Abstract
Geospatial information systems (GIS) support decision making and situational awareness in a wide variety of applications. These systems often require large amounts of labeled data to be displayed in a way that is easy to use and understand. Manually editing these information displays can be extremely time-consuming for an analyst. Algorithms have been designed to alleviate some of this work by automatically generating map displays or digitizing features. However, these systems regularly make mistakes, requiring analysts to verify and correct their output. This human-in-the-loop process of validating the algorithm’s labels can provide a means to continuously improve a model over time by using interactive machine learning (IML). This process allows for systems that can function with little or no training data and as the features continue to evolve. Such systems must also account for the strengths and limitations of both the analysts and underlying algorithms to avoid unnecessary frustration, encourage adoption, and increase productivity of the human-machine team. In this chapter, we introduce three examples of how IML has been used in GIS systems for airfield change detection, geographic region digitization and digital map editing. We also describe several considerations for designing IML workflows to ensure that the analyst and system complement one another, resulting in increased productivity and quality of the GIS output. Finally, we will consider new challenges that arise when applying IML to the complex task of automatic map labeling.
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Scheuerman, J., Michael, C.J., Landreneau, B., Acklin, D.M., Harman, J.L. (2021). Designing Interactive Machine Learning Systems for GIS Applications. In: Lawless, W.F., Llinas, J., Sofge, D.A., Mittu, R. (eds) Engineering Artificially Intelligent Systems. Lecture Notes in Computer Science(), vol 13000. Springer, Cham. https://doi.org/10.1007/978-3-030-89385-9_9
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