arXiv Computer Vision

GeoNLI - A Natural Language Interpreter for Satellite Imagery

GeoNLI introduces a unified, modular pipeline that combines advanced SAM variants with multimodal large language models to perform satellite image captioning, visual question answering (VQA), and visual grounding. The EarthMind model achieves strong results on captioning and VQA, while multiple RemoteSAM-SAM and DiffuSAM pipelines are used for grounding, ultimately employing a majority‑voting ensemble across several models. The system reports 82% captioning accuracy, 83.32% VQA accuracy, and 64.94% grounding accuracy, demonstrating improved consistency over task‑specific approaches.

arXiv Computer Vision

Sep 24

VLM2GeoVec: Toward Universal Multimodal Embeddings for Remote Sensing

The paper introduces RSMEB, a unified benchmark for remote‑sensing multimodal retrieval that evaluates both cross‑modal and interleaved retrieval across 21 tasks under a single ranking protocol. It also presents VLM2GeoVec, an instruction‑conditioned single‑encoder model that embeds image, text, bounding‑box, and geo‑coordinate tokens into one sequence and achieves state‑of‑the‑art performance on region‑caption, referring‑expression, and semantic geo‑aware retrieval while remaining competitive on conventional tasks. The authors provide code, checkpoints, and data on GitHub to facilitate reproducibility.

By Emanuel S\'anchez Aimar, Gulnaz Zhambulova, Fahad Shahbaz Khan, Yonghao Xu, Michael Felsberg

rag multimodal benchmarks

arXiv Machine Learning

Jul 20

More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe

arXiv:2607. 15942v1 Announce Type: cross Abstract: Remote sensing vision-language models are increasingly expected to support open-ended reasoning over Earth Observation data and a variety of tasks.

By Stefan Maria Ailuro (INSAIT, Sofia University "St. Kliment Ohridski"), Mario Markov (INSAIT, Sofia University "St. Kliment Ohridski"), Mohammad Mahdi (INSAIT, Sofia University "St. Kliment Ohridski"), Luc Van Gool (INSAIT, Sofia University "St. Kliment Ohridski"), Danda Pani Paudel (INSAIT, Sofia University "St. Kliment Ohridski")

llms computer-vision reinforcement-learning multimodal benchmarks safety

arXiv AI

Jun 10

Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks

arXiv:2606. 10819v1 Announce Type: cross Abstract: RS-MLLMs enable natural-language understanding and spatial reasoning over earth observation imagery.

By Miaoxin Cai, Guanqun Wang, Wei Zhang, Guangyao Zhou, Yin Zhuang, Tong Zhang, Hao Wang, He Chen, Jun Li

llms multimodal benchmarks safety

arXiv Computer Vision

Sep 24

A Unified Framework and Dataset for Oriented Object Visual Grounding in Remote Sensing

The paper introduces O$^2$-VG, a unified framework for oriented object visual grounding in remote sensing images, comprising three complementary models: O$^2$-VG-Trans, a cross‑modality transformer; O$^2$-VG-Uni, which predicts universal oriented proposals; and O$^2$-VG-VLM, an autoregressive vision‑language model that generates oriented bounding boxes. It also presents DIOR‑R‑SVG, a new dataset containing image, expression, and oriented box triplets for training and evaluation. The framework demonstrates superior performance across multiple benchmarks and is supported by publicly available code.

By Zeyu Ding, Yong Zhou, Jiaqi Zhao, Wen-Liang Du, Xixi Li, Hancheng Zhu, Rui Yao, Abdulmotaleb El Saddik

llms rag multimodal benchmarks

arXiv AI

Jul 1

JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering

arXiv:2606. 31745v1 Announce Type: cross Abstract: Remote sensing change detection (CD) traditionally focuses on pixel-level binary segmentation, which identifies where changes occur but neither what nor why.

