We propose a pipeline for combined multi-class object geolocation and height estimation from street level RGB imagery, which is considered as a single available input data modality. Our solution is formulated via Markov Random Field optimization with deterministic output. The proposed technique uses image metadata along with coordinates of objects detected in the image plane as found by a custom-trained Convolutional Neural Network. Computing the object height using our methodology, in addition to object geolocation, has negligible effect on the overall computational cost. Accuracy is demonstrated experimentally for water drains and road signs on which we achieve average elevation estimation error lower than 20cm.
Designing streetscapes from street-view imagery using diffusion models
Department of Urban and Regional Planning, University of Florida（城市与区域规划系，佛罗里达大学）；Singapore-MIT Alliance for Research and Technology Centre (SMART)（新加坡-麻省理工联合研究中心（SMART））；Department of Landscape Architecture and Urban Planning, Texas A&M University（景观建筑与城市规划系，德克萨斯大学安德森分校）；Department of Agronomy, University of Florida（农业系，佛罗里达大学）
Efficient Cross-Country Data Acquisition Strategy for ADAS via Street-View Imagery
CARIAD SE；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Esslingen University of Applied Sciences（埃森哲应用科学大学）；FZI Research Center for Information Technology（弗劳恩霍夫信息技术研究中心）
Space2Ground 2.0: A Multi-Source Dataset and Framework for Agricultural Monitoring through Fusion of Street-Level and Satellite Imagery
Operational Unit BEYOND Centre, IAASARS, National Observatory of Athens（雅典国家天文台IAASARS研究所BEYOND中心业务单元）；DHI Water & Environment（DHI水环境公司）；Artificial Intelligence Group, Wageningen University & Research（瓦赫宁根大学及研究中心人工智能组）
Cross-Modal Urban Sensing: Evaluating Sound-Vision Alignment Across Street-Level and Aerial Imagery
Department of Geography, University of South Carolina（南卡罗来纳大学地理系）；Department of Environmental Sciences, Emory University（埃默里大学环境科学系）；University of Canterbury（坎特伯雷大学）；School of Resource and Environmental Sciences, Wuhan University（武汉大学资源与环境科学学院）
Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery with Supplementary Materials
National University of Singapore（新加坡国立大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Grab Chronos S.R.L.（Grab Chronos有限责任公司）；Grabtaxi Holdings Pte. Ltd.（Grab出租车控股私人有限公司）
DM-KG: A Novel Method for Boosting Spatial Cognition of Vision-Language Models in Street View Imagery
Institute of Surveying and Mapping, Information Engineering University（信息工程大学测绘学院）；Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences（中国科学院地理科学与资源研究所）
Recov-Vision: Linking Street View Imagery and Vision-Language Models for Post-Disaster Recovery
UrbanResilience.AI Lab（UrbanResilience.AI 实验室）；Zachry Department of Civil and Environmental Engineering（Zachry 土木与环境工程系）；Texas A&M University（德克萨斯A&M大学）；Department of Earth, Marine and Environmental Sciences（地球、海洋与环境科学系）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models
Department of Geography, Texas A&M University（地理系，德克萨斯A&M大学）；Department of Geography, Pennsylvania State University（地理系，宾夕法尼亚州立大学）；Department of Geography and Sustainability, University of Tennessee（地理与可持续发展系，田纳西大学）；Spatial Sciences Institute, University of Southern California（空间科学研究所，南加州大学）
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）
DamageArbiter: A Multimodal Arbitration Framework for Disaster Damage Assessment from Street-View Imagery
organization= Department of Geography, Texas A\&M University , city= College Station , country= USA；organization= Department of Landscape Architecture \& Urban Planning, Texas A\&M University , city= College Station , country= USA；organization= Department of Industrial；Systems Engineering, University of Florida , city= Gainesville , country= USA；organization= Spatial Sciences Institute, University of Southern California , city= Los Angeles , country= USA；organization= Department of Geography；Sustainability, University of Tennessee , city= Knoxville , country= USA
OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery
Department of Architecture, National University of Singapore（建筑系，新加坡国立大学）；Department of Electrical and Computer Engineering, National University of Singapore（电气与计算机工程系，新加坡国立大学）；School of Artificial Intelligence, Shenzhen Technology University（人工智能学院，深圳科技大学）；Department of Real Estate, National University of Singapore（房地产系，新加坡国立大学）
SVII-3D: Advancing Roadside Infrastructure Inventory with Decimeter-level 3D Localization and Comprehension from Sparse Street Imagery
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China（信息工程测绘遥感国家重点实验室（LIESMARS），武汉大学）；Research Institute Ltd, Chengdu 610000, China（四川省公路规划设计研究有限公司）
Examining the Commitments and Difficulties Inherent in Multimodal Foundation Models for Street View Imagery
Guanghua School of Management, Peking University（北京大学光华管理学院）；The Bartlett School of Sustainable Construction, University College London（伦敦大学学院巴特lett可持续建筑学院）；School of Architecture, Tsinghua University（清华大学建筑学院）；School of Computing, The University of Georgia（佐治亚大学计算学院）；Department of Geography, University College London（伦敦大学学院地理系）；Department of Geography and the Environment, University of Texas at Austin（德克萨斯大学奥斯汀分校地理与环境系）
How does spatial structure affect psychological restoration? A method based on Graph Neural Networks and Street View Imagery
Hong Kong University of Science and Technology（香港科技大学）；Jiangnan University（江南大学）；The Chinese University of Hong Kong（香港中文大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Peking University（北京大学）
UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery
West Virginia University（西弗吉尼亚大学）；University at Buffalo, State University of New York（纽约州立大学布法罗分校）；The Hong Kong University of Science and Technology（香港科技大学）；Tongji University（同济大学）
