Lumos in the Night Sky: AI-Enabled Visual Tool for Exploring Night-Time Light Patterns

Jakob Hederich; Shreya Ghosh; Zeyu He; Prasenjit Mitra

doi:10.1007/978-3-031-43430-3_27

Lumos in the Night Sky: AI-Enabled Visual Tool for Exploring Night-Time Light Patterns

Jakob Hederich, Shreya Ghosh, Zeyu He, Prasenjit Mitra

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We introduce NightVIEW, an interactive tool for Night-time light (NTL) data visualization and analytics, which enables researchers and stakeholders to explore and analyze NTL data with a user-friendly platform. Powered by efficient system architecture, NightVIEW supports image segmentation, clustering, and change pattern detection to identify urban development and sprawl patterns. It captures temporal trends of NTL and semantics of cities, answering questions about demographic factors, city boundaries, and unusual differences.

Original language	English (US)
Title of host publication	Machine Learning and Knowledge Discovery in Databases
Subtitle of host publication	Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings
Editors	Gianmarco De Francisci Morales, Francesco Bonchi, Claudia Perlich, Natali Ruchansky, Nicolas Kourtellis, Elena Baralis
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	340-344
Number of pages	5
ISBN (Print)	9783031434297
DOIs	https://doi.org/10.1007/978-3-031-43430-3_27
State	Published - 2023
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 - Turin, Italy Duration: Sep 18 2023 → Sep 22 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14175 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023
Country/Territory	Italy
City	Turin
Period	9/18/23 → 9/22/23

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-43430-3_27

Cite this

Hederich, J., Ghosh, S., He, Z., & Mitra, P. (2023). Lumos in the Night Sky: AI-Enabled Visual Tool for Exploring Night-Time Light Patterns. In G. De Francisci Morales, F. Bonchi, C. Perlich, N. Ruchansky, N. Kourtellis, & E. Baralis (Eds.), Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings (pp. 340-344). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14175 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43430-3_27

Hederich, Jakob ; Ghosh, Shreya ; He, Zeyu et al. / Lumos in the Night Sky : AI-Enabled Visual Tool for Exploring Night-Time Light Patterns. Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. editor / Gianmarco De Francisci Morales ; Francesco Bonchi ; Claudia Perlich ; Natali Ruchansky ; Nicolas Kourtellis ; Elena Baralis. Springer Science and Business Media Deutschland GmbH, 2023. pp. 340-344 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We introduce NightVIEW, an interactive tool for Night-time light (NTL) data visualization and analytics, which enables researchers and stakeholders to explore and analyze NTL data with a user-friendly platform. Powered by efficient system architecture, NightVIEW supports image segmentation, clustering, and change pattern detection to identify urban development and sprawl patterns. It captures temporal trends of NTL and semantics of cities, answering questions about demographic factors, city boundaries, and unusual differences.",

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Hederich, J, Ghosh, S, He, Z & Mitra, P 2023, Lumos in the Night Sky: AI-Enabled Visual Tool for Exploring Night-Time Light Patterns. in G De Francisci Morales, F Bonchi, C Perlich, N Ruchansky, N Kourtellis & E Baralis (eds), Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14175 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 340-344, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023, Turin, Italy, 9/18/23. https://doi.org/10.1007/978-3-031-43430-3_27

Lumos in the Night Sky: AI-Enabled Visual Tool for Exploring Night-Time Light Patterns. / Hederich, Jakob; Ghosh, Shreya; He, Zeyu et al.
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. ed. / Gianmarco De Francisci Morales; Francesco Bonchi; Claudia Perlich; Natali Ruchansky; Nicolas Kourtellis; Elena Baralis. Springer Science and Business Media Deutschland GmbH, 2023. p. 340-344 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14175 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Hederich J, Ghosh S, He Z, Mitra P. Lumos in the Night Sky: AI-Enabled Visual Tool for Exploring Night-Time Light Patterns. In De Francisci Morales G, Bonchi F, Perlich C, Ruchansky N, Kourtellis N, Baralis E, editors, Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 340-344. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43430-3_27