mlsanityData to 14 Oct 2026

UP-Diff: Latent Diffusion Model for Remote Sensing Urban Prediction

arXiv 2407.11578 ↗First posted 16 Jul 2024cs.CV eess.IVcited by 2

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

This study introduces a novel Remote Sensing (RS) Urban Prediction (UP) task focused on future urban planning, which aims to forecast urban layouts by utilizing information from existing urban layouts and planned change maps. To address the proposed RS UP task, we propose UP-Diff, which leverages a Latent Diffusion Model (LDM) to capture positionaware embeddings of pre-change urban layouts and planned change maps. In specific, the trainable cross-attention layers within UP-Diff's iterative diffusion modules enable the model to dynamically highlight crucial regions for targeted modifications. By utilizing our UP-Diff, designers can effectively refine and adjust future urban city plans by making modifications to the change maps in a dynamic and adaptive manner. Compared with conventional RS Change Detection (CD) methods, the proposed UP-Diff for the RS UP task avoids the requirement of paired prechange and post-change images, which enhances the practical usage in city development. Experimental results on LEVIRCD and SYSU-CD datasets show UP-Diff's ability to accurately predict future urban layouts with high fidelity, demonstrating its potential for urban planning. Code and model weights are available at https://github.com/zeyuwang-zju/UP-Diff.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 24 of its 42 references that are in the map. First posted on arXiv 16 Jul 2024.

Builds on

24 of 42 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives13 Apr
  2. 2023GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPM6 Jun

Other references · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  4. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
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  11. 2017Neural Discrete Representation Learningcited by 4,712
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10 more
  1. 2022A ConvNet for the 2020scited by 3,579
  2. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
  3. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  4. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,299
  5. 2023GLIGEN: Open-Set Grounded Text-to-Image Generationcited by 781
  6. 2021Remote Sensing Image Change Detection with Transformerscited by 306
  7. 2023LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generationcited by 259
  8. 2022SpaText: Spatio-Textual Representation for Controllable Image Generationcited by 217
  9. 2020DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite imagescited by 75
  10. 2024MirrorDiffusion: Stabilizing Diffusion Process in Zero-shot Image Translation by Prompts Redescription and Beyondcited by 4

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectivescited by 18
  2. 2025UrbanMind: Urban Dynamics Prediction with Multifaceted Spatial-Temporal Large Language Modelscited by 2

Similar, not cited

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  1. 2024Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning
  2. 2023Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection
  3. 2024ChangeAnywhere: Sample Generation for Remote Sensing Change Detection via Semantic Latent Diffusion Model
  4. 2025Object Fidelity Diffusion for Remote Sensing Image Generation
  5. 2025Generative AI for Urban Planning: Synthesizing Satellite Imagery via Diffusion Models
  6. 2024Remote Diffusion
  7. 2023RSDiff: Remote Sensing Image Generation from Text Using Diffusion Model
  8. 2022DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection