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Multi-scale object detection in remote sensing imagery with convolutional neural networks , journal = ISPRSJ, volume = · 2018 · ISPRS Journal of Photogrammetry and Remote Sensing · DOI 10.1016/j.isprsjprs.2018.04.003

1 Pith paper cite this work, alongside 515 external citations. Polarity classification is still indexing.

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515 external citations · OpenAlex
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fields

cs.LG 1

years

2026 1

verdicts

REJECT 1

representative citing papers

Physics-Informed Super-Resolution of Atmospheric Data

cs.LG · 2026-07-21 · reject · novelty 5.0

Adding multi-scale hydrostatic-primitive-equation losses to atmospheric super-resolution models improves reported physical-consistency scores and some reconstruction/event-detection metrics, but the metric and constraints depend on ground-truth latent fields.

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Showing 1 of 1 citing paper.

  • Physics-Informed Super-Resolution of Atmospheric Data cs.LG · 2026-07-21 · reject · none · ref 25

    Adding multi-scale hydrostatic-primitive-equation losses to atmospheric super-resolution models improves reported physical-consistency scores and some reconstruction/event-detection metrics, but the metric and constraints depend on ground-truth latent fields.