Predicting realistic ground views from satellite imagery in urban scenes is a challenging task due to the significant view gaps between satellite and ground-view images. We propose a novel pipeline to tackle this challenge, by generating geospecifc views that maximally respect the weak geometry and texture from multi-view satellite images. Different from existing approaches that hallucinate images from cues such as partial semantics or geometry from overhead satellite images, our method directly predicts ground-view images at geolocation by using a comprehensive set of information from the satellite image, resulting in ground-level images with a resolution boost at a factor of ten or more. We leverage a novel building refinement method to reduce geometric distortions in satellite data at ground level, which ensures the creation of accurate conditions for view synthesis using diffusion networks. Moreover, we proposed a novel geospecific prior, which prompts distribution learning of diffusion models to respect image samples that are closer to the geolocation of the predicted images. We demonstrate our pipeline is the first to generate close-to-real and geospecific ground views merely based on satellite images.
Structure-Aware Matching for Robust UAV Thermal-to-Satellite Geo-Localization
State Key Laboratory of Spatial Datum, Henan University（河南大学空间数据基准国家重点实验室）；College of Remote Sensing and Geoinformatics Engineering, Henan University（河南大学遥感与地理信息工程学院）；School of Computer and Information Engineering, Henan University（河南大学计算机与信息工程学院）；Henan Yunhuan NetLink UAV Technology Co., Ltd.（河南云环网联无人机科技有限公司）
This work utilizes a MobileNetV2 Convolutional Neural Network (CNN) for fast, mobile detection of satellites, and rejection of stars, in cluttered unresolved space imagery. First, a custom database is created using imagery from a synthetic satellite image program and labeled with bounding boxes over satellites for "satellite-positive" images. The CNN is then trained on this database and the inference is validated by checking the accuracy of the model on an external dataset constructed of real telescope imagery. In doing so, the trained CNN provides a method of rapid satellite identification fo…
Scale-Aware UAV-to-Satellite Cross-View Geo-Localization: A Semantic Geometric Approach
College of Aerospace Science and Engineering, National University of Defense Technology（航天工程学院，国防科技大学）；Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation（湖南省图像测量与视觉导航重点实验室）；School of Information Science and Engineering, Lanzhou University（信息科学与工程学院，兰州大学）
deSEO: Physics-Aware Dataset Creation for High-Resolution Satellite Image Shadow Removal
Austrian Institute of Technology（奥地利理工学院）；University of Cambridge（剑桥大学）；Technical University of Munich (TUM)（慕尼黑工业大学）；Friedrich Schiller University of Jena（耶拿弗里德里希·席勒大学）
A large-scale climate-aware satellite image dataset for domain adaptive land-cover semantic segmentation
School of Remote Sensing Information Engineering, Wuhan University（武汉大学遥感信息工程学院）；State Key Laboratory of Geo-information Engineering（地理信息工程国家重点实验室）；Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology（北京理工大学多学科先进研究院）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机学院）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）
GeoRay: Gauge-Aware Feed-Forward Satellite 3D Reconstruction in the Geodetic Frame
School of Electronics and Information Engineering, Harbin Institute of Technology（哈尔滨工业大学电子与信息工程学院）；th Research Institute, China Electronics Technology Group Corporation（中国电子科技集团公司第五十四研究所）；State Key Laboratory of Comprehensive PNT Network and Equipment Technology（综合PNT网络与装备技术国家重点实验室）；School of Electrical and Electronic Engineering, University of Manchester（曼彻斯特大学电气与电子工程学院）
Australian National University（澳大利亚国立大学）；Huawei（华为）；University of Oxford（牛津大学）；Data61/CSIRO（澳大利亚联邦科学与工业研究组织数据61所）
We address the problem of ground-to-satellite image geo-localization, that is, estimating the camera latitude, longitude and orientation (azimuth angle) by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our prior arts treat the above task as pure image retrieval by selecting the most similar satellite reference image matching the ground-level query image. However, such an approach often produces coarse location estimates because the geotag of the retrieved satellite image only corresponds to the image center while the ground …
German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich (TUM)（慕尼黑工业大学）
Detecting changes on the ground in multitemporal Earth observation data is one of the key problems in remote sensing. In this paper, we introduce Sibling Regression for Optical Change detection (SiROC), an unsupervised method for change detection in optical satellite images with medium and high resolution. SiROC is a spatial context-based method that models a pixel as a linear combination of its distant neighbors. It uses this model to analyze differences in the pixel and its spatial context-based predictions in subsequent time periods for change detection. We combine this spatial context-base…
