ISPRS Journal征稿|Street-level Imagery Analytics and Applications
ISPRS Journal of
Photogrammetry and Remote Sensing
Guest Editors
Fan Zhang (MIT, USA)
Jan Dirk Wegner (ETH Zurich, Switzerland)
Bisheng Yang (Wuhan University, China)
Yu Liu (Peking University, China)
Submission Deadline
31 December 2021
Aims and Scope
Street-level imagery refers to the georeferenced photographs taken along street networks, depicting the side view of urban streetscape from a similar view of human vision. With the rapid development of web mapping services, social media platforms, and vehicle-mounted intelligent hardware, street-level imagery, such as Google Street View and social media photos, are growing and blanketing every corner of cities. Compared with satellite imagery, street-level imagery is an alternative imagery data source not only describing the fine-grained physical environment, but also implying socioeconomic status and human dynamics in cities. Compared with traditional data sources describing cities, street-level imagery has some inherent advantages: easy access, cost effective, high spatiotemporal coverage, human perspective, objective and standardized view.
Benefited from the advances in deep learning and computer vision techniques, high-level semantic information can be now extracted from images automatically and efficiently. In the past few years, street-level imagery has been widely used in various fields, such as urban planning and design, autonomous vehicles, digital twins and city information modeling, public health, environmental criminology, tourism, real estate and energy consumption. It provides a new perspective to observe human settlement and to further understand the patterns of human-environment interactions.
To summarize this trend, this special issue invites submissions broadly contributing to street-level imagery analytics (street view images, geo-tagged photos, camera videos, etc.) Submitted manuscripts could cover but not limited to the following themes:
Street-level imagery collection, mapping and visualization for built-environment auditing
Data fusion of street-level imagery and remote sensing imagery
Methods in image feature extraction, classification and object detection for urban landscape observation (deep learning, computer vision, photogrammetry, etc.)
Image spatial analyses and applications in urban studies and social sensing (built-environment auditing, emission & air pollution, energy consumption, urban planning & design, transportation, human perception & dynamics, etc.)
Image spatial analyses and applications related to the United Nations Sustainable Development Goals (SDGs)
3D scene analysis and city information modeling
Approaches and applications of multi-source data fusion and integration in urban environments (Google Street Views, social media photos, LiDAR data, satellite imagery, night-time lights, points of interest, traditional survey data, etc.)
The full paper should be submitted to ISPRS Journal of Photogrammetry and Remote Sensing via its online submission system at https://www.editorialmanager.com/photo by December 31, 2021. When submitting, use “Street-level Imagery Analytics and Applications” to signify that the submission is part of this special issue. Papers must follow the instructions for authors to be found in the journal's guide for authors. For any inquiry, please contact Dr. Fan Zhang. Email: zhangfan@mit.edu.
素材来源：S3-Lab
材料整理：张 帆
内容排版：程天佑