The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning
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“Vehicles can also be detected in high-resolution satellite images with high accuracy [177,390,566,747], and image counts can serve to estimate average vehicle traffic [405].”
From Tackling climate change with machine learning (2023) · cites this paper as [566]
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Metadata
- Authors
- Mundhenk, T. N., Konjevod, G., Sakla, W. A., & Boakye, K.
- Affiliations
- Lawrence Livermore National Laboratory (US)
- Corresponding author
- Mundhenk, T. Nathan
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 785-800
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2520447284
- Cited by
- 386
- References
- 25
- Citations by year
- 2026: 16 · 2025: 45 · 2024: 48 · 2023: 44 · 2022: 45 · 2021: 62 · 2020: 48 · 2019: 45 · 2018: 21 · 2017: 11 · 2016: 1
- FWCI
- 9.02
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Lawrence Livermore National Laboratory
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- car detection, car classification, residual learning
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