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Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps

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arxiv 2308.16648 v1 pith:I2PZGZSR submitted 2023-08-31 cs.CV cs.LG

classification cs.CVcs.LG
keywords imagemodelssatelliteconditioneddiffusiongenerategenerationimages
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Despite recent advancements in image generation, diffusion models still remain largely underexplored in Earth Observation. In this paper we show that state-of-the-art pretrained diffusion models can be conditioned on cartographic data to generate realistic satellite images. We provide two large datasets of paired OpenStreetMap images and satellite views over the region of Mainland Scotland and the Central Belt. We train a ControlNet model and qualitatively evaluate the results, demonstrating that both image quality and map fidelity are possible. Finally, we provide some insights on the opportunities and challenges of applying these models for remote sensing. Our model weights and code for creating the dataset are publicly available at https://github.com/miquel-espinosa/map-sat.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multimodal LLM-Guided Semantic Correction in Text-to-Image Diffusion

    cs.CV 2025-05 conditional novelty 7.0 of 10

    PPAD injects MLLM semantic feedback into diffusion denoising via lookahead sketches and ping-pong-ahead resampling, improving text-to-image alignment.

  2. DiffRIS: Enhancing Referring Remote Sensing Image Segmentation with Pre-trained Text-to-Image Diffusion Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    DiffRIS combines frozen Stable Diffusion and CLIP encoders with a new text adapter and decoder to set a new state-of-the-art mean IoU on three referring remote sensing image segmentation benchmarks.

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