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Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
[article]
2020
arXiv
pre-print
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Specifically, our proposed model is designed as a spatial-temporal network with a meta-learning paradigm. ...
The meta-learning paradigm learns a well-generalized initialization of the spatial-temporal network, which can be effectively adapted to target cities. ...
ACKNOWLEDGMENTS We thank Xiuwen Yi for feedback on an early draft of this paper. The work was supported in part by NSF awards #1652525, #1618448, and #1639150. ...
arXiv:1901.08518v3
fatcat:7zklfbdhtrbaznovztimoac66u
CityNet: A Multi-city Multi-modal Dataset for Smart City Applications
[article]
2021
arXiv
pre-print
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In addition, to facilitate the use of CityNet, we carry out extensive machine learning experiments, including spatio-temporal predictions, transfer learning, and reinforcement learning. ...
In this paper, we present CityNet, a multi-modal urban dataset containing data from 7 cities, each of which coming from 3 data sources. ...
In this paper, we present CityNet, a dataset with data from multiple cities and sources for smart city applications. ...
arXiv:2106.15802v1
fatcat:xwhu6pnkerbspewywxcaix26yu
Transforming Future Cities: Smart City
2022
Electronics
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The primitive elements of city transformation include the integration of urban infrastructure and artificial intelligence and cutting edge IoT technologies [...] ...
An autoencoder was adopted to learn the water pattern, which naturally tries to learn the identity function and makes predictions of future patterns. ...
Data from sensors and public places can be exploited to study and conduct assessment analysis-'big data' and smart civic-analytics for cities is just around the corner, with a major transformation for ...
doi:10.3390/electronics11101534
fatcat:4kxqi7gytfgvlam6ziielcnlke
Artificial Intelligence Applications to Smart City and Smart Enterprise
2020
Applied Sciences
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Smart cities work under a more resource-efficient management and economy than ordinary cities. ...
Published works refer to the following areas of interest: vehicular traffic prediction; social big data analysis; smart city management; driving and routing; localization; and safety, health, and life ...
More specifically, different machine-learning approaches are combined to obtain a meta-machine-learning model that further aids in maximizing prediction accuracy. ...
doi:10.3390/app10082944
fatcat:rbz3kszunzfyvmleusroxm7hhy
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling
[article]
2022
arXiv
pre-print
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Concretely, CcFTL transfers the relational knowledge from multiple rich-data source cities to the target city. ...
To address the above challenging problems, we propose a novel Cross-city Federated Transfer Learning framework (CcFTL) to cope with the data insufficiency and privacy problems. ...
. • MAML [15] : a competitive and classic meta-learning method, which learns initialization parameters from multiple tasks based on a source city. • Meta-MaxUp [16] : A meta-learning approach that explores ...
arXiv:2206.00007v1
fatcat:fwvyabxm3bavpmrcebameds3ui
The Futures of the City Region
2009
Regional studies
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Just as there has not been a single dominant paradigm in the past for city regions -regional science competed with the descriptive approach of Patrick Geddes, for example -there is clearly not one paradigm ...
This type of governance learning is emergent, evolving from the collaboration of a wide variety of interests (INNES et al., 1994; HEALEY, 2006) . ...
doi:10.1080/00343400903037511
fatcat:64fvkowyffevrct63q3izzmj4y
An Anthropocentric and Enhanced Predictive Approach to Smart City Management
2021
Smart Cities
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To achieve this, multiple data sources about a city were gradually connected to a message broker, that enables increasingly rich decision-support. ...
Results show that it is possible to predict future states of a city, in aspects such as traffic, air pollution, and other ambient variables. ...
Acknowledgments: This work was supported by FCT-Fundação para a Ciência e a Tecnologia, through project UIDB/04728/2020.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/smartcities4040072
fatcat:kcalfur6fndo5j5xgik2d5vp6y
Disaster Management in Smart Cities
2021
Smart Cities
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The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. ...
The city of Lisbon (Portugal) is used as a case to show the practical application of the approach. ...
