[Ieee_vis] CFP: LOD 2021 - Special Session on Data Science for Sustainable Cities - Deadline April 29

[Ludovico Montalcini]

[Apologies if you receive multiple copies of this announcement.]

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Special Session on Data Science for Sustainable Cities
@ 7th International Online & Onsite Conference on Machine Learning,
Optimization & Data Science (LOD 2021)
October 5-8, 2021
Grasmere, Lake District, England - UK
https://lod2021.icas.cc/special-sessions/

Conference post-proceedings: Springer-Nature Lecture Notes in Computer Science
(LNCS)

A selection of the best papers will be invited to submit an extended
version for publication on
Frontiers in Sustainable Cities - Data Analysis and Data-Driven Modeling for
Smart Cities
https://www.frontiersin.org/research-topics/17634/data-analysis-and-data-driven-modeling-for-smart-cities
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We invite submissions of papers, abstracts, posters, talks and demos
on all topics
related to the special session.


** Important Dates **
* Paper Submission Deadline: **Thursday April 29, 2021** (Anywhere on Earth)
* Abstract/Poster/Demo Submission Deadline: **Sunday May 9, 2021**
(Anywhere on Earth)
* Reviews Released to Authors: by Tuesday June 15, 2021
* Rebuttal Due: by Tuesday June 22, 2021
* Decision Notification to Authors: by Wednesday June 30, 2021
* Camera Ready Submission Deadline: by Saturday July 10, 2021



** Aims and Scope **
The amount of data generated nowadays by society, city
infrastructures, and digital
technologies around us is astonishing. The analysis, modeling and knowledge
extraction of/from these data is a key asset for understanding urban
environments and improving the efficiency of urban mobility, air quality
and other forms of sustainability. This special session provides a platform
to share high-quality research ideas related to data science methods and
technologies for urban environments, a topic of crucial
importance for many Sustainable Development Goals (i.e., SDG 7 on
Sustainable Energy and SDG 11 on Sustainable Cities and Communities). Another
important goal is to establish a meeting point for researchers in academia
and industry who develop methodologies and technologies for data science,
machine learning and artificial intelligence with specific applications in
smart and sustainable cities.


** Topics **
- Data acquisition and data analysis in sustainable cities
- Data-driven predictive modeling for urban and built environments
- Time series analysis and forecasting for urban environments
- Anomaly detection for multivariate sensor data
- Robust machine learning and model verification
- ICT platforms for collecting, visualizing and analyzing data in urban
environments
- Data analysis for mobility and transportation
- Data analysis for air quality monitoring
- Data analysis for heating management
- Data analysis for smart buildings and smart grids
- Model explainability and interpretability in urban applications
- Predictive modeling for district heating networks
- Predictive modeling for energy-efficient cities
- Innovative sensing platforms (e.g., mobile sensors) for data gathering
- Data gathering and management for citizen science in urban environments
- Data security, privacy and blockchain
- Analytics for municipalities and urban stakeholders
- Cloud and big data platforms
- Smart hospitals and healthcare for sustainable cities
- Data-driven modeling for urban complex systems
- Data analytics for emergency management
- Urban Analytics
- Information diffusion and social networks for sustainable cities
- Epidemic data analysis in urban environments
- City monitoring and Urban planning
- Senseable cities
- Analytics for smart growth and effective infrastructure


** Post-proceedings of selected and revised papers **
Conference post-proceedings: Springer-Nature Lecture Notes in Computer Science
(LNCS)


** Selection of best papers **A selection of the best papers accepted to the
special session will be invited
to submit an extended version for publication in Frontiers in Sustainable
Cities
Research Topic: Data Analysis and Data-Driven Modeling for Smart Cities
https://www.frontiersin.org/research-topics/17634/data-analysis-and-data-driven-modeling-for-smart-cities


** Paper submission and format **
All papers must be submitted using EasyChair.
Any questions regarding the submission process can be sent to
conference organizers:
lod at icas.cc

Please prepare your paper in English using the Springer Nature - Lecture Notes
in Computer Science (LNCS) template, available on the conference website.
Papers must be submitted in PDF.

Types of Submission:
- long paper: original novel and unpublished work (max. 12 pages in
Springer LNCS format);
- short paper: an extended abstract of novel work (max. 4 pages);
- work for oral presentation only (no page restriction; any format). For
example, work already published elsewhere, which is relevant and which may
solicit fruitful discussion at the conference;
- abstract for poster presentation only (max 2 pages; any format). The
poster format for the presentation is A0 (118.9 cm high and 84.1 cm wide,
respectively 46.8 x 33.1 inch). For research work which is relevant and
which may solicit fruitful discussion at the conference.

Each paper submitted will be rigorously evaluated. The evaluation will
ensure the high interest and expertise of reviewers. Following the
tradition of LOD, we expect high-quality papers in terms of their
scientific contribution, rigor, correctness, novelty, clarity, quality of
presentation and reproducibility of experiments.
Accepted papers must contain significant novel results. Results can be either
theoretical or empirical. Results will be judged on the degree to which
they have been objectively established and/or their potential for scientific
and technological impact.

It is also possible to present the talk virtually (Zoom).

** Special session organizers **
Alberto Castellini (Universita' di  Verona, Italy)
Alessandro Farinelli (Universita' di  Verona, Italy)
Giuseppe Nicosia (Universita' di  Catania, Italy)
Varun Ojha (University of Reading, United Kingdom)

https://easychair.org/conferences/?conf=lod2021
https://lod2021.icas.cc
lod at icas.cc
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