Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD). However, both architectures have inherent shortcomings: CNN are constrained by a limited receptive field that may hinder their ability to capture broader spatial contexts, while Transformers are computationally intensive, making them costly to train and deploy on large datasets. Recently, the Mamba architecture, based on state space models, has shown remarkable performance in a series of natural language processing tasks, which can effectively compensate for the shortcomings of the above two architectures. In this paper, we explore for the first time the potential of the Mamba architecture for remote sensing CD tasks. We tailor the corresponding frameworks, called MambaBCD, MambaSCD, and MambaBDA, for binary change detection (BCD), semantic change detection (SCD), and building damage assessment (BDA), respectively. All three frameworks adopt the cutting-edge Visual Mamba architecture as the encoder, which allows full learning of global spatial contextual information from the input images. For the change decoder, which is available in all three architectures, we propose three spatio-temporal relationship modeling mechanisms, which can be naturally combined with the Mamba architecture and fully utilize its attribute to achieve spatio-temporal interaction of multi-temporal features, thereby obtaining accurate change information. On five benchmark datasets, our proposed frameworks outperform current CNN- and Transformer-based approaches without using any complex training strategies or tricks, fully demonstrating the potential of the Mamba architecture in CD tasks. Further experiments show that our architecture is quite robust to degraded data. The source code will be available in https://github.com/ChenHongruixuan/MambaCD
DC-Mamba: Bi-temporal deformable alignment and scale-sparse enhancement for remote sensing change detection
Min Sun School of Materials Science and Engineering Hainan University（孙敏 海南大学材料科学与工程学院）；Fenghui Guo School of Materials Science and Engineering Hainan University（郭峰 海南大学材料科学与工程学院）
FUSAR-GPT : A Spatiotemporal Feature-Embedded and Two-Stage Decoupled Visual Language Model for SAR Imagery
Fudan University（复旦大学）；Discipline and Technology Center of Microwave Vision Intelligent Sensing, Fudan University（微波视觉智能感知学科与技术中心，复旦大学）；Shanghai Innovation Institute（上海创新研究院）
UniTS: Unified Spatio-Temporal Generative Model for Remote Sensing
Jockey Club STEM Laboratory of Quantitative Remote Sensing（裘英俊STEM实验室（定量遥感））；Department of Geography, the University of Hong Kong（香港大学地理系）；School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息电子学院）；Beijing Key Laboratory of Fractional Signals and Systems（北京分数信号与系统重点实验室）；Department of Electrical and Computer Engineering, University of Delaware（德雷塞尔大学电气与计算机工程系）
L-UNet: An LSTM Network for Remote Sensing Image Change Detection
College of Life Sciences and Oceanography, Shenzhen University（深圳大学生命科学与海洋学院）；School of Computer Science, China University of Geosciences（中国地质大学（武汉）计算机学院）
MCSDNet: Mesoscale Convective System Detection Network via Multi-scale Spatiotemporal Information
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）；Beijing Jinkai New Energy Environmental Technology Co., Ltd.（北京金凯新能源环境技术有限公司）
DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning
School of Software Technology Zhejiang University Hangzhou, China；School of Software Technology Zhejiang University Hangzhou, China Innovation Center of Yangtze River Delta Zhejiang University Jiaxing Zhejiang, 314103, China
Information transmission: Inferring change area from change moment in time series remote sensing images
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；School of Geography and Planning, Sun Yat-sen University（中山大学地理科学与规划学院）
ChangeMinds: Multi-task Framework for Detecting and Describing Changes in Remote Sensing
Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗森多夫亥姆霍兹中心）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Technical University of Munich（慕尼黑工业大学）；Nirmata Research（尼尔马塔研究院）；Lancaster University（兰卡斯特大学）
3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection
School of Computer Science and Technology, Civil Aviation University of China（计算机科学与技术学院，中国民航大学）；School of Safety Science and Engineering, Civil Aviation University of China（安全科学与工程学院，中国民航大学）
Towards Remote Sensing Change Detection with Neural Memory
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；School of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（计算机科学与工程学院，电子科技大学）
KTH Royal Inst. of Technology（KTH皇家理工学院）；University of Rwanda（卢旺达大学）
To better understand current trends of urban population growth in Sub-Saharan Africa, high-quality spatiotemporal population estimates are necessary. While the joint use of remote sensing and deep learning has achieved promising results for population distribution estimation, most of the current work focuses on fine-scale spatial predictions derived from single date census, thereby neglecting temporal analyses. In this work, we focus on evaluating how deep learning change detection techniques can unravel temporal population dynamics at short intervals. Since Post-Classification Comparison (PCC…
