RSEdit: Text-Guided Image Editing for Remote Sensing
Paper • 2603.13708 • Published
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Server error while post-processing the rows. This occured on row 12. Please report the issue.
Error code: TransformRowsProcessingError
Exception: TypeError
Message: Image cell must be a PIL image or an encoded dict of an image, but got {'bytes': None, 'path': 'zip://benchmark_results_ablations/rsedit-dit-channel-concat/generation_config.json::/tmp/hf-datasets-cache/medium/datasets/90683946293470-config-parquet-and-info-BiliSakura-RSEdit-Benchma-2f1cb8e6/hub/datasets--BiliSakura--RSEdit-Benchmark-Results/snapshots/cb1ef45e7cbb14544...
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/rows_utils.py", line 37, in _transform_row
transformed_row[featureName] = get_cell_value(
^^^^^^^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/viewer_utils/features.py", line 427, in get_cell_value
return image(
^^^^^^
File "/src/libs/libcommon/src/libcommon/viewer_utils/features.py", line 117, in image
raise TypeError(
TypeError: Image cell must be a PIL image or an encoded dict of an image, but got {'bytes': None, 'path': 'zip://benchmark_results_ablations/rsedit-dit-channel-concat/generation_config.json::/tmp/hf-datasets-cache/medium/datasets/90683946293470-config-parquet-and-info-BiliSakura-RSEdit-Benchma-2f1cb8e6/hub/datasets--BiliSakura--RSEdit-Benchmark-Results/snapshots/cb1ef45e7cbb14544...Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
RSCC-RSEdit-Test-Split
Project Page | Paper | GitHub
This repository contains the test split for RSEdit, a unified framework designed for text-guided image editing in the remote sensing (RS) domain.
Description
RSEdit adapts pre-trained text-to-image diffusion models (including U-Net and DiT architectures) into instruction-following editors for Earth observation imagery. This dataset consists of bi-temporal remote sensing image pairs and corresponding textual instructions. It is used to evaluate the model's ability to perform precise, physically coherent edits—such as simulating disaster impacts, urban growth, and seasonal shifts—while maintaining geospatial consistency and orthographic constraints.
The full framework was trained on over 60,000 semantically rich bi-temporal image pairs to learn these complex spatial and temporal priors.
Citation
If you find this dataset or the RSEdit framework useful, please cite the following work:
@misc{zhenyuan2026rsedittextguidedimageediting,
title={RSEdit: Text-Guided Image Editing for Remote Sensing},
author={Chen Zhenyuan and Zhang Zechuan and Zhang Feng},
year={2026},
eprint={2603.13708},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2603.13708},
}
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