268,279 research outputs found
Describing Scholarly Works with Dublin Core: A Functional Approach
This article describes the development of the Scholarly Works Application Profile (SWAP)—a Dublin Core application profile for describing scholarly texts. This work provides an active illustration of the Dublin Core Metadata Initiative (DCMI) “Singapore Framework” for Application Profiles, presented at the DCMI Conference in 2007, by incorporating the various elements of Application Profile building as defined by this framework—functional requirements, domain model, description set profile, usage guidelines, and data format. These elements build on the foundations laid down by the Dublin Core Abstract Model and utilize a preexisting domain model (FR-BR—Functional Requirements for Bibliographic Records) in order to support the representation of complex data describing multiple entities and their relationships. The challenges of engaging community acceptance and implementation will be covered, along with other related initiatives to support the growing corpus of scholarly resource types, such as data objects, geographic data, multimedia, and images whose structure and metadata requirements introduce the need for new application profiles. Finally, looking to other initiatives, the article will comment on how Dublin Core relates to the broader scholarly information world, where projects like Object Re-use and Exchange are attempting to better equip repositories to exchange resources
A Sparse Voxel Octree-Based Framework for Computing Solar Radiation Using 3D City Models
abstract: An effective three-dimensional (3D) data representation is required to assess the spatial distribution of the photovoltaic potential over urban building roofs and facades using 3D city models. Voxels have long been used as a spatial data representation, but practical applications of the voxel representation have been limited compared with rasters in traditional two-dimensional (2D) geographic information systems (GIS). We propose to use sparse voxel octree (SVO) as a data representation to extend the GRASS GIS r.sun solar radiation model from 2D to 3D. The GRASS GIS r.sun model is nested in an SVO-based computing framework. The presented 3D solar radiation computing framework was applied to 3D building groups of different geometric complexities to demonstrate its efficiency and scalability. We presented a method to explicitly compute diffuse shading losses in r.sun, and found that diffuse shading losses can reduce up to 10% of the annual global radiation under clear sky conditions. Hence, diffuse shading losses are of significant importance especially in complex urban environments
Scalable Self-Supervised Representation Learning from Spatiotemporal Motion Trajectories for Multimodal Computer Vision
Self-supervised representation learning techniques utilize large datasets
without semantic annotations to learn meaningful, universal features that can
be conveniently transferred to solve a wide variety of downstream supervised
tasks. In this work, we propose a self-supervised method for learning
representations of geographic locations from unlabeled GPS trajectories to
solve downstream geospatial computer vision tasks. Tiles resulting from a
raster representation of the earth's surface are modeled as nodes on a graph or
pixels of an image. GPS trajectories are modeled as allowed Markovian paths on
these nodes. A scalable and distributed algorithm is presented to compute
image-like representations, called reachability summaries, of the spatial
connectivity patterns between tiles and their neighbors implied by the observed
Markovian paths. A convolutional, contractive autoencoder is trained to learn
compressed representations, called reachability embeddings, of reachability
summaries for every tile. Reachability embeddings serve as task-agnostic,
feature representations of geographic locations. Using reachability embeddings
as pixel representations for five different downstream geospatial tasks, cast
as supervised semantic segmentation problems, we quantitatively demonstrate
that reachability embeddings are semantically meaningful representations and
result in 4-23% gain in performance, as measured using area under the
precision-recall curve (AUPRC) metric, when compared to baseline models that
use pixel representations that do not account for the spatial connectivity
between tiles. Reachability embeddings transform sequential, spatiotemporal
mobility data into semantically meaningful tensor representations that can be
combined with other sources of imagery and are designed to facilitate
multimodal learning in geospatial computer vision.Comment: Extended abstract accepted for presentation at BayLearn 2022. 3
pages, 2 figures, 1 table. Abstract based on IEEE MDM 2022 research track
paper: arXiv:2110.1252
Succinct Data Structures in the Realm of GIS
Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] Geographic Information Systems (GIS) have spread all over our technological environment in the last decade. The inclusion of GPS technologies in everyday portable devices along with the creation of massive shareable geographical data banks has boosted the rise of geoinformatics. Despite the technological maturity of this field, there are still relevant research challenges concerning efficient information storage and representation. One of the most powerful techniques to tackle these issues is designing new Succinct Data Structures (SDS). These structures are defined by three main characteristics: they use a compact representation of the data, they have self-index properties and, as a consequence, they do not need decompression to process the enclosed information. Thus, SDS are not only capable of storing geographical data using as little space as possible, but they can also solve queries efficiently without any previous decompression. This work introduces how SDS can be successfully applied in the GIS context through several novel approaches and practical use cases.This work is partially funded by the CITIC research center funded by Xunta/FEDER-UE 2014-2020 Program, ED431G 2019/01. MICINN(PGE/ERDF) [EXTRA-Compact: PID2020-114635RB-I00]Xunta de Galicia; ED431G 2019/0
Mapping municipal solid waste to boost circular valorization practices in Łódzkie
ABSTRACT: Geographic Information System (GIS) is a powerful instrument that can be used for the spatial representation of waste and by-product flows at various levels, allowing to improve municipal solid waste (MSW) management. The mapping obtained can be advantageously targeted to build a regional network of technological, economic, social and environmental linkages and to boost circular economy practices. In this work, the data on MSW produced in the Łódzkie region, Poland, during 2021 were used to generate a geolocalized database and an interactive web map, using ArcGIS software. The geodatabase and the map visualization were organized in three layers of information with increasing detail to foster a map-driven symbiosis between waste suppliers and waste recipients, paving the way for a more circular regional economy.info:eu-repo/semantics/publishedVersio
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
The modelling, analysis, and visualisation of dynamic geospatial phenomena
has been identified as a key developmental challenge for next-generation
Geographic Information Systems (GIS). In this context, the envisaged
paradigmatic extensions to contemporary foundational GIS technology raises
fundamental questions concerning the ontological, formal representational, and
(analytical) computational methods that would underlie their spatial
information theoretic underpinnings.
