While the volume of remote sensing data is increasing daily, deep learning in Earth Observation faces lack of accurate annotations for supervised optimization. Crowdsourcing projects such as OpenStreetMap distribute the annotation load to their community. However, such annotation inevitably generates noise due to insufficient control of the label quality, lack of annotators, frequent changes of the Earth's surface as a result of natural disasters and urban development, among many other factors. We present Adaptively trIggered Online Object-wise correction (AIO2) to address annotation noise induced by incomplete label sets. AIO2 features an Adaptive Correction Trigger (ACT) module that avoids label correction when the model training under- or overfits, and an Online Object-wise Correction (O2C) methodology that employs spatial information for automated label modification. AIO2 utilizes a mean teacher model to enhance training robustness with noisy labels to both stabilize the training accuracy curve for fitting in ACT and provide pseudo labels for correction in O2C. Moreover, O2C is implemented online without the need to store updated labels every training epoch. We validate our approach on two building footprint segmentation datasets with different spatial resolutions. Experimental results with varying degrees of building label noise demonstrate the robustness of AIO2. Source code will be available at https://github.com/zhu-xlab/AIO2.git.
Semantic Segmentation of Remote Sensing Images with Sparse Annotations
Technical University of Munich（慕尼黑工业大学）；German Aerospace Center（德国航空航天中心）；Wageningen University（瓦赫宁根大学）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation
College of Instrumentation and Electrical Engineering, Jilin University（吉林大学仪器电气工程学院）；Key Laboratory of Geophysical Exploration Equipment, Ministry of Education（教育部地球物理探测装备重点实验室）；School of Mathematics, Jilin University（吉林大学数学学院）
SGMA: Semantic-Guided Modality-Aware Segmentation for Remote Sensing with Incomplete Multimodal Data
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）；Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州研究院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images
University of Electronic Science and Technology of China（电子科技大学）；Inception Institute of Artificial Intelligence (IIAI)（人工智能初始研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
IBM Zurich Research Lab（IBM苏黎世研究实验室）；EPFL（洛桑联邦理工学院）；APHP. Centre（巴黎公立医院中心）；Cochin hospital（科钦医院）；Université de Paris（巴黎大学）；ETH Zurich（苏黎世联邦理工学院）；Uppsala University（乌普萨拉大学）
Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images
School of Software Engineering, Xi’an Jiaotong University（软件工程学院，西安交通大学）；School of Computer Science and Technology and Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi’an Jiaotong University（计算机科学与技术学院和教育部智能网络与网络安全重点实验室，西安交通大学）；School of Automation, Xi’an Jiaotong University（自动化学院，西安交通大学）；College of Artificial Intelligence, Xi’an Jiaotong University（人工智能学院，西安交通大学）；School of Mathematics and Statistics and Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi’an Jiaotong University（数学与统计学院和教育部智能网络与网络安全重点实验室，西安交通大学）
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Engineering, Hong Kong University of Science and Technology（香港科技大学工程学院）
ReCon1M:A Large-scale Benchmark Dataset for Relation Comprehension in Remote Sensing Imagery
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；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）
FG-TreeSeg: Flow-Guided Tree Crown Segmentation without Instance Annotations
Department of Geography, University of South Carolina（南卡罗来纳大学地理系）；Department of Geography, Virginia Polytechnic Institute and State University（弗吉尼亚理工大学地理系）
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features
Beijing Institute of Technology（北京理工大学）；German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立高等物理、数学与信息学院）；University of Iceland（冰岛大学）
iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervision
Department of Electrical Engineering, University of Bras\'ilia, Bras\'ilia, DF, Brazil；Department of Geography, University of Bras\'ilia, Bras\'ilia, DF, Brazil
G2HFNet: GeoGran-Aware Hierarchical Feature Fusion Network for Salient Object Detection in Optical Remote Sensing Images
School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；State Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）；School of Automation, Hangzhou Dianzi University（自动化学院，杭州电子科技大学）；School of information engineering, Huzhou University（信息工程学院，湖州大学）；School of Data Science, Lingnan University（数据科学学院，岭南大学）
VFM-ISRefiner: Towards Better Adapting Vision Foundation Models for Interactive Segmentation of Remote Sensing Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航天信息研究所）；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）；School of Information Science and Technology, Beijing Forestry University（北京林业大学信息科学与技术学院）
UniDiff: Parameter-Efficient Adaptation of Diffusion Models for Land Cover Classification with Multi-Modal Remotely Sensed Imagery and Sparse Annotations
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）
Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations
Beijing Institute of Technology（北京理工大学）；North China University of Technology（北方工业大学）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学附属北京天坛医院）
