3 citations · 9 across the 18 of their papers we have counts for
5 papers · 1 filter
Genesis: A Generative Engine for Hierarchical Satellite Image Synthesis
Subash Khanal, Yangzhi Cui, Daniel Cher +4
Earth observation is fundamentally multi-scale; geospatial tasks span varied resolutions, and satellite imagery is organized into cascading tile pyramids that nest fine detail with…
The first global agricultural field boundary map at 10m resolution
Caleb Robinson, Gedeon Muhawenayo, Subash Khanal +9
The agricultural field is the natural unit at which crops are planted, managed, regulated, and reported, yet most global remote-sensing products for agriculture are only available…
PRUE: A Practical Recipe for Field Boundary Segmentation at Scale
Gedeon Muhawenayo, Caleb Robinson, Subash Khanal +10
Large-scale maps of field boundaries are essential for agricultural monitoring tasks. Existing deep learning approaches for satellite-based field mapping are sensitive to illuminat…
TerraDiT: Point-Conditioned Diffusion Transformer for Satellite Image Synthesis
Srikumar Sastry, Dan Cher, Brian Wei +4
We introduce TerraDiT, a diffusion transformer designed for text-to-satellite image generation with point-based control. Existing controlled satellite image generative models often…
SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images
Aayush Dhakal, Subash Khanal, Srikumar Sastry +4
The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state…