crisis data analytics, along with a discussion on its promise, challenges, and pitfalls

Detecting animals in African Savanna with UAVs and the crowds

Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks which are routinely performed through manual counting in large collections of images. In this paper, we propose a semi-automatic system able to detect large mammals in semi-arid Savanna. It relies on an animal-detection system based on machine learning, trained with crowd-sourced annotations provided by volunteers who manually interpreted sub-decimeter resolution color images. The system achieves a high recall rate and a human operator can then eliminate false detections with limited effort. Our system provides good perspectives for the development of data-driven management practices in wildlife conservation. It shows that the detection of large mammals in semi-arid Savanna can be approached by processing data provided by standard RGB cameras mounted on affordable fixed wings UAVs

Processing Collections of Geo-Referenced Images for Natural Disasters

After disaster strikes, emergency response teams need to work fast. In this context, crowdsourcing has emerged as a powerful mechanism where volunteers can help to process different tasks such as processing complex images using labeling and classification techniques. In this work we propose to address the problem of how to efficiently process large volumes of georeferenced images using crowdsourcing in the context of high risk such as natural disasters. Research on citizen science and crowdsourcing indicates that volunteers should be able to contribute in a useful way with a limited time to a project, supported by the results of usability studies. We present the design of a platform for real-time processing of georeferenced images. In particular, we focus on the interaction between the crowdsourcing and the volunteers connected to a P2P network.Facultad de Informátic

Processing Collections of Geo-Referenced Images for Natural Disasters

After disaster strikes, emergency response teams need to work fast. In this context, crowdsourcing has emerged as a powerful mechanism where volunteers can help to process different tasks such as processing complex images using labeling and classification techniques. In this work we propose to address the problem of how to efficiently process large volumes of georeferenced images using crowdsourcing in the context of high risk such as natural disasters. Research on citizen science and crowdsourcing indicates that volunteers should be able to contribute in a useful way with a limited time to a project, supported by the results of usability studies. We present the design of a platform for real-time processing of georeferenced images. In particular, we focus on the interaction between the crowdsourcing and the volunteers connected to a P2P network.Facultad de Informátic