3 citations · 9 across the 22 of their papers we have counts for
25 papers
Not Where You Think: Protecting Location Privacy Against Reasoning-Based Geolocation
Shanghao Shi, Jian Xiao, Hao Li +5
Recent large reasoning models can infer where ordinary photos were taken using only visual information, creating a serious location privacy risk even when metadata and obvious geog…
Chronosphere: Space-Time Tessellation of Local Climate Experts
Daniel Cher, Eric Xing, Kexing Li +3
We introduce Chronosphere, a spatio-temporal neural field that learns representations of climate. A central challenge in geographic representation learning is modeling environmenta…
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…
Trajectory-aware Cross-view Geo-localization with Sequential Observations
Tianyi Gao, Jiayu Lin, Danielle Beaulieu +1
Cross-view geo-localization matches ground-level observations against geo-tagged satellite imagery. Recent methods show that sequential queries such as video clips yield richer spa…
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…
Tessellating The Earth
Daniel Cher, Hamza Iqbal, Eric Xing +2
Geolocation encoders, which map geographic coordinates to learned representations, are emerging as an effective means of capturing visual and non-visual characteristics from a lati…
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…
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…
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…
MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas
Feng Qiao, Zhexiao Xiong, Xinge Zhu +3
Omnidirectional depth estimation presents a significant challenge due to the inherent distortions in panoramic images. Despite notable advancements, the impact of projection method…
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…
Eroding Trust In Aerial Imagery: Comprehensive Analysis and Evaluation Of Adversarial Attacks In Geospatial Systems
Michael Lanier, Aayush Dhakal, Zhexiao Xiong +3
In critical operations where aerial imagery plays an essential role, the integrity and trustworthiness of data are paramount. The emergence of adversarial attacks, particularly tho…
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…
A Partially Supervised Reinforcement Learning Framework for Visual Active Search
Anindya Sarkar, Nathan Jacobs, Yevgeniy Vorobeychik
Visual active search (VAS) has been proposed as a modeling framework in which visual cues are used to guide exploration, with the goal of identifying regions of interest in a large…
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…