EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote Sensing
Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology（北京理工大学多学科先进研究院）；School of Mechatronical Engineering, Beijing Institute of Technology（北京理工大学机电工程学院）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）
Wei Zhang, Miaoxin Cai, Tong Zhang, Jun Li, Yin Zhuang, Xuerui Mao
Recent advances in prompt learning have allowed users to interact with artificial intelligence (AI) tools in multi-turn dialogue, enabling an interactive understanding of images. However, it is difficult and inefficient to deliver information in complicated remote sensing (RS) scenarios using plain language instructions alone, which would severely hinder deep comprehension of the latent content in imagery. Besides, existing prompting strategies in natural scenes are hard to apply to interpret the RS data due to significant domain differences. To address these challenges, the first visual prompting-based multi-modal large language model (MLLM) named EarthMarker is proposed in the RS domain. EarthMarker is capable of interpreting RS imagery at the image, region, and point levels by levering visual prompts (i.e., boxes and points). Specifically, a shared visual encoding method is developed to establish the spatial pattern interpretation relationships between the multi-scale representations of input images and various visual prompts. Subsequently, the mixed visual-spatial representations are associated with language instructions to construct joint prompts, enabling the interpretation of intricate content of RS imagery. Furthermore, to bridge the domain gap between natural and RS data, and effectively transfer domain-level knowledge from natural scenes to the RS domain, a cross-domain learning strategy is developed to facilitate the RS imagery understanding. In addition, to tackle the lack of RS visual prompting data, a dataset named RSVP featuring multi-modal multi-granularity visual prompts instruction-following is constructed. Our code and dataset are available at https://github.com/wivizhang/EarthMarker.
EarthGPT-X: A Spatial MLLM for Multi-level Multi-Source Remote Sensing Imagery Understanding with Visual Prompting
School of Interdisciplinary Science, Beijing Institute of Technology（交叉科学学院，北京理工大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology（空间智能信息处理国家重点实验室，北京理工大学）；School of Optics and Photonics, Beijing Institute of Technology（光学与 photonics 学院，北京理工大学）；State Key Laboratory of Explosion Science and Safety Protection, Beijing（爆炸科学与安全防护国家重点实验室，北京）
GRASP: Guided Region-Aware Sparse Prompting for Adapting MLLMs to Remote Sensing
School of Computing, Kyung Hee University（京畿大学计算机学院）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；College of Computer Science and Information Engineering, Harbin Normal University（哈尔滨师范大学计算机科学与信息工程学院）；College of Professional and Continuing Education, The Hong Kong Polytechnic University（香港理工大学专业及继续教育学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）
March in Chat: Interactive Prompting for Remote Embodied Referring Expression
Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Beyond Templates: Revisiting Zero-Shot Remote Sensing through Meta-Prompting
Remote Sensing Lab, National Technical University of Athens（遥感实验室，国家技术大学雅典分校）；Remote Sensing Lab, Department of Engineering and Sciences, National Technical University of Athens（遥感实验室，工程与科学系，国家技术大学雅典分校）；Universitas Mercatorum（默卡托大学）
SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting
Mediterranean Agronomic Institute of Montpellier - CIHEAM-IAMM（地中海农业研究院-CIHEAM-IAMM）；Inria（法国国家信息与自动化技术研究院）；INRAE（法国国家农业研究咨询中心）；Cirad（国际热带农业研究中心）；UMR TETIS（TETIS联合研究单位）；Univ. of Montpellier（蒙彼利埃大学）
SSPA: Split-and-Synthesize Prompting with Gated Alignments for Multi-Label Image Recognition
School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）；School of Computer Science and Engineering, Macau University of Science and Technology（澳门科技大学计算机科学与工程学院）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；Sangfor Technologies Inc.（深信服科技股份有限公司）；Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Westlake University（西湖大学）
