Benchmarking Human Performance for Visual Search of Aerial Images

Aerial images are frequently used in geospatial analysis to inform responses to crises and disasters but can pose unique challenges for visual search when they contain low resolution, degraded information about color, and small object sizes. Aerial image analysis is often performed by humans, but machine learning approaches are being developed to complement manual analysis. To date, however, relatively little work has explored how humans perform visual search on these tasks, and understanding this could ultimately help enable human-machine teaming. We designed a set of studies to understand what features of an aerial image make visual search difficult for humans and what strategies humans use when performing these tasks. Across two experiments, we tested human performance on a counting task with a series of aerial images and examined the influence of features such as target size, location, color, clarity, and number of targets on accuracy and search strategies. Both experiments presented trials consisting of an aerial satellite image; participants were asked to find all instances of a search template in the image. Target size was consistently a significant predictor of performance, influencing not only accuracy of selections but the order in which participants selected target instances in the trial. Experiment 2 demonstrated that the clarity of the target instance and the match between the color of the search template and the color of the target instance also predicted accuracy. Furthermore, color also predicted the order of selecting instances in the trial. These experiments establish not only a benchmark of typical human performance on visual search of aerial images but also identify several features that can influence the task difficulty level for humans. These results have implications for understanding human visual search on real-world tasks and when humans may benefit from automated approaches.

Search for the Unknown: Guidance of Visual Search in the Absence of an Active Template

Can you efficiently look for something even without knowing what it looks like? According to theories of visual search, the answer is no: A template of the search target must be maintained in an active state to guide search for potential locations of the target. Here, we tested the need for an active template by assessing a case in which this template is improbable: the search for a familiar face among unfamiliar ones when the identity of the target face is unknown. Because people are familiar with hundreds of faces, an active guiding template seems unlikely in this case. Nevertheless, participants (35 Israelis and 33 Germans) were able to guide their search as long as extrafoveal processing of the target features was possible. These results challenge current theories of visual search by showing that guidance can rely on long-term memory and extrafoveal processing rather than on an active search template.