Publication2024
Hybrid ResNet152-EML model for Geo-spatial image classification
The record
- Type
- Publication
- Date
- 1 February 2024
- Level
- National
- Journal
- International Journal of Information Technology Singapore
- Volume
- 16
- Issue
- 2
- Pages
- 659-673
- Published
- 1 February 2024
- DOI
- 10.1007/s41870-023-01534-x
- Scopus ID
- 85174062941
- ISSN
- 25112104
- Authors
- 3
- Open access
- No
- Citations
- 15
- International co-authorship
- No
- Co-author countries
- IN
Classification
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UN Sustainable Development Goals
9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Hybrid ResNet152-EML model achieves 99.83% accuracy in satellite image classification supporting earth observation and remote sensing technology.
- 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
Viksit Bharat @2047
vb-3.1 Research and development
Deep learning geo-spatial analysis advances research capacity for earth observation and multi-scale satellite imagery interpretation.
- 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. “Hybrid ResNet152-EML model for Geo-spatial image classification”. Kortex.