Search and Discovery Through Human Computation

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.