By Ziyuan Liu, Ruifei Zhu, Ouqiao Ma, Yuantao Gu

llms computer-vision nlp multimodal benchmarks

arXiv AI

Jun 18

NAVI-Orbital: First In-Orbit Demonstration of a Zero-Shot Vision-Language Model for Autonomous Earth Observation

arXiv:2606. 18271v1 Announce Type: new Abstract: As Earth Observation data generation outpaces downlink bandwidth and human-in-the-loop processing, a widening gap has emerged between onboard collection and actionable ground intelligence.

By Juan Manuel Delfa Victoria, Taran Cyriac John, Andrew W. Herson

llms agents fine-tuning multimodal benchmarks

arXiv Machine Learning

Aug 4

OSMDA: OpenStreetMap-based Domain Adaptation for Remote Sensing VLMs

arXiv:2603. 11804v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) adapted to remote sensing rely heavily on domain-specific image-text supervision, yet high-quality annotations for satellite and aerial imagery remain scarce and expensive to produce.

By Stefan Maria Ailuro (INSAIT, Sofia University "St. Kliment Ohridski"), Mario Markov (INSAIT, Sofia University "St. Kliment Ohridski"), Mohammad Mahdi (INSAIT, Sofia University "St. Kliment Ohridski"), Delyan Boychev (INSAIT, Sofia University "St. Kliment Ohridski"), Luc Van Gool (INSAIT, Sofia University "St. Kliment Ohridski"), Danda Pani Paudel (INSAIT, Sofia University "St. Kliment Ohridski")

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jun 9

AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning

arXiv:2603. 14342v2 Announce Type: replace-cross Abstract: Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery.

By Jiarui Zhang, Junqi Hu, Zurong Mai, Yang Liu, Yuhang Chen, Shuohong Lou, Henglian Huang, Hong Cheng, Lingyuan Zhao, Jianxi Huang, Yutong Lu, Haohuan Fu, Juepeng Zheng

llms nlp fine-tuning multimodal benchmarks safety

arXiv Computer Vision

Aug 25

SkyNative: A Native Multimodal Architecture for Remote Sensing Vision-Language Understanding

arXiv:2605.17949v2 Announce Type: replace Abstract: Remote sensing vision-language models (RS-VLMs) commonly employ a pretrained vision encoder and a projection module to map image features into the...

By Xiao Yang, Ronghao Fu, Zhiwen Lin, Zhuoran Duan, Lang Sun, Jiaqi Liu, Jiashun Zhu, Jiasen Hu, Xu Na, Bo Yang

llms rag multimodal

arXiv Computer Vision

1d ago

One Adapter, Every Resolution: Gated Low-Rank Adaptation for Remote Sensing VLMs

arXiv:2605.07562v2 Announce Type: replace Abstract: Remote sensing imagery spans ground sampling distances (GSDs) from centimeters to tens of meters, so both the visual evidence for a geographic conc...

By Song Zhang, Yanlong Chen, Yining Chen, Xiaowei Zhang, Yawei Li

llms fine-tuning multimodal

arXiv AI

Jul 1

REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agent

arXiv:2511. 17442v3 Announce Type: replace-cross Abstract: Foundation Models (FMs) are increasingly integrated into remote sensing (RS) pipelines.

By Binger Chen, Tacettin Emre B\"ok, Behnood Rasti, Volker Markl, Beg\"um Demir

llms agents computer-vision nlp multimodal benchmarks

arXiv Computation and Language

Sep 17

PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection

PANORAMA introduces a new panoptic grounded captioning framework that jointly generates detailed image captions and associates each phrase with precise pixel-level masks. The authors create PanoCaps, a human‑annotated benchmark with dense captions and near‑complete pixel coverage, and propose a phrase‑mask matching protocol with a generalized Panoptic Quality metric. PANORAMA conditions a pretrained segmenter on contextualized phrase representations, learns to select appropriate masks, and achieves state‑of‑the‑art grounding performance on PanoCaps and other pixel‑level tasks.

By Sara Pieri, Evangelos Kazakos, Shizhe Chen, Josef Sivic, Cordelia Schmid

llms computer-vision multimodal benchmarks

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