This review article collects knowledge on the use of eye-tracking and machine learning methods for application in automated and interactive geovisualization systems. Our focus is on exploratory reading of geovisualizations (abbr. geoexploration) and on machine learning tools for exploring vector geospatial data. We particularly consider geospatial data that is unlabeled, confusing or unknown to the user. The contribution of the article is in (i) defining principles and requirements for enabling user interaction with the geovisualizations that learn from and adapt to user behavior, and (ii) reviewing the use of eye tracking and machine learning to design gaze-aware interactive map systems (GAIMS). In this context, we review literature on (i) human-computer interaction (HCI) design for exploring geospatial data, (ii) eye tracking for cartographic user experience, and (iii) machine learning applied to vector geospatial data. The review indicates that combining eye tracking and machine learning is promising in terms of assisting geoexploration. However, more research is needed on eye tracking for interaction and personalization of cartographic/map interfaces as well as on machine learning for detection of geometries in vector format.

Due to digitalization and rapid development of collection techniques for elevation data, the emphasis of users' needs for elevation contours has moved from needs of accurate elevation assessment towards needs of perception of landforms. This article introduces a design approach that considers the changed need, and a solution for accordingly generating contours for topographic maps from digital elevation models (DEMs). The solution builds upon comprehensively modifying and blending complete DEMs in order to make the generation procedure understandable for the map readers and to facilitate the readers' reliable interpretation of contours throughout the depicted terrain. The core of the solution is blending lightly and strongly smoothed DEMs according to the Topographic Position Index (TPI) that measures local deviation from surrounding variations of elevations. The solution is built to continuously consider the requirements for different levels of smoothing in areas of uniformly varying and locally deviating relief. An initial case study for typical and challenging Finnish terrains is described and success of the solution is qualitatively evaluated based on the resulting contours. Eventually, limitations of the solution as well as further evaluation and refinement needs are outlined with an intention to prepare the presented contour generation procedure for production.