Generation of 3D City Models Using Domain-Specific Information Fusion

Linking Semantic 3D City Models with Domain-Specific Simulation Tools for the Planning and Validation of Energy Applications at District Level

Sustainability ◽

10.3390/su13168782 ◽

2021 ◽

Vol 13 (16) ◽

pp. 8782

Author(s):

Edmund Widl ◽

Giorgio Agugiaro ◽

Jan Peters-Anders

Keyword(s):

Simulation Models ◽

Data Access ◽

Operating Conditions ◽

Data Modelling ◽

Gas Network ◽

Simulation Tools ◽

3D City Models ◽

Energy Applications ◽

Domain Specific ◽

City Models

Worldwide, cities are nowadays formulating their own sustainability goals, including ambitious targets related to the generation and consumption of energy. In order to support decision makers in reaching these goals, energy experts typically rely on simulation models of urban energy systems, which provide a cheap and efficient way to analyze potential solutions. The availability of high-quality, well-formatted and semantically structured data is a crucial prerequisite for such simulation-based assessments. Unfortunately, best practices for data modelling are rarely utilized in the context of energy-related simulations, so data management and data access often become tedious and cumbersome tasks. However, with the steady progress of digitalization, more and more spatial and semantic city data also become available and accessible. This paper addresses the challenge to represent these data in a way that ensures simulation tools can make use of them in an efficient and user-friendly way. Requirements for an effective linking of semantic 3D city models with domain-specific simulation tools are presented and discussed. Based on these requirements, a software prototype implementing the required functionality has been developed on top of the CityGML standard. This prototype has been applied to a simple yet realistic use case, which combines data from various sources to analyze the operating conditions of a gas network in a city district. The aim of the presented approach is to foster a stronger collaboration between experts for urban data modelling and energy simulations, based on a concrete proof-of-concept implementation that may serve as an inspiration for future developments.

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Assessment of the photovoltaic potential at urban level based on 3D city models: A case study and new methodological approach

Solar Energy ◽

10.1016/j.solener.2017.02.043 ◽

2017 ◽

Vol 146 ◽

pp. 264-275 ◽

Cited By ~ 43

Author(s):

Laura Romero Rodríguez ◽

Eric Duminil ◽

José Sánchez Ramos ◽

Ursula Eicker

Keyword(s):

Methodological Approach ◽

3D City Models ◽

Urban Level ◽

City Models

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3D city models for urban farming site identification in buildings

Computers Environment and Urban Systems ◽

10.1016/j.compenvurbsys.2020.101584 ◽

2021 ◽

Vol 86 ◽

pp. 101584

Author(s):

Ankit Palliwal ◽

Shuang Song ◽

Hugh Tiang Wah Tan ◽

Filip Biljecki

Keyword(s):

Urban Farming ◽

3D City Models ◽

Site Identification ◽

City Models

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From consistency to flexibility: A simplified database schema for the management of CityJSON 3D city models

Transactions in GIS ◽

10.1111/tgis.12807 ◽

2021 ◽

Author(s):

Gilles‐Antoine Nys ◽

Roland Billen

Keyword(s):

Database Schema ◽

3D City Models ◽

City Models

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Knowledge Enhanced LSTM for Coreference Resolution on Biomedical Texts

Bioinformatics ◽

10.1093/bioinformatics/btab153 ◽

2021 ◽

Author(s):

Yufei Li ◽

Xiaoyong Ma ◽

Xiangyu Zhou ◽

Pengzhen Cheng ◽

Kai He ◽

...

Keyword(s):

Information Integration ◽

Short Term Memory ◽

Superior Performance ◽

Supplementary Information ◽

Specific Information ◽

Coreference Resolution ◽

Fine Grained ◽

Domain Specific ◽

Memory Network ◽

Biomedical Texts

Abstract Motivation Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events’ attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. Results In this paper, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences. Supplementary information Supplementary data are available at Bioinformatics online.

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Generating 3D City Models from Open LiDAR Point Clouds: Advancing Towards Smart City Applications

Open Source Geospatial Science for Urban Studies - Lecture Notes in Intelligent Transportation and Infrastructure ◽

10.1007/978-3-030-58232-6_6 ◽

2020 ◽

pp. 97-116

Author(s):

Sebastián Ortega ◽

José Miguel Santana ◽

Jochen Wendel ◽

Agustín Trujillo ◽

Syed Monjur Murshed

Keyword(s):

Smart City ◽

Point Clouds ◽

3D City Models ◽

City Models

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PROTEIN STRUCTURE ALIGNMENT AND FAST SIMILARITY SEARCH USING LOCAL SHAPE SIGNATURES

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720004000533 ◽

2004 ◽

Vol 02 (01) ◽

pp. 215-239 ◽

Cited By ~ 4

Author(s):

TOLGA CAN ◽

YUAN-FANG WANG

Keyword(s):

Protein Structure ◽

Protein Structures ◽

Structure Alignment ◽

Protein Structure Alignment ◽

Specific Information ◽

Alignment Algorithm ◽

Screening Process ◽

Domain Specific ◽

Local Sequence ◽

Shape Signatures

We present a new method for conducting protein structure similarity searches, which improves on the efficiency of some existing techniques. Our method is grounded in the theory of differential geometry on 3D space curve matching. We generate shape signatures for proteins that are invariant, localized, robust, compact, and biologically meaningful. The invariancy of the shape signatures allows us to improve similarity searching efficiency by adopting a hierarchical coarse-to-fine strategy. We index the shape signatures using an efficient hashing-based technique. With the help of this technique we screen out unlikely candidates and perform detailed pairwise alignments only for a small number of candidates that survive the screening process. Contrary to other hashing based techniques, our technique employs domain specific information (not just geometric information) in constructing the hash key, and hence, is more tuned to the domain of biology. Furthermore, the invariancy, localization, and compactness of the shape signatures allow us to utilize a well-known local sequence alignment algorithm for aligning two protein structures. One measure of the efficacy of the proposed technique is that we were able to perform structure alignment queries 36 times faster (on the average) than a well-known method while keeping the quality of the query results at an approximately similar level.

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