ArticleiScience2023
High-quality super-resolution mapping using spatial deep learning.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- GeoAI: Beyond mapping earth and cities through explainability, adaptability, and sustainability.iScience · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Super-resolution mapping (SRM) is a critical technology in remote sensing. Recently, several deep learning models have been developed for SRM. Most of these models, however, only use a single stream to process remote sensing images and mainly focus on capturing spectral features. This can undermine the quality of the resulting maps. To address this issue, we propose a soft information-constrained network (SCNet) for SRM that leverages spatial transition features represented by soft information as a spatial prior. Our network incorporates a separate branch to process prior spatial features for feature enhancement. SCNet can extract multi-level feature representations simultaneously from both remote sensing images and prior soft information and hierarchically incorporate features from soft information into image features. Experimental results on three datasets demonstrate that SCNet generates more complete spatial details in complex areas, providing an effective means for producing high-quality and high-resolution mapping products from remote sensing images.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.