Vertical Color Maps: A Data Independent Alternative to Floor Plan Maps

Location sharing in indoor environments is limited by the sparse availability of indoor positioning and lack of geographical building data. Recently, several solutions have begun to implement digital maps for use in indoor space. The map design is often a variant of floor-plan maps. Whereas massive databases and GIS exist for outdoor use, the majority of indoor environments are not yet available in a consistent digital format. This dearth of indoor maps is problematic, as navigating multistorey buildings is known to create greater difficulty in maintaining spatial orientation and developing accurate cognitive maps. The development of standardized, more intuitive indoor maps can address this vexing problem. The authors therefore present an alternative solution to current indoor map design that explores the possibility of using colour to represent the vertical dimension on the map. Importantly, this solution is independent of existing geographical building data. The new design is hypothesized to do a better job than existing solutions of facilitating the integration of indoor spaces. Findings from a human experiment with 251 participants demonstrate that the vertical colour map is a valid alternative to the regular floor-plan map

MapAI: Precision in BuildingSegmentation

MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) 1 in collaboration with Centre for Artificial Intelligence Research at the University of Agder (CAIR)2 , the Norwegian Mapping Authority3 , AI:Hub4 , Norkart5 , and the Danish Agency for Data Supply and Infrastructure6 . The competition will be held in the fall of 2022. It will be concluded at the Northern Lights Deep Learning conference focusing on the segmentation of buildings using aerial images and laser data. We propose two different tasks to segment buildings, where the first task can only utilize aerial images, while the second must use laser data (LiDAR) with or without aerial images. Furthermore, we use IoU and Boundary IoU [1] to properly evaluate the precision of the models, with the latter being an IoU measure that evaluates the results’ boundaries. We provide the participants with a training dataset and keep a test dataset for evaluation.publishedVersio

A New Concept of Multi-Scenario, Multi-Component Animated Maps for the Visualization of Spatio-Temporal Landscape Evolution

In this paper, we propose a new approach to the presentation of the spatio-temporal evolution of landscape using a multi-component multi-scenario animated map system. The concept of multi-scenario map was introduced with a few conceptual level objectives. Firstly, to facilitate understanding of geographic spatio-temporal changeability (especially landscape changeability) by the use of complex cartographic animations. Secondly, to investigate factors which influence an intuitive and effective use of multi-component cartographic applications. In relation to understanding processes, the overriding purpose was to build up a generic approach that allows users to recognize features of complex geographic phenomena. Finally, since the implementation of the concept was of importance, a prototype has been prepared

MapAI: Precision in Building Segmentation

MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) in collaboration with Centre for Artificial Intelligence Research at the University of Agder (CAIR), the Norwegian Mapping Authority, AI:Hub, Norkart, and the Danish Agency for Data Supply and Infrastructure. The competition will be held in the fall of 2022. It will be concluded at the Northern Lights Deep Learning conference focusing on the segmentation of buildings using aerial images and laser data. We propose two different tasks to segment buildings, where the first task can only utilize aerial images, while the second must use laser data (LiDAR) with or without aerial images. Furthermore, we use IoU and Boundary IoU to properly evaluate the precision of the models, with the latter being an IoU measure that evaluates the results' boundaries. We provide the participants with a training dataset and keep a test dataset for evaluation.MapAI: Presis Bygningssegmentering er en konkurranse arrangert med Norwegian Artificial Intelligence Research Consortium (NORA) i samarbeid med Centre for Artificial Intelligence Research på Universitetet i Agder, Kartverket, AI:Hub, Norkart, og Styrelsen for Dataforsyning og Infrastruktur i Danmark. Konkurransen holdes høsten 2022. Resultatene vil bli presentert på Northern Lights Deep Learning konferansen med fokus på segmentering av bygninger ved bruk av flybilder og laserdata. Konkurransen er delt opp i to forskjellige oppgaver, hvor den første er å segmentere bygninger kun ved bruk av flybilder, mens i den andre må man bruke laserdata og kan kombinere dette med flydata. For evalueringen bruker vi IoU og Boundary IoU til å måle nøyaktigheten til modellene. Boundary IoU er en målemetode som spesielt fokuserer på kanten og formen til segmenteringsmaskene. Deltakerene får et treningsdataset, mens vi holder testdatasettet skjult til konkurransen er over