@article{579,
  keywords = {Task analysis, Decision Making, Weapons, Optimization, Metadata, Image Reconstruction, Prototypes, Application Motivated Visualization, Geospatial Data, Mixed Initiative Human Machine Analysis, Process Workflow Design, Task Abstractions Application Domains, temporal data, Spacetime, Selection Time, Prototype, Design Process, user study, Aerial Images, Real World Scenarios, Spatial Coverage, Domain Experts, Temporal Coverage, Subset Of Images, User Tasks, Knowledge Gaps, Set Of Equations, Optimal Combination, Temporal Relationship, Direct Use, Explanatory Model, Divide And Conquer, Visual Design, Effective Guidance, Hundreds Of Images, Divide And Conquer Approach, Partner Companies, Image Metadata, Agonistic Behavior, User Selection, Overview Images, Search Task},
  author = {Ignacio Baltazar Pérez Messina and Davide Ceneda and Silvia Miksch},
  title = {Guided Visual Analytics for Image Selection in Time and Space},
  abstract = {<p><span>Unexploded Ordnance (UXO) detection, the identification of remnant active bombs buried underground from archival aerial images, implies a complex workflow involving decision-making at each stage. An essential phase in UXO detection is the task of image selection, where a small subset of images must be chosen from archives to reconstruct an area of interest (AOI) and identify craters. The selected image set must comply with good spatial and temporal coverage over the AOI, particularly in the temporal vicinity of recorded aerial attacks, and do so with minimal images for resource optimization. This paper presents a guidance-enhanced visual analytics prototype to select images for UXO detection. In close collaboration with domain experts, our design process involved analyzing user tasks, eliciting expert knowledge, modeling quality metrics, and choosing appropriate guidance. We report on a user study with two real-world scenarios of image selection performed with and without guidance. Our solution was well-received and deemed highly usable. Through the lens of our task-based design and developed quality measures, we observed guidance-driven changes in user behavior and improved quality of analysis results. An expert evaluation of the study allowed us to improve our guidance-enhanced prototype further and discuss new possibilities for user-adaptive guidance.</span></p>},
  year = {2024},
  journal = {IEEE Transactions on Visualization & Computer Graphics},
  volume = {30},
  chapter = {66},
  pages = {10},
  month = {01/2024},
  issn = {1941-0506},
  url = {https://doi.ieeecomputersociety.org/10.1109/TVCG.2023.3326572},
  doi = {10.1109/TVCG.2023.3326572},
}