The limited area models (LAMs) are NWP models at high spatial-temporal resolution providing a reaction time that goes from 3 to 6 h up to 72 h, while precipitation nowcasting provides a reaction time between ...
doi:10.3390/smartcities4020042
fatcat:bisk5r6i7vfljlju63wacqxe7q
Semantic Trails of City Explorations: How Do We Live a City
[article]
2019
arXiv
pre-print
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We finally present an application using these datasets to build a recommender system meant to guide tourists while exploring a city. ...
In this paper, we fill the gaps in defining what is a semantic trail of city exploration and how it can be generated by integrating different data sources. ...
In order to analyze the check-ins of the two datasets from a spatial point of view, we considered the distributions of the number of check-ins for each city. ...
arXiv:1812.04367v2
fatcat:2cg2ffwx6je4hlgsnc2afccnze
III. Domain-Specific Narratives of Change
[chapter]
2020
The Redundant City
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Approaching Pruitt-Igoe today, for the purpose of learning from our past mistakes, the initial question would need extending: What can we learn from the failure and what can we learn from the myth of Pruitt-Igoe ...
Filling the spatial and temporal gap (German 'Lücke') of a redevelopment site, the intervention provided a platform for planned and spontaneous encounters, discussions and play. ...
doi:10.14361/9783839451144-004
fatcat:tos4sxpk3zcshdg7aevy7tsuea
CityFlow: Exploiting Edge Computing for Large Scale Smart City Applications
2019
2019 IEEE International Conference on Big Data and Smart Computing (BigComp)
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This paper presents an approach to supporting the development process for large-scale smart city applications that leverage edge computing resources. ...
For the evaluation, a lab-based setup and a real world deployment were executed and are presented. ...
We assume that the covariate shift exists in the city, as the distribution of the data from the city often varies significantly in spatial and in temporal domains, namely, a spatial-temporal covariate ...
doi:10.1109/bigcomp.2019.8679234
dblp:conf/bigcomp/GiangLKYNLB19
fatcat:mudo4ppl5bfujj6wg3jmdlsv6y
Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2009.09379v1 fatcat:yrjdxzgxrnbf3foeteg3yjr5aeOther Versions
The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent deep learning approaches ...
spatial and temporal factors, aiming to make different application-driven approaches comparable; (ii) we design a spatio-temporal meta-model, called STMeta, which can flexibly integrate generalizable ...
Guideline 3: Our proposed STMeta can be used as a meta-model to integrate multiple temporal and spatial knowledge. ...
arXiv:2009.09379v3
fatcat:f3qzl6rpbvezre2gswfwa55fyi
Sensors and Actuators in Smart Cities
2018
Journal of Sensor and Actuator Networks
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Acknowledgments: This publication was made possible thanks to NPRPGrant #[6-1508-2-616] from the Qatar National Research Fund (a member of the Qatar Foundation). ...
Author Contributions: Alex Adim Obinikpo and Burak Kantarci conceived and pursued the literature survey on deep learning techniques on big sensed data for smart health applications, reviewed the state ...
) and (ii) related segments (spatial prediction). ...
doi:10.3390/jsan7010008
fatcat:pt7nkf4oaraijkmsndohahqtnq
Sensing places' life to make city smarter
2012
Proceedings of the ACM SIGKDD International Workshop on Urban Computing - UrbComp '12
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Indeed, in this context, Spatial Data Infrastructure plays an important role and acts as an enabling platform linking governments authoritative spatial information with crowd sourced, voluntary information ...
Categories and Subject Descriptors -Urban sensing and city dynamics sensing -Smart recommendations in urban spaces See also ...
as an indivisible whole, as a single coherent and predictable unit. ...
doi:10.1145/2346496.2346503
dblp:conf/kdd/RocheR12
fatcat:zl5me372czfadkftwwtudjxoue
Space–time series clustering: Algorithms, taxonomy, and case study on urban smart cities
2020
Engineering applications of artificial intelligence
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We have divided the existing approaches into three main categories depending on the type of clustering results. ...
A B S T R A C T This paper provides a short overview of space-time series clustering, which can be generally grouped into three main categories such as: hierarchical, partitioning-based, and overlapping ...
Multiple and heterogeneous space-time series are first created from the spatio temporal data. ...
doi:10.1016/j.engappai.2020.103857
fatcat:oojlhynaq5c53jsipnximndvl4
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