Reconstructing historical 3D city models

Historical maps are increasingly used for studying how cities have evolved over time, and their applications are multiple: understanding past outbreaks, urban morphology, economy, etc. However, these maps are usually scans of older paper maps, and they are therefore restricted to two dimensions. We investigate in this paper how historical maps can be ‘augmented’ with the third dimension so that buildings have heights, volumes, and roof shapes. The resulting 3D city models, also known as digital twins, have several benefits in practice since it is known that some spatial analyses are only possible in 3D: visibility studies, wind flow analyses, population estimation, etc. At this moment, reconstructing historical models is (mostly) a manual and very time-consuming operation, and it is plagued by inaccuracies in the 2D maps. In this paper, we present a new methodology to reconstruct 3D buildings from historical maps, we developed it with the aim of automating the process as much as possible, and we discuss the engineering decisions we made when implementing it. Our methodology uses extra datasets for height extraction, reuses the 3D models of buildings that still exist, and infers other buildings with procedural modelling. We have implemented and tested our methodology with real-world historical maps of European cities for different times between 1700 and 2000

Modeling and manipulating spacetime objects in a true 4D model

The concept of spacetime has long been used in physics to refer to models that integrate 3D space and time as a single 4D continuum. We argue in this paper that it is also advantageous to use this concept in a practical geographic context by realizing a true 4D model, where time is modeled and implemented as a dimension in the same manner as the three spatial dimensions. Within this paper we focus on 4D vector objects, which can be implemented using dimension-independent data structures such as generalized maps. A 4D vector model allows us to create and manipulate models with actual 4D objects and the topological relationships connecting them, all of which have a geometric interpretation and can be constructed, modified, and queried. In this paper we discuss where such a 4D model fits with respect to other spatiotemporal modeling approaches, and we show concretely how higher-dimensional modeling can be used to represent such 4D objects and topological relationships. In addition, we explain how the 4D objects in such a system can be created and manipulated using a small set of implementable operations, which use simple 3D space and 1D time inputs for intuitiveness and which modify the underlying 4D model indirectly

DDL-MVS: Depth Discontinuity Learning for MVS Networks

Traditional MVS methods have good accuracy but struggle with completeness, while recently developed learning-based multi-view stereo (MVS) techniques have improved completeness except accuracy being compromised. We propose depth discontinuity learning for MVS methods, which further improves accuracy while retaining the completeness of the reconstruction. Our idea is to jointly estimate the depth and boundary maps where the boundary maps are explicitly used for further refinement of the depth maps. We validate our idea and demonstrate that our strategies can be easily integrated into the existing learning-based MVS pipeline where the reconstruction depends on high-quality depth map estimation. Extensive experiments on various datasets show that our method improves reconstruction quality compared to baseline. Experiments also demonstrate that the presented model and strategies have good generalization capabilities. The source code will be available soon

Validation and automatic repair of planar partitions using a constrained triangulation

Planar partitions are frequently used to model, among others, land cover, cadastral parcels and administrative boundaries. In practice, they are often stored as a set of individual polygons to which attributes are attached (e.g. with the Simple Features paradigm), causing different errors and inconsistencies (e.g. gaps, overlaps and disconnected polygons), which are introduced during their creation, manipulation and exchange. These errors severely hamper the use of planar partitions in other software (e.g. due to false assumptions causing erroneous calculations). Existing approaches to validate planar partitions involve first building a planar graph of the polygons and enforcing constraints, then repair is done by snapping vertices and edges of this graph. We argue that these approaches have many shortcomings in terms of complexity, numerical robustness and difficulty of implementation, and do not guarantee valid results. Furthermore, they are semi-automatic, requiring manual user intervention. We propose in this paper a novel method to validate and automatically repair planar partitions. It uses a constrained triangulation of the polygons as a base-which by definition is a planar partition-and only simple operations are needed (i.e. labelling of triangles) to both validate and repair. Perhaps the biggest advantage of our method is that we can guarantee that a planar partition is valid after repair. In the paper we describe the details of our method, our implementation, and the experiments we have done with real-world datasets. We show that our implementation scales to big datasets and that it offers better capabilities and overall performance than existing solutions

