This paper deals with the problem of computing surface ice concentration for two different types of ice from digital images of river surface. It presents the results of attempting to solve this problem using several state of the art semantic segmentation methods based on deep convolutional neural networks (CNNs). This task presents two main challenges - very limited availability of labeled training data and presence of noisy labels due to the great difficulty of visually distinguishing between the two types of ice, even for human experts. The results are used to analyze the extent to which some of the best deep learning methods currently in existence can handle these challenges. The code and data used in the experiments are made publicly available to facilitate further work in this domain.
An easy zero-shot learning combination: Texture Sensitive Semantic Segmentation IceHrNet and Advanced Style Transfer Learning Strategy
Hohai University（河海大学）；Nanjing Research Institute of Hydrology and Water Conservation Automation, Ministry of Water Resources（水利部南京水利水文自动化研究所）；Nanjing Zhongyu Intelligent Water Conservancy Research Institute Co., Ltd（南京中禹智慧水利研究院有限公司）
Riverine Land Cover Mapping through Semantic Segmentation of Multispectral Point Clouds
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI（遥感与摄影测量系，芬兰地理研究所）；Faculty of Technology, University of Turku（技术学院，图尔库大学）；Department of Geography and Geology, University of Turku（地理与地质系，图尔库大学）；Faculty of Geological Sciences, Complutense University of Madrid（地质科学学院，马德里卡洛斯三世大学）
Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions
University of Colorado Boulder（科罗拉多大学博尔德分校）；Norwegian Meteorological Institute（挪威气象研究所）；National Snow and Ice Data Center（国家冰雪数据中心）；CIRES（CIRES（环境科学研究合作研究所））
本文针对 Sentinel-1 SAR 海冰制图自动化难题，提出结合 ResNet 与空洞空间金字塔池化的分割方法，在 Extreme Earth version 2 数据集上实现高效、抗噪的海冰与冰型分类，性能优于基线 U-Net。
Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image Segmentation
College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；The First Affiliated Hospital, Sun Yat-Sen University（中山大学附属第一医院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration
Westlake Institute for Advanced Study（西湖高等研究院）；Fudan University（复旦大学）；Meta AI；Westlake University（西湖大学）；Meituan（美团）；Shandong First Medical University & Shandong Academy of Medical Sciences（山东第一医科大学（山东省医学科学院））
EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation
School of Software Engineering, South China University of Technology（南方科技大学软件工程学院）；Pazhou Laboratory, Guangzhou, China（广州帕佐实验室）；School of Electronic and Information Engineering, South China University of Technology（南方科技大学电子与信息工程学院）；Minieye, Shenzhen, Guangdong, China（深圳Minieye公司）
Ice-FMBench: A Foundation Model Benchmark for Sea Ice Type Segmentation
University of Colorado Denver（科罗拉多大学丹佛分校）；University of Colorado Boulder（科罗拉多大学波德分校）；National Snow and Ice Data Center (NSIDC), CIRES, University of Colorado Boulder（国家冰雪数据研究中心（NSIDC）、CIRES、科罗拉多大学波德分校）
Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China's Yangtze River Economic Belt
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China（中国科学院 aerospace information research institute, 北京）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China（中国科学院大学电子电气与通信工程学院, 北京）；School of Mathematics, Southeast University, Nanjing 210096, China（东南大学数学学院, 南京）；Graduate School of Frontier Sciences, the University of Tokyo, Chiba 277-8561, Japan（东京大学前沿科学研究生院, 日本）；RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan（RIKEN 高度智能项目中心, 日本）
Sea Ice Segmentation From SAR Data by Convolutional Transformer Networks
CEOSpaceTech, University Politehnica of Bucharest（布加勒斯特理工大学空间技术研究中心）；Remote Sensing Technology Institute, German Aerospace Center (DLR)（德国航空航天中心（DLR）遥感技术研究所）
AI-ready Snow Radar Echogram Dataset (SRED) for climate change monitoring
Center for Remote Sensing and Integrated Systems (CReSIS) University of Kansas（远程感知与集成系统中心（CReSIS）大学堪萨斯大学）；Department of Information Systems University of Maryland Baltimore County（信息系统系马里兰大学巴尔的摩县分校）；Department of Computer Science and Engineering Lehigh University（计算机科学与工程系莱恩大学）
Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images
Graphics-Visualization-Computing Lab, International Institute of Information Technology Bangalore, Karnataka 560100, India（国际信息学院信息技术研究所，班加罗尔，卡纳塔克邦，印度）；Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India（电子与通信工程系，曼尼普尔理工学院，曼尼普尔高等教育学院，曼尼普尔，卡纳塔克邦，印度）
