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Genesis

A Generative Engine for Hierarchical Satellite Image Synthesis — ACM SIGSPATIAL 2026 (Oral).

Code

Genesis: sparse seed tiles → full multi-scale quadtree

Genesis completes a sparse set of seed satellite tiles into a full, zoomable Web-Mercator quadtree — filling every scale and location. Two flow-matching JiT operators do the work: a parent-conditional super-resolution model (one 256×256 parent tile → the 512×512 mosaic of its four children, one zoom level down) and a mask-based outpainting model (256×256 completion under quadrant masks). Composed into a pyramid engine, they bring the seeds to a common working level, greedily outpaint to complete that level, super-resolve down to the leaf tiles, and fill the coarser levels by downsampling — yielding a pyramid that is consistent across scales and honors every seed. Models are trained on the Git-10M global tile corpus.

Subash Khanal, Yangzhi Cui, Daniel Cher, Eric Xing, Brian Wei, Srikumar Sastry, Nathan Jacobs — Washington University in St. Louis.

Checkpoints

All models sample with 50 flow-matching steps, cfg = 1.0 (the paper eval setting) and load EMA weights. Main runs are trained to 800k steps. The main SR models are DINOv3-conditioned (+LPIPS); the main outpainting models are no-DINO.

Path	Model / arch	Size	Train steps	What it's for
`main/superresolution/main_B16/sr-full-tile-stage3-step0800000.ckpt`	SR · JiT-B/16	3.3 GB	800k	Main SR (DINOv3 + LPIPS), 256→512
`main/superresolution/main_H16/sr-full-tile-stage3-step0800000.ckpt`	SR · JiT-H/16	23 GB	800k	Main SR, largest model (quick-start default)
`main/outpainting/main_B16/op-new-stage3-step0800000.ckpt`	OP · JiT-B/16	3.6 GB	800k	Main outpainting (no-DINO, quadrant masks)
`main/outpainting/main_H16/op-new-stage3-step0800000.ckpt`	OP · JiT-H/16	26 GB	800k	Main outpainting, largest model (quick-start default)
`shared/git10m_quad_meta.json`	metadata	1.7 GB	—	Git-10M split metadata (paper eval)
`shared/hierarchy_index_quad.pkl`	metadata	0.6 GB	—	Parent↔child quadtree index (paper eval)
`dinov3_vitl16_pretrain_sat493m-eadcf0ff.pth`	DINOv3 ViT-L/16 (SAT-493M)	1.2 GB	—	Frozen conditioning encoder for SR

The ablations/ folder holds additional checkpoints used only to reproduce the paper's ablation table (see the code repo's docs/EVALUATION.md).

Quick start

Install the code repo (checkpoints download from this HF repo automatically):

bash

git clone https://github.com/mvrl/genesis && cd genesis
uv sync && source .venv/bin/activate    # Python 3.13, torch 2.8 cu128

Then run one SR ×2 pass and one quadrant-outpainting pass on a live Esri World Imagery tile (a Bavarian village amid fields and forest, at zoom 16, serves as the dummy input — the script fetches it over the network). The JiT-H/16 checkpoints total ~49 GB on first download; swap H16→B16 in the two checkpoint paths for a lighter 7 GB variant:

python

import os, sys
sys.path.insert(0, "src"); sys.path.insert(0, "demos")

from huggingface_hub import hf_hub_download
from PIL import Image
from utils.genesis_common import (fetch_tile_at_zoom, inference_device,
                                  load_sr_denoiser, load_op_denoiser,
                                  outpaint_context_white_holes_preview,
                                  run_superresolution, run_outpainting)

device = inference_device()                       # cuda if available, else cpu
out = "logs/example_sr_op"; os.makedirs(out, exist_ok=True)

sr_ckpt = hf_hub_download("MVRL/genesis", "main/superresolution/main_H16/sr-full-tile-stage3-step0800000.ckpt")
op_ckpt = hf_hub_download("MVRL/genesis", "main/outpainting/main_H16/op-new-stage3-step0800000.ckpt")
dino    = hf_hub_download("MVRL/genesis", "dinov3_vitl16_pretrain_sat493m-eadcf0ff.pth")

tile = fetch_tile_at_zoom(lon=12.9062, lat=48.6529, zoom=16)   # a real 256×256 tile (Bavarian village)
tile.save(f"{out}/01_original_tile.png")