We present the conceptual overview and architecture for the development of
high-level semantic and qualitative analytical capabilities for dynamic
geospatial domains. Building on formal methods in the areas of commonsense
reasoning, qualitative reasoning, spatial and temporal representation and
reasoning, reasoning about actions and change, and computational models of
narrative, we identify concrete theoretical and practical challenges that
accrue in the context of formal reasoning about `space, events, actions, and
change'. With this as a basis, and within the backdrop of an illustrated
scenario involving the spatio-temporal dynamics of urban narratives, we address
specific problems and solutions techniques chiefly involving `qualitative
abstraction', `data integration and spatial consistency', and `practical
geospatial abduction'. From a broad topical viewpoint, we propose that
next-generation dynamic GIS technology demands a transdisciplinary scientific
perspective that brings together Geography, Artificial Intelligence, and
Cognitive Science.
Keywords: artificial intelligence; cognitive systems; human-computer
interaction; geographic information systems; spatio-temporal dynamics;
computational models of narrative; geospatial analysis; geospatial modelling;
ontology; qualitative spatial modelling and reasoning; spatial assistance
systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964);
Special Issue on: Geospatial Monitoring and Modelling of Environmental
Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press
Quantitative Perspectives on Fifty Years of the Journal of the History of Biology
Journal of the History of Biology provides a fifty-year long record for
examining the evolution of the history of biology as a scholarly discipline. In
this paper, we present a new dataset and preliminary quantitative analysis of
the thematic content of JHB from the perspectives of geography, organisms, and
thematic fields. The geographic diversity of authors whose work appears in JHB
has increased steadily since 1968, but the geographic coverage of the content
of JHB articles remains strongly lopsided toward the United States, United
Kingdom, and western Europe and has diversified much less dramatically over
time. The taxonomic diversity of organisms discussed in JHB increased steadily
between 1968 and the late 1990s but declined in later years, mirroring broader
patterns of diversification previously reported in the biomedical research
literature. Finally, we used a combination of topic modeling and nonlinear
dimensionality reduction techniques to develop a model of multi-article fields
within JHB. We found evidence for directional changes in the representation of
fields on multiple scales. The diversity of JHB with regard to the
representation of thematic fields has increased overall, with most of that
diversification occurring in recent years. Drawing on the dataset generated in
the course of this analysis, as well as web services in the emerging digital
history and philosophy of science ecosystem, we have developed an interactive
web platform for exploring the content of JHB, and we provide a brief overview
of the platform in this article. As a whole, the data and analyses presented
here provide a starting-place for further critical reflection on the evolution
of the history of biology over the past half-century.Comment: 45 pages, 14 figures, 4 table
Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa
Neural networks are capable of learning rich, nonlinear feature
representations shown to be beneficial in many predictive tasks. In this work,
we use these models to explore the use of geographical features in predicting
colorectal cancer survival curves for patients in the state of Iowa, spanning
the years 1989 to 2012. Specifically, we compare model performance using a
newly defined metric -- area between the curves (ABC) -- to assess (a) whether
survival curves can be reasonably predicted for colorectal cancer patients in
the state of Iowa, (b) whether geographical features improve predictive
performance, and (c) whether a simple binary representation or richer, spectral
clustering-based representation perform better. Our findings suggest that
survival curves can be reasonably estimated on average, with predictive
performance deviating at the five-year survival mark. We also find that
geographical features improve predictive performance, and that the best
performance is obtained using richer, spectral analysis-elicited features.Comment: 8 page
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