Correction to: Where do university graduates live? – a computer vision approach using satellite images

Applied Intelligence - A Correction to this paper has been published: https://doi.org/10.1007/s10489-021-02433-z     

Towards User‐Centered Active Learning Algorithms

The labeling of data sets is a time‐consuming task, which is, however, an important prerequisite for machine learning and visual analytics. Visual‐interactive labeling (VIAL) provides users an active role in the process of labeling, with the goal to combine the potentials of humans and machines to make labeling more efficient. Recent experiments showed that users apply different strategies when selecting instances for labeling with visual‐interactive interfaces. In this paper, we contribute a systematic quantitative analysis of such user strategies. We identify computational building blocks of user strategies, formalize them, and investigate their potentials for different machine learning tasks in systematic experiments. The core insights of our experiments are as follows. First, we identified that particular user strategies can be used to considerably mitigate the bootstrap (cold start) problem in early labeling phases. Second, we observed that they have the potential to outperform existing active learning strategies in later phases. Third, we analyzed the identified core building blocks, which can serve as the basis for novel selection strategies. Overall, we observed that data‐based user strategies (clusters, dense areas) work considerably well in early phases, while model‐based user strategies (e.g., class separation) perform better during later phases. The insights gained from this work can be applied to develop novel active learning approaches as well as to better guide users in visual interactive labeling.     

A study on skeletonization of complex petroglyph shapes

Multimedia Tools and Applications - In this paper, we present a study on skeletonization of real-world shape data. The data stem from the cultural heritage domain and represent contact tracings of...     

Where do university graduates live? – A computer vision approach using satellite images

In this article, we examine to what extent the settlement of university graduates can be derived from satellite images. We apply a convolutional neural network (CNN) to grid images of a city and predict five density classes of university graduates at a micro level (250 m × 250 m grid size). The CNN reaches an accuracy rate of 40.5% (random approach: 20%). Furthermore, the accuracy increases to 78.3% when considering a one-class deviation compared to the true class. We also examine the predictability of inhabited and uninhabited grid cells, where we achieve an accuracy of 95.3% using the same CNN. From this, we conclude that there is information that correlates with graduate density that can be derived by analysing only satellite images. The findings show the high potential of computer vision for urban and regional economics. Particularly in data-poor regions, the approach utilised facilitates comparative analytics and provides a possible solution for the modifiable aerial unit (MAU) problem. The MAU problem is a statistical bias that can influence the results of a spatial data analysis of point-estimate data that is aggregated in districts of different shapes and sizes, distorting the results.