# RSEdit: Text-Guided Image Editing for Remote Sensing Source: [RSEdit: Text-Guided Image Editing for Remote Sensing]() Collection index: [llms.txt]() Data updated: 2026-05-20T05:00:20.896Z Authors: Chen Zhenyuan, Zhang Zechuan, Zhang Feng Published: 2026-03-14T02:22:02.000Z ## Abstract In this paper, we explore text-guided image editing in the remote sensing domain using generative modeling. We propose \\rsedit, a collection of models from U-Net to DiT with various configurations. Specifically, we present the first comprehensive study of conditioning strategies for building image editing models from off-the-shelf text-to-image ones. Our experiments show that \\rsedit achieves the best instruction-faithful edits while preserving geospatial structure. We release the code at \\url{https://github.com/Bili-Sakura/RSEdit-Preview} and checkpoints at \\url{https://huggingface.co/collections/BiliSakura/rsedit}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Change Generation on RSCC re-split (300 images) (test)](): Semantic Change Generation; RSCC re-split (300 images) (test); F1dam: 34.11. - [Text-Guided Image Editing on SECOND-CC (test)](): Text-Guided Image Editing; SECOND-CC (test); SC: 4.6. - [Text-Guided Image Editing on LEVIR-CC (test)](): Text-Guided Image Editing; LEVIR-CC (test); SC: 3.88. ## Original sources and implementation [Original paper]() [PDF]()