Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification to categorize each pixel. Despite the advancements brought about by Deep Neural Networks (DNNs), their performance in segmentation tasks is hindered by challenges such as limited availability of labeled data, class imbalance and the inherent variability and complexity of satellite images. In order to mitigate those issues, our study explores the effectiveness of a Cut-and-Paste augmentation technique for semantic segmentation in satellite images. We adapt this augmentation, which usually requires labeled instances, to the case of semantic segmentation. By leveraging the connected components in the semantic segmentation labels, we extract instances that are then randomly pasted during training. Using the DynamicEarthNet dataset and a U-Net model for evaluation, we found that this augmentation significantly enhances the mIoU score on the test set from 37.9 to 44.1. This finding highlights the potential of the Cut-and-Paste augmentation to improve the generalization capabilities of semantic segmentation models in satellite imagery.
Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
Mechanical and Aerospace Engineering, AIAA Student Member（机械与航空航天工程，AIAA学生会员）；Department of Computer Science（计算机科学系）；School of Environment and Natural Resources（环境与自然资源学院）；Computer Science and Engineering（计算机科学与工程）；Department of Electronics and Telecommunications Engineering（电子与电信工程系）；Aeronautical and Astronautical Engineering, AIAA Associate Fellow（航空与航天工程，AIAA associate fellow）
Detecting Endangered Marine Species in Autonomous Underwater Vehicle Imagery Using Point Annotations and Few-Shot Learning
Australian Centre for Robotics, University of Sydney（悉尼大学澳大利亚机器人中心）；Marine Technology Department, Norwegian University of Science and Technology（挪威科技大学海洋技术系）；Institute for Marine and Antarctic Studies, University of Tasmania（塔斯马尼亚大学海洋与南极研究所）
MineNetCD: A Benchmark for Global Mining Change Detection on Remote Sensing Imagery
Technical University of Munich（慕尼黑工业大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗森多夫亥姆霍兹研究中心）；Wuhan University of Science and Technology（武汉科技大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Lancaster University（兰卡斯特大学）
Evaluating the Efficacy of Sentinel-2 versus Aerial Imagery in Serrated Tussock Classification
School of Information Technology, Deakin University, Australia（信息技术学院，德堪大学，澳大利亚）；Agriculture Victoria Research, Department of Energy, Environment and Climate Action, Australia（维多利亚农业研究，能源、环境与气候变化行动部，澳大利亚）；Institute of Innovation, Science and Sustainability, Federation University, Australia（创新、科学与可持续性研究所，联邦大学，澳大利亚）
Multi-Modal Building Change Detection for Large-Scale Small Changes: Benchmark and Baseline
MOE Key Lab of ICSP, Anhui Provincial Key Lab of Multimodal Cognitive Computation, IMIS Lab of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei, China（教育部信息与通信领域重点实验室、安徽省多模态认知计算重点实验室、安徽省IMIS实验室、计算机科学与技术学院、安徽大学、合肥，中国）；School of Public Safety and Emergency Management, Anhui University of Science and Technology, Hefei 231131, China（公共安全与应急管理学院、安徽理工大学、合肥231131，中国）；College of Computing and Data Science, Nanyang Technological University, Singapore 639798（计算与数据科学学院、南洋理工大学、新加坡639798）；Shaanxi Joint Laboratory of Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, China（人工智能联合实验室、陕西科技大学、西安710021，中国）；School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China（软件工程学院、西安交通大学、西安710049，中国）；Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8561, Japan（前沿科学研究生院、东京大学、千叶，日本）
A Data-Driven RetinaNet Model for Small Object Detection in Aerial Images
Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；University of Missouri（密苏里大学）；Computer Science University of Missouri Columbia, MO, USA（计算机科学密苏里大学哥伦比亚分校）
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?
Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心机器人与机电一体化研究所）；Department of Informatics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院信息学系）；Department of Computer Science, Technical University of Munich (TUM)（慕尼黑工业大学计算机科学系）
Enhancing Ship Classification in Optical Satellite Imagery: Integrating Convolutional Block Attention Module with ResNet for Improved Performance
Sungkyunkwan University School of Medicine（成均馆大学医学院）；The Coala Inc.（考拉公司）；Korea Science Academy of Korea Advanced Institute of Science and Technology（韩国科学技术院韩国科学学院）；Seoul National University School of Earth and Environmental Sciences（首尔国立大学地球与环境科学学院）
Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery
Nanjing Normal University（南京师范大学）；Peking University（北京大学）；Technical University of Munich（慕尼黑工业大学）；Shenyang Jianzhu University（沈阳建筑大学）；Nanjing University（南京大学）
DengueNet: Dengue Prediction using Spatiotemporal Satellite Imagery for Resource-Limited Countries
Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）；University College Dublin（都柏林大学学院）；University of Cauca（考卡大学）；Indiana University – Purdue University Indianapolis（印第安纳大学与普渡大学印第安纳波利斯联合分校）；Worcester Polytechnic Institute（伍斯特理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
