Seamless Satellite-image Synthesis

• URL: http://arxiv.org/abs/2111.03384v1
• Date: Fri, 5 Nov 2021 10:42:24 GMT
• Title: Seamless Satellite-image Synthesis
• Authors: Jialin Zhu and Tom Kelly
• Abstract summary: While 2D data is cheap and easily, accurate satellite imagery is expensive and often unavailable or out of date date. Our approach seamless textures over arbitrarily extents which are consistent through scale-space.
• Score: 1.3401746329218014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Seamless Satellite-image Synthesis (SSS), a novel neural architecture to create scale-and-space continuous satellite textures from cartographic data. While 2D map data is cheap and easily synthesized, accurate satellite imagery is expensive and often unavailable or out of date. Our approach generates seamless textures over arbitrarily large spatial extents which are consistent through scale-space. To overcome tile size limitations in image-to-image translation approaches, SSS learns to remove seams between tiled images in a semantically meaningful manner. Scale-space continuity is achieved by a hierarchy of networks conditioned on style and cartographic data. Our qualitative and quantitative evaluations show that our system improves over the state-of-the-art in several key areas. We show applications to texturing procedurally generation maps and interactive satellite image manipulation.

Related papers

• Using Texture to Classify Forests Separately from Vegetation [0.0]
  This paper presents an initial proposal for a static, algorithmic process to identify forest regions in satellite image data. With strong initial results, this paper also identifies the next steps to improve the accuracy of the classification and verification processes.
  arXiv  Detail & Related papers  (2024-05-01T00:48:55Z)
• Self-Supervision in Time for Satellite Images(S3-TSS): A novel method of SSL technique in Satellite images [0.38366697175402226]
  We propose S3-TSS, a novel method of self-supervised learning technique that leverages natural augmentation occurring in temporal dimension. Our method was able to perform better than baseline SeCo in four downstream datasets.
  arXiv  Detail & Related papers  (2024-03-07T19:16:17Z)
• Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion [77.34078223594686]
  We propose a novel architecture for direct 3D scene generation by introducing diffusion models into 3D sparse representations and combining them with neural rendering techniques. Specifically, our approach generates texture colors at the point level for a given geometry using a 3D diffusion model first, which is then transformed into a scene representation in a feed-forward manner. Experiments in two city-scale datasets show that our model demonstrates proficiency in generating photo-realistic street-view image sequences and cross-view urban scenes from satellite imagery.
  arXiv  Detail & Related papers  (2024-01-19T16:15:37Z)
• DiffusionSat: A Generative Foundation Model for Satellite Imagery [63.2807119794691]
  We present DiffusionSat, to date the largest generative foundation model trained on a collection of publicly available large, high-resolution remote sensing datasets. Our method produces realistic samples and can be used to solve multiple generative tasks including temporal generation, superresolution given multi-spectral inputs and in-painting.
  arXiv  Detail & Related papers  (2023-12-06T16:53:17Z)
• SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation [68.42476385214785]
  We propose a novel Spatial-Semantic Map Guided (SSMG) diffusion model that adopts the feature map, derived from the layout, as guidance. SSMG achieves superior generation quality with sufficient spatial and semantic controllability compared to previous works. We also propose the Relation-Sensitive Attention (RSA) and Location-Sensitive Attention (LSA) mechanisms.
  arXiv  Detail & Related papers  (2023-08-20T04:09:12Z)
• Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs [32.4349978810128]
  This paper aims to develop an accurate 3D geometry representation of satellite images using satellite-ground image pairs. We draw inspiration from the density field representation used in volumetric neural rendering and propose a new approach, called Sat2Density. Our method utilizes the properties of ground-view panoramas for the sky and non-sky regions to learn faithful density fields of 3D scenes in a geometric perspective.
  arXiv  Detail & Related papers  (2023-03-26T10:15:33Z)
• Dual Pyramid Generative Adversarial Networks for Semantic Image Synthesis [94.76988562653845]
  The goal of semantic image synthesis is to generate photo-realistic images from semantic label maps. Current state-of-the-art approaches, however, still struggle to generate realistic objects in images at various scales. We propose a Dual Pyramid Generative Adversarial Network (DP-GAN) that learns the conditioning of spatially-adaptive normalization blocks at all scales jointly.
  arXiv  Detail & Related papers  (2022-10-08T18:45:44Z)
• Convolutional Neural Processes for Inpainting Satellite Images [56.032183666893246]
  Inpainting involves predicting what is missing based on the known pixels and is an old problem in image processing. We show ConvvNPs can outperform classical methods and state-of-the-art deep learning inpainting models on a scanline inpainting problem for LANDSAT 7 satellite images.
  arXiv  Detail & Related papers  (2022-05-24T23:29:04Z)
• Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching [102.39635336450262]
  We address the problem of ground-to-satellite image geo-localization by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image.
  arXiv  Detail & Related papers  (2022-03-26T20:10:38Z)
• Boundary Regularized Building Footprint Extraction From Satellite Images Using Deep Neural Network [6.371173732947292]
  We propose a novel deep neural network, which enables to jointly detect building instance and regularize noisy building boundary shapes from a single satellite imagery. Our model can accomplish multi-tasks of object localization, recognition, semantic labelling and geometric shape extraction simultaneously.
  arXiv  Detail & Related papers  (2020-06-23T17:24:09Z)

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.