A Model-based Architecture for Autonomic and Heterogeneous Cloud Systems

Best Paper AwardInternational audienceOver the last few years, Autonomic Computing has been a key enabler for Cloud system's dynamic adaptation. However, autonomously managing complex systems (such as in the Cloud context) is not trivial and may quickly become fastidious and error-prone. We advocate that Cloud artifacts, regardless of the layer carrying them, share many common characteristics. Thus, this makes it possible to specify, (re)configure and monitor them in an homogeneous way. To this end, we propose a generic model-based architecture for allowing the autonomic management of any Cloud system. From a " XaaS " model describing a given Cloud system, possibly over multiple layers of the Cloud stack, Cloud administrators can derive an autonomic manager for this system. This paper introduces the designed model-based architecture, and notably its core generic XaaS modeling language. It also describes the integration with a constraint solver to be used by the autonomic manager , as well as the interoperability with a Cloud standard (TOSCA). It presents an implementation (with its application on a multi-layer Cloud system) and compares the proposed approach with other existing solutions

Interactions between visceral afferent signaling and stimulus processing

Visceral afferent signals to the brain influence thoughts, feelings and behaviour. Here we highlight the findings of a set of empirical investigations in humans concerning body-mind interaction that focus on how feedback from states of autonomic arousal shapes cognition and emotion. There is a longstanding debate regarding the contribution of the body, to mental processes. Recent theoretical models broadly acknowledge the role of (autonomically mediated) physiological arousal to emotional, social and motivational behaviours, yet the underlying mechanisms are only partially characterized. Neuroimaging is overcoming this shortfall; first, by demonstrating correlations between autonomic change and discrete patterns of evoked, and task- independent, neural activity; second, by mapping the central consequences of clinical perturbations in autonomic response and; third, by probing how dynamic fluctuations in peripheral autonomic state are integrated with perceptual, cognitive and emotional processes. Building on the notion that an important source of the brain’s representation of physiological arousal is derived from afferent information from arterial baroreceptors, we have exploited the phasic nature of these signals to show their differential contribution to the processing of emotionally-salient stimuli. This recent work highlights the facilitation at neural and behavioral levels of fear and threat processing that contrasts with the more established observations of the inhibition of central pain processing during baroreceptors activation. The implications of this body-brain-mind axis are discussed

Under pressure: Response urgency modulates striatal and insula activity during decision-making under risk

When deciding whether to bet in situations that involve potential monetary loss or gain (mixed gambles), a subjective sense of pressure can influence the evaluation of the expected utility associated with each choice option. Here, we explored how gambling decisions, their psychophysiological and neural counterparts are modulated by an induced sense of urgency to respond. Urgency influenced decision times and evoked heart rate responses, interacting with the expected value of each gamble. Using functional MRI, we observed that this interaction was associated with changes in the activity of the striatum, a critical region for both reward and choice selection, and within the insula, a region implicated as the substrate of affective feelings arising from interoceptive signals which influence motivational behavior. Our findings bridge current psychophysiological and neurobiological models of value representation and action-programming, identifying the striatum and insular cortex as the key substrates of decision-making under risk and urgency

Effect of parasympathetic stimulation on brain activity during appraisal of fearful expressions

Autonomic nervous system activity is an important component of human emotion. Mental processes influence bodily physiology, which in turn feeds back to influence thoughts and feelings. Afferent cardiovascular signals from arterial baroreceptors in the carotid sinuses are processed within the brain and contribute to this two-way communication with the body. These carotid baroreceptors can be stimulated non-invasively by externally applying focal negative pressure bilaterally to the neck. In an experiment combining functional neuroimaging (fMRI) with carotid stimulation in healthy participants, we tested the hypothesis that manipulating afferent cardiovascular signals alters the central processing of emotional information (fearful and neutral facial expressions). Carotid stimulation, compared with sham stimulation, broadly attenuated activity across cortical and brainstem regions. Modulation of emotional processing was apparent as a significant expression-by-stimulation interaction within left amygdala, where responses during appraisal of fearful faces were selectively reduced by carotid stimulation. Moreover, activity reductions within insula, amygdala, and hippocampus correlated with the degree of stimulation-evoked change in the explicit emotional ratings of fearful faces. Across participants, individual differences in autonomic state (heart rate variability, a proxy measure of autonomic balance toward parasympathetic activity) predicted the extent to which carotid stimulation influenced neural (amygdala) responses during appraisal and subjective rating of fearful faces. Together our results provide mechanistic insight into the visceral component of emotion by identifying the neural substrates mediating cardiovascular influences on the processing of fear signals, potentially implicating central baroreflex mechanisms for anxiolytic treatment targets