The photograph and our understanding of photography is ever changing and has transitioned from a world of unprocessed rolls of C-41 sitting in a fridge 50 years ago to sharing photos on the 1.5" screen of a point and shoot camera 10 years back. And today the photograph is again something different. The way we take photos is fundamentally different. We can view, share, and interact with photos on the device they were taken on. We can edit, tag, or "filter" photos directly on the camera at the same time the photo is being taken. Photos can be automatically pushed to various online sharing services, and the distinction between photos and videos has lessened. Beyond this, and more importantly, there are now lots of them. To Facebook alone more than 250 billion photos have been uploaded and on average it receives over 350 million new photos every day [6], while YouTube reports that 300 hours of video are uploaded every minute [22]. A back of the envelope estimation reports 10% of all photos in the world were taken in the last 12 months, and that was calculated already more than three years ago [8].Today, a large number of the digital media objects that are shared have been uploaded to services like Flickr or Instagram, which along with their metadata and their social ecosystem form a vibrant environment for finding solutions to many research questions at scale. Photos and videos provide a wealth of information about the universe, covering entertainment, travel, personal records, and various other aspects of life in general as it was when they were taken.

The Placing Task is a yearly challenge offered by the MediaEval Multimedia Benchmarking Initiative that requires participants to develop algorithms that automatically predict the geo-location of social media videos and images. We introduce a recent development of a new standardized webscale geo-tagged dataset for Placing Task 2014, which contains 5.5 million images and 35,000 videos. This standardized benchmark with a large persistent dataset allows the research community to easily evaluate new algorithms and to analyze their performance with respect to the state-ofthe-art approaches. We discuss the characteristics of this year's Placing Task along with the description of the new dataset components and how they were collected.

The automated production of maps of human settlement from recent satellite images is essential to detailed studies of urbanization, population movement, and the like. Commercial satellite imagery is becoming available with sufficient spectral and spatial resolution to apply computer vision techniques previously considered only for laboratory (high resolution, low noise) images. In this paper we attempt to extract human settlement from IKONOS 4-band and panchromatic images using spectral segmentation together with a form of generalized second-order statistics and detection of edges, corners, and other candidate human-made features in the imagery.

The Image Content Engine (ICE) is being developed to provide cueing assistance to human image analysts faced with increasingly large and intractable amounts of image data. The ICE architecture includes user configurable feature extraction pipelines which produce intermediate feature vector and match surface files which can then be accessed by interactive relational queries. Application of the feature extraction algorithms to large collections of images may be extremely time consuming and is launched as a batch job on a Linux cluster. The query interface accesses only the intermediate files and returns candidate hits nearly instantaneously. Queries may be posed for individual objects or collections. The query interface prompts the user for feedback, and applies relevance feedback algorithms to revise the feature vector weighting and focus on relevant search results. Examples of feature extraction and both model-based and search-by-example queries are presented.

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