activity
20242026
most citedMulti-Agent Geospatial Copilots for Remote Sensing Workflows

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

MARS - A foundational map auto-regressor

Qi Zhang, Suvam Bag, Rupanjali Kukal +9

Map generation tasks feature extensive non-structural vectorized data (e.g., points, polylines, and polygons) and thus pose significant challenges to common pixel-wise generative m…

cs.LG2026

Rollback-Free Stable Brick Structures Generation

Chenhui Xu, Ziyue Bai, Fuxun Yu +2

While autoregressive models have advanced 3D generation, creating physically stable brick structures remains a challenge due to the strict requirements of gravity and interconnecti…

cs.CV2025

Unlocking Zero-Shot Geospatial Reasoning via Indirect Rewards

Chenhui Xu, Fuxun Yu, Michael J. Bianco +15

Training robust reasoning vision-language models (VLMs) in rare domains (such as geospatial) is fundamentally constrained by supervision scarcity. While raw geospatial imagery is a…

cs.CV2025

Geospatial Foundational Embedder: Top-1 Winning Solution on EarthVision Embed2Scale Challenge (CVPR 2025)

Zirui Xu, Raphael Tang, Mike Bianco +4

EarthVision Embed2Scale challenge (CVPR 2025) aims to develop foundational geospatial models to embed SSL4EO-S12 hyperspectral geospatial data cubes into embedding vectors that fac…

cs.LG2025★ 1 cited

Multi-Agent Geospatial Copilots for Remote Sensing Workflows

Chaehong Lee, Varatheepan Paramanayakam, Andreas Karatzas +7

We present GeoLLM-Squad, a geospatial Copilot that introduces the novel multi-agent paradigm to remote sensing (RS) workflows. Unlike existing single-agent approaches that rely on…

cs.CV2024

Online Learning via Memory: Retrieval-Augmented Detector Adaptation

Yanan Jian, Fuxun Yu, Qi Zhang +3

This paper presents a novel way of online adapting any off-the-shelf object detection model to a novel domain without retraining the detector model. Inspired by how humans quickly…

cs.DC2024

LLM-dCache: Improving Tool-Augmented LLMs with GPT-Driven Localized Data Caching

Simranjit Singh, Michael Fore, Andreas Karatzas +6

As Large Language Models (LLMs) broaden their capabilities to manage thousands of API calls, they are confronted with complex data operations across vast datasets with significant…