# A new public Alsat-2B dataset for single-image super-resolution

arXiv: https://arxiv.org/abs/2103.12547 · HTML: https://mlsanity.com/paper/2103.12547/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 21 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [Residual Feature Distillation Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2009.11551/) (2020)
- [Super-resolution of multispectral satellite images using convolutional neural networks](https://mlsanity.com/paper/2002.00580/) (2020)

## Built on by (most cited first, 3 of 3)

- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2403.11078/) (2024)
- [Text-RSIR: A Text-Guided Framework for Efficient Remote Sensing Image Transmission and Reconstruction](https://mlsanity.com/paper/2605.15558/) (2026)

## Abstract (arXiv)

Currently, when reliable training datasets are available, deep learning methods dominate the proposed solutions for image super-resolution. However, for remote sensing benchmarks, it is very expensive to obtain high spatial resolution images. Most of the super-resolution methods use down-sampling techniques to simulate low and high spatial resolution pairs and construct the training samples. To solve this issue, the paper introduces a novel public remote sensing dataset (Alsat2B) of low and high spatial resolution images (10m and 2.5m respectively) for the single-image super-resolution task. The high-resolution images are obtained through pan-sharpening. Besides, the performance of some super-resolution methods on the dataset is assessed based on common criteria. The obtained results reveal that the proposed scheme is promising and highlight the challenges in the dataset which shows the need for advanced methods to grasp the relationship between the low and high-resolution patches.