Search Results for author:
Found 8 papers, 4 papers with code
GEOBIND: Binding Text, Image, and Audio through Satellite Images
In this work, we present a deep-learning model, GeoBind, that can infer about multiple modalities, specifically text, image, and audio, from satellite imagery of a location.
GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis
We present GeoSynth, a model for synthesizing satellite images with global style and image-driven layout control.
LD-SDM: Language-Driven Hierarchical Species Distribution Modeling
Further, we propose a novel proximity-aware evaluation metric that enables evaluating species distribution models using any pixel-level representation of ground-truth species range map.
Eroding Trust In Aerial Imagery: Comprehensive Analysis and Evaluation Of Adversarial Attacks In Geospatial Systems
In critical operations where aerial imagery plays an essential role, the integrity and trustworthiness of data are paramount.
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping
We propose a metadata-aware self-supervised learning~(SSL)~framework useful for fine-grained classification and ecological mapping of bird species around the world.
FroSSL: Frobenius Norm Minimization for Efficient Multiview Self-Supervised Learning
However, these two approaches are difficult to combine due to the computational complexity of computing eigenvalues.
Learning Tri-modal Embeddings for Zero-Shot Soundscape Mapping
We focus on the task of soundscape mapping, which involves predicting the most probable sounds that could be perceived at a particular geographic location.
Ranked #1 on
Cross-Modal Retrieval
on SoundingEarth
(using extra training data)
Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images
For a given location and overhead image, our model predicts the expected CLIP embeddings of the ground-level scenery.
