Constrained tGAP for generalisation between scales: the case of Dutch topographic data

This article presents the results of integrating large- and medium-scale data into a unified data structure. This structure can be used as a single non-redundant representation for the input data, which can be queried at any arbitrary scale between the source scales. The solution is based on the constrained topological Generalized Area Partition (tGAP), which stores the results of a generalization process applied to the large-scale dataset, and is controlled by the objects of the medium-scale dataset, which act as constraints on the large-scale objects. The result contains the accurate geometry of the large-scale objects enriched with the generalization knowledge of the medium-scale data, stored as references in the constraint tGAP structure. The advantage of this constrained approach over the original tGAP is the higher quality of the aggregated maps. The idea was implemented with real topographic datasets from The Netherlands for the large- (1:1000) and medium-scale (1:10,000) data. The approach is expected to be equally valid for any categorical map and for other scales as well

Love Thy Neighbors: Image Annotation by Exploiting Image Metadata

Some images that are difficult to recognize on their own may become more clear in the context of a neighborhood of related images with similar social-network metadata. We build on this intuition to improve multilabel image annotation. Our model uses image metadata nonparametrically to generate neighborhoods of related images using Jaccard similarities, then uses a deep neural network to blend visual information from the image and its neighbors. Prior work typically models image metadata parametrically, in contrast, our nonparametric treatment allows our model to perform well even when the vocabulary of metadata changes between training and testing. We perform comprehensive experiments on the NUS-WIDE dataset, where we show that our model outperforms state-of-the-art methods for multilabel image annotation even when our model is forced to generalize to new types of metadata.Comment: Accepted to ICCV 201

Visualization in cyber-geography: reconsidering cartography's concept of visualization in current usercentric cybergeographic cosmologies

This article discusses some epistemological problems of a semiotic and cybernetic character in two current scientific cosmologies in the study of geographic information systems (GIS) with special reference to the concept of visualization in modern cartography. Setting off from Michael Batty’s prolegomena for a virtual geography and Michael Goodchild’s “Human-Computer-Reality-Interaction” as the field of a new media convergence and networking of GIS-computation of geo-data, the paper outlines preliminarily a common field of study, namely that of cybernetic geography, or just “cyber-geography) owing to the principal similarities with second order cybernetics. Relating these geographical cosmologies to some of Science’s dominant, historical perceptions of the exploring and appropriating of Nature as an “inventory of knowledge”, the article seeks to identify some basic ontological and epistemological dimensions of cybernetic geography and visualization in modern cartography. The points made is that a generalized notion of visualization understood as the use of maps, or more precisely as cybergeographic GIS-thinking seems necessary as an epistemological as well as a methodological prerequisite to scientific knowledge in cybergeography. Moreover do these generalized concept seem to lead to a displacement of the positions traditionally held by the scientist and lay-man citizen, that is not only in respect of the perception of the matter studied, i.e. the field of geography, but also of the manner in which the scientist informs the lay-man citizen in the course of action in the public participation in decision making; a displacement that seems to lead to a more critical, or perhaps even quasi-scientific approach as concerns the lay-man user