arXiv AI

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.

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 4

LaVIDE: Language-Prompted Satellite Change Detection via Map-Image Alignment

arXiv:2411. 19758v2 Announce Type: replace-cross Abstract: Remote sensing change detection based on a map reference and an up-to-date image boosts timely observation of the Earth's surface when earlier images are lacking for comparison.

By Shuguo Jiang, Fang Xu, Chuandong Liu, Hong Tan, Shengyang Li, Lei Yu, Wen Yang, Sen Jia, Gui-Song Xia

rag computer-vision multimodal benchmarks safety

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 30

CCRC: A Change-Aware Captioning and Reasoning Chain for Image Change Captioning and Segmentation

arXiv:2606. 28724v1 Announce Type: cross Abstract: Understanding and localizing subtle changes between paired images is critical for tasks such as surveillance and image editing.

By Jinhong Hu, Xiaoping Wang, Shuyin Huang, Guojin Zhong, Kaitai Liu, Kai Lu

llms computer-vision multimodal benchmarks

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 28

ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensing

arXiv:2605. 15375v2 Announce Type: replace-cross Abstract: Remote sensing change detection (RSCD) localises changes between two images of the same geographic region.

By Bla\v{z} Rolih, Matic Fu\v{c}ka, Filip Wolf, Luka \v{C}ehovin Zajc

diffusion benchmarks

arXiv AI

5d ago

LongEarth-R1: Benchmarking and Aligning Vision-Language Models for Long-Horizon Earth Observation Reasoning

arXiv:2608. 13344v1 Announce Type: new Abstract: Long-horizon Earth observation reasoning requires models to organize multi-stage geographic evolution, localize spatial changes, detect temporal anomalies, and infer future from extended image sequences.

By Yupan Ding, Jing Xiao, Zhenyuan Zhang, Chaofeng Chen, Liang Liao, Gui-Song Xia, Mi Wang

llms nlp fine-tuning multimodal benchmarks

arXiv AI

2d ago

EchoChange: A Diffusion Language Model with Dual Pass Remasking for Factual Remote Sensing Disaster Change Captioning

arXiv:2608. 01856v2 Announce Type: replace Abstract: Bi-temporal remote-sensing disaster change captioning often needs to identify sparse and spatially localized changes across large pre- and post-event scenes and then translate them into coherent, factual descriptions.

By Dongwei Sun, Bowen Yao, Yujie Zhang, Pei Liu, Jing Yao, Xiangyong Cao

llms diffusion multimodal benchmarks

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 AI

Jul 3

MMBench-Live: A Continuously Evolving Benchmark for Multimodal Models

arXiv:2607. 01813v1 Announce Type: cross Abstract: Evaluation benchmarks are essential for assessing vision-language models (VLMs), but most multimodal benchmarks are static, making them vulnerable to temporal staleness, data contamination, and costly maintenance.

By Yuanzhi Liu, Shousheng Zhao, Bo Zhou, Kongming Liang, Zhanyu Ma

llms agents multimodal benchmarks safety

arXiv AI

Aug 7

CogVis: Must Open-Vocabulary Change Detection Perceive the Scene Anew for Every Query?

arXiv:2608. 06150v1 Announce Type: new Abstract: Earth-surface monitoring requires change detection models capable of recognizing arbitrary semantic categories.

By Zijie Wang, Chen Zhong, Wei He

efficiency benchmarks

arXiv AI

Jul 10

MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks

arXiv:2605. 21917v2 Announce Type: replace-cross Abstract: Training Vision Language Models (VLMs) for video event reasoning requires high-quality structured annotations capturing not only what happened, but when, where, why, and with what consequence, at a scale manual labelling cannot support.

By Han Zhang, Wanting Jiang, Tomasz Kornuta, Tian Zheng, Vidya Murali

llms agents reinforcement-learning fine-tuning safety data-engineering

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