MaskCD: A Remote Sensing Change Detection Network Based on Mask Classification
Helmholtz Institute Freiberg for Resource Technology（弗莱贝格亥姆霍兹资源技术研究所）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗森多夫亥姆霍兹中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；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 Engineering, Shiv Nadar University（希夫·纳达尔大学工程学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Lancaster University（兰卡斯特大学）
Deep Learning for Wildfire Risk Prediction: Integrating Remote Sensing and Environmental Data
Schulich School of Engineering, Department of Geomatics Engineering, University of Calgary（Schulich 工程学院，测绘工程系，卡尔加里大学）；Department of Geography and Environmental Management, University of Waterloo（地理与环境管理系，滑铁卢大学）；Department of Systems Design Engineering, University of Waterloo（系统设计工程系，滑铁卢大学）；School of Emergency Management, Nanjing University of Information Science and Technology（应急管理学院，南京信息工程大学）；Division of Geoinformatics, KTH Royal Institute of Technology（地理信息学系，皇家理工学院）；School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（遥感与测绘工程学院，南京信息工程大学）；Department of Software Engineering, University of Waterloo（软件工程系，滑铁卢大学）；Department of Management Science and Engineering, University of Waterloo（管理科学与工程系，滑铁卢大学）
Early, precise detection of nutrient deficiency stress (NDS) has key economic as well as environmental impact; precision application of chemicals in place of blanket application reduces operational costs for the growers while reducing the amount of chemicals which may enter the environment unnecessarily. Furthermore, earlier treatment reduces the amount of loss and therefore boosts crop production during a given season. With this in mind, we collect sequences of high-resolution aerial imagery and construct semantic segmentation models to detect and predict NDS across the field. Our work sits a…
A knowledge-based data-driven (KBDD) framework for all-day identification of cloud types using satellite remote sensing
Southern University of Science and Technology（南方科技大学）；Peng Cheng Laboratory（鹏城实验室）；Eastern Institute of Technology（东方理工大学）；Ningbo Institute of Digital Twin（宁波数字孪生研究院）；Peking University（北京大学）
Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through the contact sensing method, which is inconvenient and unfriendly to continuous BP measurement. Hence, we propose an efficient end-to-end network to estimate the BP values from a facial video to achieve remote BP measurement in daily life. In this study, we first derived a Spatial-temporal map of a short-time (~15s) facial video. According to the Spatial-temporal map, we then regressed the BP ranges by a designed blood pressure classifier and simult…
Cochin University of Science and Technology（科钦科学技术大学）；Rajagiri School of Engineering & Technology（拉贾吉里工程技术学院）
Change detection process has recently progressed from a post-classification method to an expert knowledge interpretation process of the time-series data. The technique finds applications mainly in remote sensing images and can be utilized to analyze urbanization and monitor forest regions. In this paper, a framework to perform a knowledge based interpretation of the changes/no changes observed in a spatiotemporal domain using tensor based approaches is presented. An incremental approach to Boolean Tensor Factorization method is proposed in this work, which is adopted to model the change patter…
Multi-scale Restoration of Missing Data in Optical Time-series Images with Masked Spatial-Temporal Attention Network
School of Computer Science, China University of Geosciences（中国地质大学（武汉）计算机学院）；Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education（教育部自然资源信息管理与数字孪生工程软件工程研究中心）；Second Surveying and Mapping Institute of Hunan Province（湖南省第二测绘院）；Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources（自然资源部南方山地自然资源监测与监管重点实验室）
Geospatial-Temporal Sensemaking of Remote Sensing Activity Detections with Multimodal Large Language Model
SenSIP Center, School of ECEE, Arizona State University（SenSIP中心，电子与计算机工程学院，亚利桑那州立大学）；Prime Solutions Group Inc（Prime Solutions Group公司）；Intelligence Advanced Research Projects Activity（智能高级研究计划局）
DFM: Difference Feature Modeling with Text-Guided Gated Contrastive Loss for Remote Sensing Image Change Captioning
ShanghaiTech University（上海科技大学）；National University of Defense Technology（国防科技大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Goldman Sachs（高盛）
Northeastern University（东北大学）；NASA Ames Research Center（美国宇航局艾姆斯研究中心）；Bay Area Environmental Research Institute（湾区环境研究所）；California State University, Monterey Bay（加州州立大学蒙特雷湾分校）
Numerical models based on physics represent the state-of-the-art in earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest-generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning metho…