Reconstructing Satellites in 3D from Amateur Telescope Images
College of Future Technology（未来技术学院）；National Biomedical Imaging Center（国家生物医学成像中心）；College of Engineering（工程学院）；State Key Laboratory of Multimedia Information Processing（多媒体信息处理国家重点实验室）；National Engineering Research Center of Visual Technology（视觉技术国家工程研究中心）；School of Computer Science（计算机科学学院）；Beijing Jingdun Technology Co., Ltd.（北京竞 Dunn 技术有限公司）；National Laboratory on Adaptive Optics（自适应光学国家实验室）；Institute of Optics and Electronics（光学与电子学研究所）；Chinese Academy of Sciences（中国科学院）
TAMMs: Change Understanding and Forecasting in Satellite Image Time Series with Temporal-Aware Multimodal Models
School of Computer Science & Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；School of Computing, National University of Singapore（计算学院，新加坡国立大学）
We introduce the Satellite Neural Radiance Field (Sat-NeRF), a new end-to-end model for learning multi-view satellite photogrammetry in the wild. Sat-NeRF combines some of the latest trends in neural rendering with native satellite camera models, represented by rational polynomial coefficient (RPC) functions. The proposed method renders new views and infers surface models of similar quality to those obtained with traditional state-of-the-art stereo pipelines. Multi-date images exhibit significant changes in appearance, mainly due to varying shadows and transient objects (cars, vegetation). Rob…
Risk-aware urban localization with the Global Navigation Satellite System (GNSS) remains an unsolved problem with frequent misdetection of the user's street or side of the street. Significant advances in 3D map-aided GNSS use grid-based GNSS shadow matching alongside AI-driven line-of-sight (LOS) classifiers and server-based processing to improve localization accuracy, especially in the cross-street direction. Our prior work introduces a new paradigm for shadow matching that proposes set-valued localization with computationally efficient zonotope set representations. While existing literature …
Mayachitra, Inc.（Mayachitra公司）；UC Santa Barbara（加州大学圣塔芭芭拉分校）
Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or insert/remove roads in a satellite image. This paper proposes a novel approach for detecting and localizing seams in such images. While there are methods to detect seam carving based manipulations, this is the first time that robust localization and detection of seam carving forgery is made possible. We also propose a seam localization score (SLS) metric to evaluate the effectiveness of localization. The proposed metho…
Zero padding is widely used in convolutional neural networks to prevent the size of feature maps diminishing too fast. However, it has been claimed to disturb the statistics at the border. As an alternative, we propose a context-aware (CA) padding approach to extend the image. We reformulate the padding problem as an image extrapolation problem and illustrate the effects on the semantic segmentation task. Using context-aware padding, the ResNet-based segmentation model achieves higher mean Intersection-Over-Union than the traditional zero padding on the Cityscapes and the dataset of DeepGlobe …
BuildMamba: A Visual State-Space Based Model for Multi-Task Building Segmentation and Height Estimation from Satellite Images
Graduate School of Engineering, Ozyegin university（奥济根大学工程研究生院）；Department of Computer Science, University of Rochester（罗切斯特大学计算机科学系）；School of Engineering and Natural Sciences, Istanbul Medipol University（伊斯坦布尔梅迪波尔大学工程与自然科学学院）；Huawei Turkiye R&D Center（华为土耳其研发中心）
Data-Driven Uncertainty-Aware Forecasting of Sea Ice Conditions in the Gulf of Ob Based on Satellite Radar Imagery
Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russia（斯克尔科沃科学与技术研究所应用人工智能中心）；Moscow Institute of Physics and Technology, Dolgoprudny, Russia（莫斯科物理技术学院）；Autonomous Non-Profit Organization Artificial Intelligence Research Institute (AIRI), Moscow, Russia（自主非营利组织人工智能研究 institute（AIRI））
Wetland mapping from sparse annotations with satellite image time series and temporal-aware segment anything model
Department of Geography, The University of Hong Kong, Hong Kong, China（香港大学地理系）；Pengcheng Laboratory, Shenzhen, China（深圳鹏城实验室）；Department of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China（哈尔滨工业大学（深圳）电子与信息工程学院）；Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Ministry of Natural Resources, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China（粤港澳大湾区地理环境监测重点实验室，自然资源部，广东省城市信息学重点实验室，深圳空间智能感知与服务重点实验室，深圳大学）
Time-aware UNet and super-resolution deep residual networks for spatial downscaling
Department of Mathematics and Statistics（数学与统计学系）；University of Jyväskylä（于韦斯屈莱大学）；Department of Economic Sciences（经济科学系）；University of Salento（萨伦托大学）；National Centre for HPC, Big Data and Quantum Computing（高性能计算、大数据与量子计算国家中心）；National Biodiversity Future Center（国家生物多样性未来中心）；University of Helsinki（赫尔辛基大学）
Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data
Beijing University of Posts and Telecommunications（北京邮电大学）；Wuhan University（武汉大学）；Communication University of China（中国传媒大学）；University of Luxembourg（卢森堡大学）
