Diffusion Models for Earth Observation Use-cases: from cloud removal to
urban change detection
- URL: http://arxiv.org/abs/2311.06222v1
- Date: Fri, 10 Nov 2023 18:24:25 GMT
- Title: Diffusion Models for Earth Observation Use-cases: from cloud removal to
urban change detection
- Authors: Fulvio Sanguigni, Mikolaj Czerkawski, Lorenzo Papa, Irene Amerini,
Bertrand Le Saux
- Abstract summary: This work proposes and analyses three use cases which demonstrate the potential of diffusion-based approaches for satellite image data.
Namely, we tackle cloud removal and inpainting, dataset generation for change-detection tasks, and urban replanning.
- Score: 31.572818380518914
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The advancements in the state of the art of generative Artificial
Intelligence (AI) brought by diffusion models can be highly beneficial in novel
contexts involving Earth observation data. After introducing this new family of
generative models, this work proposes and analyses three use cases which
demonstrate the potential of diffusion-based approaches for satellite image
data. Namely, we tackle cloud removal and inpainting, dataset generation for
change-detection tasks, and urban replanning.
Related papers
- Diffusion Models in Low-Level Vision: A Survey [82.77962165415153]
diffusion model-based solutions have emerged as widely acclaimed for their ability to produce samples of superior quality and diversity.
We present three generic diffusion modeling frameworks and explore their correlations with other deep generative models.
We summarize extended diffusion models applied in other tasks, including medical, remote sensing, and video scenarios.
arXiv Detail & Related papers (2024-06-17T01:49:27Z) - Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models [0.0]
This study investigates the application of deep-learning diffusion models for the super-resolution of weather data.
We present a methodology for transforming low-resolution weather data into high-resolution outputs.
arXiv Detail & Related papers (2024-06-06T14:15:12Z) - Urban Air Pollution Forecasting: a Machine Learning Approach leveraging Satellite Observations and Meteorological Forecasts [0.11249583407496218]
Air pollution poses a significant threat to public health and well-being, particularly in urban areas.
This study introduces a series of machine-learning models that integrate data from the Sentinel-5P satellite, meteorological conditions, and topological characteristics to forecast future levels of five major pollutants.
arXiv Detail & Related papers (2024-05-30T10:02:53Z) - SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation [69.42764583465508]
We explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks.
To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation.
arXiv Detail & Related papers (2024-03-25T10:30:22Z) - DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
Current perceptive models heavily depend on resource-intensive datasets.
We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability.
Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
arXiv Detail & Related papers (2024-03-20T04:58:03Z) - Reference-Free Isotropic 3D EM Reconstruction using Diffusion Models [8.590026259176806]
We propose a diffusion-model-based framework that overcomes the limitations of requiring reference data or prior knowledge about the degradation process.
Our approach utilizes 2D diffusion models to consistently reconstruct 3D volumes and is well-suited for highly downsampled data.
arXiv Detail & Related papers (2023-08-03T07:57:02Z) - Deceptive-NeRF/3DGS: Diffusion-Generated Pseudo-Observations for High-Quality Sparse-View Reconstruction [60.52716381465063]
We introduce Deceptive-NeRF/3DGS to enhance sparse-view reconstruction with only a limited set of input images.
Specifically, we propose a deceptive diffusion model turning noisy images rendered from few-view reconstructions into high-quality pseudo-observations.
Our system progressively incorporates diffusion-generated pseudo-observations into the training image sets, ultimately densifying the sparse input observations by 5 to 10 times.
arXiv Detail & Related papers (2023-05-24T14:00:32Z) - A Comparative Study on Generative Models for High Resolution Solar
Observation Imaging [59.372588316558826]
This work investigates capabilities of current state-of-the-art generative models to accurately capture the data distribution behind observed solar activity states.
Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts.
arXiv Detail & Related papers (2023-04-14T14:40:32Z) - Multimodal learning-based inversion models for the space-time
reconstruction of satellite-derived geophysical fields [40.33123267556167]
A variety of satellite sensors deliver observation data with different sampling patterns due satellite orbits and/or their sensitivity to atmospheric conditions.
Here, we investigate how end-to-end learning schemes provide new means to address multimodal inversion problems.
We show how this scheme can successfully extract relevant information from satellite-derived sea surface temperature images and enhance the reconstruction of sea surface currents issued from satellite altimetry data.
arXiv Detail & Related papers (2022-03-20T20:37:03Z) - Toward Foundation Models for Earth Monitoring: Proposal for a Climate
Change Benchmark [95.19070157520633]
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.
Such models, recently coined as foundation models, have been transformational to the field of natural language processing.
We propose to develop a new benchmark comprised of a variety of downstream tasks related to climate change.
arXiv Detail & Related papers (2021-12-01T15:38:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.