# SR: z16 parent → 512×512 mosaic of its four z17 children.
# Showcase config = the paper's pyramid-engine SR settings (cfg 2.5 + bicubic
# warm start); the engine reads `cfg_scale`, so set the attribute (not args.cfg).
tile.save(f"{out}/02_sr_input.png")
tile.resize((512, 512), Image.BICUBIC).save(f"{out}/03_sr_bicubic_512.png")  # baseline SR starts from
sr_model, _ = load_sr_denoiser(sr_ckpt, "JiT-H/16", dino_weights=dino, device=device)
sr_model.cfg_scale = 2.5
sr_model.args.bicubic_init = True
sr_model.args.bicubic_init_t_start = 0.4
run_superresolution(sr_model, tile, device, target_zoom=17).save(f"{out}/04_sr_output.png")

# OP: keep the upper-left quadrant real, outpaint the other three (white = hole)
mask = Image.new("L", (256, 256), 255); mask.paste(0, (0, 0, 128, 128))
op_model, _ = load_op_denoiser(op_ckpt, "JiT-H/16", dino_weights="", device=device)
outpaint_context_white_holes_preview(tile, mask).save(f"{out}/05_op_masked_input.png")
run_outpainting(op_model, tile, mask, device, tile_zoom=16).save(f"{out}/06_op_output.png")

Every input and output lands in logs/example_sr_op/, numbered in pipeline order — original tile, SR input / bicubic baseline / SR output, masked OP input, outpainted OP output. The SR pass technically starts from the bicubic upsample (the warm start anchors on it), so comparing 03_sr_bicubic_512.png against 04_sr_output.png shows exactly what the model adds beyond plain interpolation.

Or as a one-liner, using the copy shipped in the code repo — demos/example_sr_op.py:

python demos/example_sr_op.py

Live imagery: Esri World Imagery — Source: Esri, Maxar, Earthstar Geographics, and the GIS User Community.

Demos

Interactive Gradio apps live in the code repo — see demos/README.md. The main one is the full-pyramid builder (demos/app_pyramid_unified.py): click a satellite map to drop sparse seed tiles and watch Genesis complete an entire 4-level quadtree live, tile-by-tile, with a stitched-PNG download. Companion apps cover single-tile 256→512 SR, quadrant outpainting, a large lateral-outpainting grid, a deep 6-level pyramid, and no-model interpolation baselines.

Reproduce the paper

One command per paper table — see docs/EVALUATION.md in the code repo: bash src/eval/run_all_sr.sh · run_all_op.sh · run_all_ablation.sh · run_all_dense500.sh, each with the paper-exact defaults baked in.

Citation

bibtex

@inproceedings{khanal2026genesis,
  title     = {{Genesis}: A Generative Engine for Hierarchical Satellite Image Synthesis},
  author    = {Khanal, Subash and Cui, Yangzhi and Cher, Daniel and Xing, Eric and
               Wei, Brian and Sastry, Srikumar and Jacobs, Nathan},
  booktitle = {ACM SIGSPATIAL International Conference on Advances in
               Geographic Information Systems (SIGSPATIAL)},
  year      = {2026},
}

License & acknowledgments

The Genesis code and checkpoints are released under the MIT License (see the code repo). Both Genesis operators build on JiT — we thank the authors for releasing their codebase.

dinov3_vitl16_pretrain_sat493m-eadcf0ff.pth is the satellite-pretrained (SAT-493M) ViT-L/16 encoder from Meta AI's DINOv3 release, redistributed here unmodified for convenience; it remains governed by Meta's DINOv3 license, not MIT. At inference the SR model loads it via torch.hub.load('facebookresearch/dinov3', 'dinov3_vitl16', weights=...).

Training data comes from the Git-10M global satellite-tile corpus. Demo/example imagery is fetched live from Esri World Imagery (attribution above).