3 citations · 9 across the 18 of their papers we have counts for
19 papers
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…
ProM3E: Probabilistic Masked MultiModal Embedding Model for Ecology
Srikumar Sastry, Subash Khanal, Aayush Dhakal +4
We introduce ProM3E, a probabilistic masked multimodal embedding model for any-to-any generation of multimodal representations for ecology. ProM3E is based on masked modality recon…
Global and Local Entailment Learning for Natural World Imagery
Srikumar Sastry, Aayush Dhakal, Eric Xing +2
Learning the hierarchical structure of data in vision-language models is a significant challenge. Previous works have attempted to address this challenge by employing entailment le…
Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping
Subash Khanal, Srikumar Sastry, Aayush Dhakal +3
We present Sat2Sound, a unified multimodal framework for geospatial soundscape understanding, designed to predict and map the distribution of sounds across the Earth's surface. Exi…
RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings
Aayush Dhakal, Srikumar Sastry, Subash Khanal +3
The choice of representation for geographic location significantly impacts the accuracy of models for a broad range of geospatial tasks, including fine-grained species classificati…
TaxaBind: A Unified Embedding Space for Ecological Applications
Srikumar Sastry, Subash Khanal, Aayush Dhakal +2
We present TaxaBind, a unified embedding space for characterizing any species of interest. TaxaBind is a multimodal embedding space across six modalities: ground-level images of sp…
PSM: Learning Probabilistic Embeddings for Multi-scale Zero-Shot Soundscape Mapping
Subash Khanal, Eric Xing, Srikumar Sastry +4
A soundscape is defined by the acoustic environment a person perceives at a location. In this work, we propose a framework for mapping soundscapes across the Earth. Since soundscap…
Mixed-View Panorama Synthesis using Geospatially Guided Diffusion
Zhexiao Xiong, Xin Xing, Scott Workman +2
We introduce the task of mixed-view panorama synthesis, where the goal is to synthesize a novel panorama given a small set of input panoramas and a satellite image of the area. Thi…
GEOBIND: Binding Text, Image, and Audio through Satellite Images
Aayush Dhakal, Subash Khanal, Srikumar Sastry +2
In remote sensing, we are interested in modeling various modalities for some geographic location. Several works have focused on learning the relationship between a location and typ…
GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis
Srikumar Sastry, Subash Khanal, Aayush Dhakal +1
We present GeoSynth, a model for synthesizing satellite images with global style and image-driven layout control. The global style control is via textual prompts or geographic loca…
LD-SDM: Language-Driven Hierarchical Species Distribution Modeling
Srikumar Sastry, Xin Xing, Aayush Dhakal +3
We focus on species distribution modeling using global-scale presence-only data, leveraging geographical and environmental features to map species ranges, as in previous studies. H…
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping
Srikumar Sastry, Subash Khanal, Aayush Dhakal +2
We propose a metadata-aware self-supervised learning~(SSL)~framework useful for fine-grained classification and ecological mapping of bird species around the world. Our framework u…
Learning Tri-modal Embeddings for Zero-Shot Soundscape Mapping
Subash Khanal, Srikumar Sastry, Aayush Dhakal +1
We focus on the task of soundscape mapping, which involves predicting the most probable sounds that could be perceived at a particular geographic location. We utilise recent state-…
Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images
Aayush Dhakal, Adeel Ahmad, Subash Khanal +3
We propose a weakly supervised approach for creating maps using free-form textual descriptions. We refer to this work of creating textual maps as zero-shot mapping. Prior works hav…
Causality for Inherently Explainable Transformers: CAT-XPLAIN
Subash Khanal, Benjamin Brodie, Xin Xing +2
There have been several post-hoc explanation approaches developed to explain pre-trained black-box neural networks. However, there is still a gap in research efforts toward designi…