6,167 research outputs found
Modeling structural change in spatial system dynamics: A Daisyworld example
System dynamics (SD) is an effective approach for helping reveal the temporal
behavior of complex systems. Although there have been recent developments in
expanding SD to include systems' spatial dependencies, most applications have
been restricted to the simulation of diffusion processes; this is especially
true for models on structural change (e.g. LULC modeling). To address this
shortcoming, a Python program is proposed to tightly couple SD software to a
Geographic Information System (GIS). The approach provides the required
capacities for handling bidirectional and synchronized interactions of
operations between SD and GIS. In order to illustrate the concept and the
techniques proposed for simulating structural changes, a fictitious environment
called Daisyworld has been recreated in a spatial system dynamics (SSD)
environment. The comparison of spatial and non-spatial simulations emphasizes
the importance of considering spatio-temporal feedbacks. Finally, practical
applications of structural change models in agriculture and disaster management
are proposed
Image databases: Problems and perspectives
With the increasing number of computer graphics, image processing, and pattern recognition applications, economical storage, efficient representation and manipulation, and powerful and flexible query languages for retrieval of image data are of paramount importance. These and related issues pertinent to image data bases are examined
LiveSketch: Query Perturbations for Guided Sketch-based Visual Search
LiveSketch is a novel algorithm for searching large image collections using
hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch
search by creating visual suggestions that augment the query as it is drawn,
making query specification an iterative rather than one-shot process that helps
disambiguate users' search intent. Our technical contributions are: a triplet
convnet architecture that incorporates an RNN based variational autoencoder to
search for images using vector (stroke-based) queries; real-time clustering to
identify likely search intents (and so, targets within the search embedding);
and the use of backpropagation from those targets to perturb the input stroke
sequence, so suggesting alterations to the query in order to guide the search.
We show improvements in accuracy and time-to-task over contemporary baselines
using a 67M image corpus.Comment: Accepted to CVPR 201
A framework for utility data integration in the UK
In this paper we investigate various factors which prevent utility knowledge from being
fully exploited and suggest that integration techniques can be applied to improve the
quality of utility records. The paper suggests a framework which supports knowledge
and data integration. The framework supports utility integration at two levels: the
schema and data level. Schema level integration ensures that a single, integrated geospatial
data set is available for utility enquiries. Data level integration improves utility data
quality by reducing inconsistency, duplication and conflicts. Moreover, the framework
is designed to preserve autonomy and distribution of utility data. The ultimate aim of
the research is to produce an integrated representation of underground utility infrastructure
in order to gain more accurate knowledge of the buried services. It is hoped that
this approach will enable us to understand various problems associated with utility data,
and to suggest some potential techniques for resolving them
Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
Computational complexity has been the bottleneck of applying physically-based
simulations on large urban areas with high spatial resolution for efficient and
systematic flooding analyses and risk assessments. To address this issue of
long computational time, this paper proposes that the prediction of maximum
water depth rasters can be considered as an image-to-image translation problem
where the results are generated from input elevation rasters using the
information learned from data rather than by conducting simulations, which can
significantly accelerate the prediction process. The proposed approach was
implemented by a deep convolutional neural network trained on flood simulation
data of 18 designed hyetographs on three selected catchments. Multiple tests
with both designed and real rainfall events were performed and the results show
that the flood predictions by neural network uses only 0.5 % of time comparing
with physically-based approaches, with promising accuracy and ability of
generalizations. The proposed neural network can also potentially be applied to
different but relevant problems including flood predictions for urban layout
planning
Republishing OpenStreetMapâs roads as linked routable tiles
Route planning providers manually integrate different geo-spatial datasets before offering a Web service to developers, thus creating a closed world view. In contrast, combining open datasets at runtime can provide more information for user-specific route planning needs. For example, an extra dataset of bike sharing availabilities may provide more relevant information to the occasional cyclist. A strategy for automating the adoption of open geo-spatial datasets is needed to allow an ecosystem of route planners able to answer more specific and complex queries. This raises new challenges such as (i) how open geo-spatial datasets should be published on the Web to raise interoperability, and (ii) how route planners can discover and integrate relevant data for a certain query on the fly. We republished OpenStreetMap's road network as "Routable Tiles" to facilitate its integration into open route planners. To achieve this, we use a Linked Data strategy and follow an approach similar to vector tiles. In a demo, we show how client-side code can automatically discover tiles and perform a shortest path algorithm. We provide four contributions: (i) we launched an open geo-spatial dataset that is available for everyone to reuse at no cost, (ii) we published a Linked Data version of the OpenStreetMap ontology, (iii) we introduced a hypermedia specification for vector tiles that extends the Hydra ontology, and (iv) we released the mapping scripts, demo and routing scripts as open source software
An analysis of the use of graphics for information retrieval
Several research groups have addressed the problem of retrieving vector graphics. This work has, however, focused either on domain-dependent areas or was based on very simple graphics languages. Here we take a fresh look at the issue of graphics retrieval in general and in particular at the tasks which retrieval systems must support. The paper presents a series of case studies which explored the needs of professionals in the hope that these needs can help direct future graphics IR research. Suggested modelling techniques for some of the graphic collections are also presented
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