3 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation
Hannah Kerner, Snehal Chaudhari, Aninda Ghosh +9
Crop field boundaries are foundational datasets for agricultural monitoring and assessments but are expensive to collect manually. Machine learning (ML) methods for automatically e…
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…
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…