arXiv AI

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.

arXiv AI

Jun 9

Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents

arXiv:2606. 07538v1 Announce Type: cross Abstract: Large language model (LLM)-based agents provide a novel paradigm for the automated processing of remote sensing(RS) data.

By Zeyuan Wang, Dongyang Hou, Cheng Yang, Xuezhi Cui, Linrui Xu, Bo Yu, Gaozhi Zhou, Ziyu Li, Liangtian Liu, Kai Ouyang, Wang Guo, Lili Zhu, Chao Tao

llms agents

arXiv AI

Jun 16

RSRCC: A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking

arXiv:2604. 20623v2 Announce Type: replace-cross Abstract: Traditional change detection identifies where changes occur, but does not explain what changed in natural language.

By Roie Kazoom, Yotam Gigi, George Leifman, Tomer Shekel, Genady Beryozkin

rag computer-vision multimodal benchmarks

arXiv AI

Jun 6

CangLing-KnowFlow: A Unified Knowledge-and-Flow-fused Agent for Comprehensive Remote Sensing Applications

arXiv:2512. 15231v3 Announce Type: replace Abstract: The automated and intelligent processing of massive remote sensing (RS) datasets is critical in Earth observation (EO).

By Zhengchao Chen, Haoran Wang, Jing Yao, Jianshe Zhang, Pedram Ghamisi, Jun Zhou, Peter M. Atkinson, Bing Zhang

llms rag agents benchmarks safety

arXiv AI

Jul 29

RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation

arXiv:2607. 24772v1 Announce Type: new Abstract: Geoscience research requires complex analysis and domain expertise, with remote sensing (RS) observations as a key foundation.

By Bingxian Wu, Yu Zhang, Zonghao Guo, Tang Liu, Chen Qian, Yuxiang Lu, Xingbo Du, Yanghao Li, Yidan Zhang, Chi Chen, Ling Yao, Maosong Sun

llms agents

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

Jul 15

The Emerging Paradigm of Geospatial Foundation Models: From Pre-Training to Agentic Reasoning

arXiv:2607. 12177v1 Announce Type: new Abstract: The analysis of satellite and aerial imagery has entered a new era with the advent of foundation models.

By Shelley Cazares

llms agents fine-tuning multimodal benchmarks

arXiv AI

Jul 29

Filling Before Advancing: Capability-Gap-Driven Post-Training for Scenario-Specialized Remote Sensing MLLMs

arXiv:2607. 22205v2 Announce Type: replace-cross Abstract: Remote sensing multimodal large language models (RS-MLLMs) have improved general aerial-image understanding.

By Yuheng Zong, Minghua Wang, Xin Zhao, Zhi-Hui Zhan, Antonio Plaza, Jon Atli Benediktsson

llms fine-tuning 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 AI

Jul 15

NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing

arXiv:2603. 16307v2 Announce Type: replace Abstract: Remote sensing underpins crucial applications such as disaster relief and ecological field surveys, where systems must understand complex scenes and constraints and make reliable decisions.

By Ming Yang, Zhi Zhou, Shi-Yu Tian, Kun-Yang Yu, Lan-Zhe Guo, Yu-Feng Li

llms 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

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 12

DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems

arXiv:2601. 13591v2 Announce Type: replace Abstract: Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning.

By Maojun Sun, Yifei Xie, Yue Wu, Ruijian Han, Binyan Jiang, Defeng Sun, Yancheng Yuan, Jian Huang

llms agents multimodal benchmarks

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