Genesis: A Generative Engine for Hierarchical Satellite Image Synthesis

• URL: http://arxiv.org/abs/2609.02683v1
• Date: Wed, 02 Sep 2026 14:53:21 GMT
• Title: Genesis: A Generative Engine for Hierarchical Satellite Image Synthesis
• Abstract summary: Genesis is a generative engine that brings both axes together by composing two specialized operators over the quadtree.<n>It propagates sparse seeds into seamless, multi-resolution maps from any initial configuration.<n> dense500 is a fully observed multi-scale pyramid dataset spanning diverse geographic regions.
• Score: 19.89638839762492
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Earth observation is fundamentally multi-scale; geospatial tasks span varied resolutions, and satellite imagery is organized into cascading tile pyramids that nest fine detail within wide coverage. Current generative models of satellite imagery, however, operate along a single axis: they either zoom to enhance a single tile's resolution or pan to extend imagery at a fixed scale. As a result, no existing method produces a complete pyramid that stays consistent across both scale and space, where a high-zoom tile must agree with the coarse context it refines and with the neighbors it meets. Motivated by this gap, we introduce a new task, multi-scale tile completion: given a sparse set of seed tiles at arbitrary zoom levels and positions, synthesize a complete, uniform quadtree that is globally consistent across both scale and space. We approach this task with Genesis, a generative engine that brings both axes together by composing two specialized operators over the quadtree: a vertical super-resolution model and a horizontal mask-based outpainting model, producing pyramids that are consistent across zoom levels and seamless across neighboring tiles. Each operator achieves state-of-the-art results on its subtask, and the engine propagates sparse seeds into seamless, multi-resolution maps from any initial configuration. To evaluate the task and benchmark Genesis, we introduce dense500, a fully observed multi-scale pyramid dataset spanning diverse geographic regions, together with a suite of pyramid-level metrics. Code, models, and our dataset are available at https://github.com/mvrl/genesis.

Related papers

• TerraDiT-$Ω$: Unified Spatial Control for Satellite Image Synthesis with Any Geospatial Primitive [19.699267665705715]
  We introduce TerraDiT-$$, a unified spatial control framework that generates satellite imagery directly from any native primitive.<n>Generative models have achieved remarkable progress, yet applying them to satellite imagery remains challenging.
  arXiv  Detail & Related papers  (2026-06-30T01:56:41Z)
• Beyond 2D Matching: A Unified Single-Stage Framework for Geometry-Aware Cross-View Object Geo-Localization [21.013795754583697]
  Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view within a geo-tagged reference image (e.g., satellite)<n>Existing approaches heavily rely on 2D appearance matching and are constrained by limited datasets lacking geometric metadata.<n>We first introduce dataset, a large-scale, high-fidelity building dataset comprising over 220,000 ground-satellite and drone-satellite pairs.<n>We propose a novel single-stage Geometry-Aware Geo-localization framework (GAGeo), built upon the permutation-equivariant 3D foundation model $
  arXiv  Detail & Related papers  (2026-06-29T17:19:49Z)
• MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling [33.18025936405946]
  We develop a generative foundation model MetaEarth-MM for multi-modal remote sensing imagery.<n>Our model organizes the generation around the underlying scene content.<n>It exhibits strong generative capability and robust generalization across diverse generation tasks.
  arXiv  Detail & Related papers  (2026-05-19T16:47:02Z)
• CylinderSplat: 3D Gaussian Splatting with Cylindrical Triplanes for Panoramic Novel View Synthesis [53.231427733457224]
  CylinderSplat is a feed-forward framework for panoramic 3DGS.<n>It achieves state-of-the-art results in both single-view and multi-view panoramic novel view synthesis.
  arXiv  Detail & Related papers  (2026-03-06T04:04:20Z)
• TerraGen: A Unified Multi-Task Layout Generation Framework for Remote Sensing Data Augmentation [20.17595840637294]
  Current generative data augmentation frameworks are task-isolated, i.e., each vision task requires training an independent generative model.<n>We propose textbfTerraGen, a unified layout-to-image generation framework that enables flexible, spatially controllable synthesis of remote sensing imagery.
  arXiv  Detail & Related papers  (2025-10-24T12:29:12Z)
• MeshMosaic: Scaling Artist Mesh Generation via Local-to-Global Assembly [62.48017648785026]
  We introduce MeshMosaic, a novel local-to-global framework for artist mesh generation that scales to over 100K triangles.<n>We show that MeshMosaic significantly outperforms state-of-the-art methods in both geometric fidelity and user preference.
  arXiv  Detail & Related papers  (2025-09-24T11:02:03Z)
• Focus on Neighbors and Know the Whole: Towards Consistent Dense Multiview Text-to-Image Generator for 3D Creation [64.07560335451723]
  CoSER is a novel consistent dense Multiview Text-to-Image Generator for Text-to-3D. It achieves both efficiency and quality by meticulously learning neighbor-view coherence. It aggregates information along motion paths explicitly defined by physical principles to refine details.
  arXiv  Detail & Related papers  (2024-08-23T15:16:01Z)
• Mesh Denoising Transformer [104.5404564075393]
  Mesh denoising is aimed at removing noise from input meshes while preserving their feature structures. SurfaceFormer is a pioneering Transformer-based mesh denoising framework. New representation known as Local Surface Descriptor captures local geometric intricacies. Denoising Transformer module receives the multimodal information and achieves efficient global feature aggregation.
  arXiv  Detail & Related papers  (2024-05-10T15:27:43Z)
• 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)
• Point Cloud Upsampling via Disentangled Refinement [86.3641957163818]
  Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uniformity and proximity-to-surface. We formulate two cascaded sub-networks, a dense generator and a spatial refiner.
  arXiv  Detail & Related papers  (2021-06-09T02:58:42Z)
• Aligning Latent and Image Spaces to Connect the Unconnectable [22.90522514729253]
  We develop a method to generate infinite high-resolution images with diverse and complex content. It is based on a perfectly equivariant generator with synchronous equis in the image and latent spaces. We test the approach on LHQ, LSUN Tower and LSUN Bridge and outperform the baselines by at least 4 times.
  arXiv  Detail & Related papers  (2021-04-14T16:29:20Z)

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.