3 citations · 8 across the 20 of their papers we have counts for
19 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…
TerraDiT-: Unified Spatial Control for Satellite Image Synthesis with Any Geospatial Primitive
Brian Wei, Srikumar Sastry, Daniel Cher +2
Generative models have achieved remarkable progress, yet applying them to satellite imagery remains challenging. Unlike natural imagery, satellite scenes are structured by spatiall…
Beyond Flat Labels: Level-Restricted Contrastive Learning for Hierarchical Fine-Grained Vision Classification
Zhiyuan Tao, Srikumar Sastry, Matthew J Thompson +9
Multimodal contrastive learning has enabled zero-shot visual classification by aligning images with textual categories. However, in hierarchically structured label spaces, existing…
DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments
Anindya Sarkar, Srikumar Sastry, Aleksis Pirinen +2
Visual active search (VAS) has been introduced as a modeling framework that leverages visual cues to direct aerial (e.g., UAV-based) exploration and pinpoint areas of interest with…
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…
VectorSynth: Fine-Grained Satellite Image Synthesis with Structured Semantics
Daniel Cher, Brian Wei, Srikumar Sastry +1
We introduce VectorSynth, a diffusion-based framework for pixel-accurate satellite image synthesis conditioned on polygonal geographic annotations with semantic attributes. Unlike…
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…
GOMAA-Geo: GOal Modality Agnostic Active Geo-localization
Anindya Sarkar, Srikumar Sastry, Aleksis Pirinen +3
We consider the task of active geo-localization (AGL) in which an agent uses a sequence of visual cues observed during aerial navigation to find a target specified through multiple…
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…
Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning
Xin Xing, Zhexiao Xiong, Abby Stylianou +3
This paper presents a novel approach to Single-Positive Multi-label Learning. In general multi-label learning, a model learns to predict multiple labels or categories for a single…
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…