A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images
College of Computer Science and Software Engineering, Hohai University（河海大学计算机与软件学院）；Hong Kong University of Science and Technology（香港科技大学）；School of Computer and Electronic Information, Nanjing Normal University（南京师范大学计算机与电子信息学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery
Ben-Gurion University of the Negev（内盖夫本-古里安大学）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Ben Gurion Institute for the Study of Israel & Zionism（本-古里安以色列与犹太复国主义研究所）；Ben-Gurion Israel Research Institute（本-古里安以色列研究所）
Breaking the Box: Enhancing Remote Sensing Image Segmentation with Freehand Sketches
Huzhou University（湖州大学）；Singapore University of Technology and Design（新加坡科技设计大学）；University of Science and Technology of China（中国科学技术大学）；Zhejiang University（浙江大学）；KOKONI 3D, Moxin (Huzhou) Tech. Co., LTD.（墨芯（湖州）科技有限公司（KOKONI 3D））
RSHallu: Dual-Mode Hallucination Evaluation for Remote-Sensing Multimodal Large Language Models with Domain-Tailored Mitigation
College of Computer Science, Chongqing University（重庆大学计算机科学学院）；Heavy Rainfall Research Center of China（中国强降水研究中心）；CMA Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing Institute of Meteorological Sciences（中国气象局气候资源转化经济重点开放实验室、重庆气象科学研究院）；Chongqing Metropolitan College of Science（重庆科学理工学院）；School of Intelligence Science（智能科学学院）；College of Artificial Intelligence, Harbin Institute of Technology（人工智能学院、哈尔滨工业大学）；BNU-UIC Institute of Artificial Intelligence（北京师范大学-国际学院人工智能与未来网络研究院）；Future Networks, Beijing Normal University at Zhuhai（未来网络、北京师范大学珠海分校）
Task-Guided Prompting for Unified Remote Sensing Image Restoration
School of Electronic and Information Engineering, Ningbo University of Technology（宁波工程学院电子与信息工程学院）；University of Exeter（埃克塞特大学）；Engineering Research Center of Intelligent Finance, Ministry of Education（教育部智能金融工程研究中心）；School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机与人工智能学院）
Auto-Prompting SAM for Weakly Supervised Landslide Extraction
School of Information Science and Engineering, Wuhan University of Science and Technology（武汉科技大学信息科学与工程学院）；School of Science and Engineering, The Chinese University of Hong Kong（香港中文大学（深圳）理学院）；Chair of Data Science in Earth Observation, Technical University of Munich (TUM)（慕尼黑技术大学地球观测数据科学教授职位）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗斯托克亥姆霍兹研究中心）；Lancaster Environment Centre, Lancaster University（兰卡斯特大学环境研究中心）
GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；The University of Western Australia（西澳大利亚大学）
VP-Hype: A Hybrid Mamba-Transformer Framework with Visual-Textual Prompting for Hyperspectral Image Classification
Institute of Applied Sciences and Intelligent Systems (ISASI), CNR（应用科学与智能系统研究所（ISASI），CNR）；Dept. of Engineering for Innovation, University of Salento（创新工程系，萨勒诺大学）；VSC Laboratory, Department of Electronics and Automation, University of Biskra（VSC实验室，电子与自动化系，比斯克拉大学）；Dept. of Computer Science and AI, University of the Basque Country (UPV/EHU)（计算机科学与人工智能系，巴斯克国家大学（UPV/EHU））；Sorbonne Center for Artificial Intelligence, Sorbonne University Abu Dhabi（索邦大学阿布扎克人工智能中心）
Advancing Weakly-Supervised Change Detection in Satellite Images via Adversarial Class Prompting
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（信息工程测绘遥感国家重点实验室，武汉大学）；School of Computer Science and Technology, Anhui University（计算机科学与技术学院，安徽大学）；Graduate School of Frontier Sciences, University of Tokyo（前沿科学研究院，东京大学）；Institute of Geodesy and Photogrammetry, ETH Zürich（测绘学研究院，苏黎世联邦理工学院）；Department of Computer Science, Stanford University（计算机科学系，斯坦福大学）
Degradation-Aware Metric Prompting for Hyperspectral Image Restoration
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China（北京理工大学计算机科学与技术学院）；School of Computer Science, Wuhan University, Wuhan, Hubei, China（武汉大学计算机学院）；Zhongguancun Academy, Beijing, China（中关村学院）
Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
