A multi-task convolutional neural network for mega-city analysis using very high resolution satellite imagery and geospatial data
The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；School of Computer, Wuhan University（武汉大学计算机学院）
Mega-city analysis with very high resolution (VHR) satellite images has been drawing increasing interest in the fields of city planning and social investigation. It is known that accurate land-use, urban density, and population distribution information is the key to mega-city monitoring and environmental studies. Therefore, how to generate land-use, urban density, and population distribution maps at a fine scale using VHR satellite images has become a hot topic. Previous studies have focused solely on individual tasks with elaborate hand-crafted features and have ignored the relationship between different tasks. In this study, we aim to propose a universal framework which can: 1) automatically learn the internal feature representation from the raw image data; and 2) simultaneously produce fine-scale land-use, urban density, and population distribution maps. For the first target, a deep convolutional neural network (CNN) is applied to learn the hierarchical feature representation from the raw image data. For the second target, a novel CNN-based universal framework is proposed to process the VHR satellite images and generate the land-use, urban density, and population distribution maps. To the best of our knowledge, this is the first CNN-based mega-city analysis method which can process a VHR remote sensing image with such a large data volume. A VHR satellite image (1.2 m spatial resolution) of the center of Wuhan covering an area of 2606 km2 was used to evaluate the proposed method. The experimental results confirm that the proposed method can achieve a promising accuracy for land-use, urban density, and population distribution maps.
The research performs urban ecosystem analysis supported by ENVI GIS by integrated studies on land cover types and geospatial modeling of Taipei city. The paper deals with the role of anthropogenic pressure on the structure of the landscape and change of land cover types. Methods included assessment of the impact from anthropogenic activities on the natural ecosystems, evaluation of the rate and scale of landscape dynamics using remote sensing data and GIS. The research aims to assist environmentalists and city planners to evaluate strategies for specific objectives of urban development in Tai…
The Hong Kong Polytechnic University（香港理工大学）；Otto Poon C. F. Smart Cities Research Institute（香港理工大学潘燦良智慧城市研究院）
Timely and accurate land use mapping is a long-standing problem, which is critical for effective land and space planning and management. Due to complex and mixed use, it is challenging for accurate land use mapping from widely-used remote sensing images (RSI) directly, especially for high-density cities. To address this issue, in this paper, we propose a coarse-to-fine machine learning-based approach for parcel-level urban land use mapping, integrating multisource geospatial data, including RSI, points-of-interest (POI), and area-of-interest (AOI) data. Specifically, we first divide the city i…
Geospatial Representation Learning: A Survey from Deep Learning to The LLM Era
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；The University of New South Wales（新南威尔士大学）；Southwest Jiaotong University（西南交通大学）；Institute for Infocomm Research (I$^2$R), A*STAR（信息通信研究院（I$^2$R），A*STAR）
The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority of existing studies focus on road maps in cities. Inherently, existing algorithms are not guaranteed to work on unusual geospatial sites, such as an airport tarmac, pedestrianized paths and shortcuts, or animal migration routes, etc. Moreover, deep learning has not been explored well enough for such tasks. This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference fra…
Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Chinese University of Hong Kong（香港中文大学）；Auckland University of Technology（奥克兰理工大学）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
The geodatabase (vectorized data) nowadays becomes a rather standard digital city infrastructure; however, updating geodatabase efficiently and economically remains a fundamental and practical issue in the geospatial industry. The cost of building a geodatabase is extremely high and labor intensive, and very often the maps we use have several months and even years of latency. One solution is to develop more automated methods for (vectorized) geospatial data generation, which has been proven a difficult task in the past decades. An alternative solution is to first detect the differences between…
UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City
Shanghai Jiao Tong University（上海交通大学）；National University of Singapore（新加坡国立大学）；Meituan（美团）；The Chinese University of Hong Kong（香港中文大学）；Shanghai University（上海大学）；University of Oxford（牛津大学）
Faculty of Electrical Engineering and Computing（电气工程与计算学院）；University of Zagreb（萨格勒布大学）
Recent progress of deep image classification models has provided great potential to improve state-of-the-art performance in related computer vision tasks. However, the transition to semantic segmentation is hampered by strict memory limitations of contemporary GPUs. The extent of feature map caching required by convolutional backprop poses significant challenges even for moderately sized Pascal images, while requiring careful architectural considerations when the source resolution is in the megapixel range. To address these concerns, we propose a novel DenseNet-based ladder-style architecture …
The research scope of this paper is to apply spatial object based image analysis (OBIA) method for processing panchromatic multispectral image covering study area of Brussels for urban mapping. The aim is to map different land cover types and more specifically, built-up areas from the very high resolution (VHR) satellite image using OBIA approach. A case study covers urban landscapes in the eastern areas of the city of Brussels, Belgium. Technically, this research was performed in eCognition raster processing software demonstrating excellent results of image segmentation and classification. Th…
University of Melbourne（墨尔本大学）；University of the Sunshine Coast（阳光海岸大学）
The confluence of recent advances in availability of geospatial information, computing power, and artificial intelligence offers new opportunities to understand how and where our cities differ or are alike. Departing from a traditional `top-down' analysis of urban design features, this project analyses millions of images of urban form (consisting of street view, satellite imagery, and street maps) to find shared characteristics. A (novel) neural network-based framework is trained with imagery from the largest 1692 cities in the world and the resulting models are used to compare within-city loc…
Envisioning global urban development with satellite imagery and generative AI
Singapore–MIT Alliance for Research and Technology Centre (SMART)（新加坡-麻省理工学院研究与技术联盟中心）；Department of Urban Planning, Tsinghua University（清华大学城市规划系）；Department of Urban Studies and Planning, Massachusetts Institute of Technology（麻省理工学院城市研究与规划系）；College of Urban and Environmental Sciences, Peking University（北京大学城市与环境学院）；Department of Urban and Regional Planning, University of Florida（佛罗里达大学城市与区域规划系）；Stanford Institute for Human-Centered Artificial Intelligence, Stanford University（斯坦福大学以人为本人工智能研究所）
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images
The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；Otto Poon Charitable Foundation Smart Cities Research Institute, The Hong Kong Polytechnic University（香港理工大学潘奥本慈善基金会智慧城市研究院）
Large collections of geo-referenced panoramic images are freely available for cities across the globe, as well as detailed maps with location and meta-data on a great variety of urban objects. They provide a potentially rich source of information on urban objects, but manual annotation for object detection is costly, laborious and difficult. Can we utilize such multimedia sources to automatically annotate street level images as an inexpensive alternative to manual labeling? With the PanorAMS framework we introduce a method to automatically generate bounding box annotations for panoramic images…
