Publication2023
Resnet-2D-ConvLSTM: A Means to Extract Features from Hyperspectral Image
- Industry, Innovation and Infrastructure
- Life on Land
- Innovation, Science and Technology
The record
- Type
- Publication
- Date
- 1 January 2023
- Level
- National
- Journal
- Communications in Computer and Information Science
- Volume
- 1793 CCIS
- Pages
- 365-376
- Published
- 1 January 2023
- DOI
- 10.1007/978-981-99-1645-0_30
- Scopus ID
- 85161715862
- ISSN
- 18650929
- Authors
- 2
- Open access
- No
- Citations
- 5
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Resnet-2D-ConvLSTM architecture with batch normalization and shortcut connections for hyperspectral image feature extraction demonstrates innovation in deep learning methodology for high-dimensional remote sensing data analysis and classification.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
15 Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss
Hyperspectral imaging analysis improves accuracy for detecting and identifying various environmental and terrestrial objects, supporting land monitoring and biodiversity assessment applications.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3 Innovation, Science and Technology
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 9 label. Resnet-2D-ConvLSTM architecture with batch normalization and shortcut connections for hyperspectral image feature extraction demonstrates innovation in deep learning methodology for high-dimensional remote sensing data analysis and classification.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
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- 1
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Citing this record
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KIIT Deemed to be University. “Resnet-2D-ConvLSTM: A Means to Extract Features from Hyperspectral Image”. Kortex.