State-of-the-art object detection methods applied to satellite and drone imagery largely fail to identify small and dense objects. One reason is the high variability of content in the overhead imagery due to the terrestrial region captured and the high variability of acquisition conditions. Another reason is that the number and size of objects in aerial imagery are very different than in the consumer data. In this work, we propose a small object detection pipeline that improves the feature extraction process by spatial pyramid pooling, cross-stage partial networks, heatmap-based region proposal network, and object localization and identification through a novel image difficulty score that adapts the overall focal loss measure based on the image difficulty. Next, we propose novel contrastive learning with progressive domain adaptation to produce domain-invariant features across aerial datasets using local and global components. We show we can alleviate the degradation of object identification in previously unseen datasets. We create a first-ever domain adaptation benchmark using contrastive learning for the object detection task in highly imbalanced satellite datasets with significant domain gaps and dominant small objects. The proposed method results in a 7.4% increase in mAP performance measure over the best state-of-art.
WV-Net: A foundation model for SAR WV-mode satellite imagery trained using contrastive self-supervised learning on 10 million images
University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）；University of Washington（华盛顿大学）；University of New Hampshire（新罕布什尔大学）；Univ. Brest（布雷斯特大学）；CNRS（法国国家科学研究中心）；IRD（法国发展研究所）；Ifremer（法国海洋开发研究院）；IUEM（欧洲海洋科学研究所）
S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images
School of Computer China University of Geosciences Wuhan, China；School of Geography；Information Engineering China University of Geosciences Wuhan, China；School of Management Wuhan University of Science
Unsupervised Wildfire Change Detection based on Contrastive Learning
University of Nebraska-Lincoln（内布拉斯加大学林肯分校）；University of California, Berkeley（加州大学伯克利分校）；Boston University（波士顿大学）；Lisbon University（里斯本大学）；Northwest Nazarene University（西北拿撒勒大学）；University of Oxford（牛津大学）
Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning
Saw Swee Hock School of Public Health（苏瑞福公共卫生学院）；Institute of Data Science, NUS（新加坡国立大学数据科学研究院）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）
Dual-level Progressive Hardness-Aware Reweighting for Cross-View Geo-Localization
Group of Intelligent Signal Processing, Harbin Engineering University（哈尔滨工程大学智能信号处理组）；School of Astronautics, Beihang University（北京航空航天大学航天学院）；State Key Laboratory of Space Information System（空间信息系统与集成应用国家重点实验室）
Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery
Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（亥姆霍兹德累斯顿罗森多夫研究中心（HZDR））；Technical University of Munich (TUM)（慕尼黑工业大学（TUM））；Getulio Vargas Foundation（热图利奥·瓦加斯基金会）；Institute of Advanced Research in Artificial Intelligence (IARAI)（人工智能高级研究所（IARAI））
Semi-LAR: Semi-supervised Contrastive Learning with Linear Attention for Removal of Nighttime Flares
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China（武汉科技大学计算机科学与技术学院）；Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China（哈尔滨工业大学计算机学院）；SRO department, Institute for Infocomm Research, A*STAR, Singapore（新加坡资讯通信研究院SRO部门）
Multimodal Urban Tree Detection from Satellite and Street-Level Imagery via Annotation-Efficient Deep Learning Strategies
Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）；Department of Biological and Agricultural Engineering, University of California, Davis（加州大学戴维斯分校生物与农业工程系）
Learning Street View Representations with Spatiotemporal Contrast
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University（遥感与地理信息系统研究所，地球与空间科学学院，北京大学）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（土木与环境工程系，香港科学与技术大学）；SEAI Lab, Department of Geography and the Environment, University of Texas at Austin（SEAI实验室，地理与环境系，德克萨斯大学奥斯汀分校）
Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Xiaomi Inc.（小米公司）；School of Software Technology, Zhejiang University（浙江大学软件学院）
AeroDeshadow: Physics-Guided Shadow Synthesis and Penumbra-Aware Deshadowing for Aerospace Imagery
MOE Key Laboratory of ICSP, IMIS Laboratory of Anhui, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Zenmorn-AHU AI Joint Laboratory, School of Computer Science and Technology, Anhui University（教育部ICSP重点实验室、安徽IMIS实验室、安徽省多模态认知计算重点实验室、Zenmorn-AHU人工智能联合实验室、安徽大学计算机科学与技术学院）
Predicting butterfly species presence from satellite imagery using soft contrastive regularisation
The Alan Turing Institute, the UK（英国阿尔安·图灵研究所）；Wageningen University & Research, the NL（瓦赫宁根大学与研究中心）；University College London, the UK（伦敦大学学院）；UK Centre for Ecology & Hydrology, the UK（英国生态与水文研究中心）
Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN
Technical University of Munich（慕尼黑工业大学）；University of the Bundeswehr Munich（慕尼黑联邦国防军大学）；China University of Mining and Technology（中国矿业大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Tongji University（同济大学）
