Abstract
In the latest evolution of the Internet, human networks are becoming functionalized through collective collaboration frameworks. Questions are now being addressed as never before, by leveraging the easy digital accessibility of crowds to supplement the limitations of machine computation. This is especially relevant in the case of visual analytics where human intuition remains beyond the scope of existing computer object recognition algorithms. Distributing the effort over a massive network of humans not only succeeds in expanding the capacity of human based analytical power, but if set up appropriately, can also provide a statistical basis to pool human perceptive knowledge when identifying the unknown. Here we describe the impacts of this capacity in efforts of search and discovery, where massively parallel human computation can be used to identify anomalies of loosely defined characteristics within large volumes of ultra-high resolution multi-spectral satellite imagery. As human generated data is inherently noisy and subjective in nature, a statistical approach is taken towards consensus based data validation. We show that a spatial landscape can serve as the framework for collaborative computation through an overview of our initial efforts in archaeology, and the subsequent applications in disaster assessment, and search and rescue.
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Acknowledgements
We thank N. Ricklin, S. Har-Noy of Tomnod Inc. as well as the entire Valley of the Khans (VOTK) project team; S. Bira, and T. Ishdorj of the International Association for Mongol Studies and F. Hiebert of the National Geographic Society for co-leadership in field expeditions; D. Vanoni, K. Ponto, D. Lomas, J. Lewis, V. deSa, F. Kuester, and S. Belongie for critical discussions and contributions; S. Poulton and A. Bucci of National Geographic Digital Media; the Digitaria team; Ron Eguchi and ImageCat Inc.; Digital Globe. This effort was made possible by the support of the National Geographic Society, the Waitt Institute for Discovery, the GeoEye Foundation, and the National Science Foundation EAGER ISS-1145291 and HCC IIS-1219138.
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Lin, A.YM., Huynh, A., Barrington, L., Lanckriet, G. (2013). Search and Discovery Through Human Computation. In: Michelucci, P. (eds) Handbook of Human Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8806-4_16
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DOI: https://doi.org/10.1007/978-1-4614-8806-4_16
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