Multi-dimension Geospatial feature learning for urban region function recognition
Beijing University of Posts and Telecommunications（北京邮电大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；City University of Hong Kong（香港城市大学）
Urban region function recognition plays a vital character in monitoring and managing the limited urban areas. Since urban functions are complex and full of social-economic properties, simply using remote sensing~(RS) images equipped with physical and optical information cannot completely solve the classification task. On the other hand, with the development of mobile communication and the internet, the acquisition of geospatial big data~(GBD) becomes possible. In this paper, we propose a Multi-dimension Feature Learning Model~(MDFL) using high-dimensional GBD data in conjunction with RS images for urban region function recognition. When extracting multi-dimension features, our model considers the user-related information modeled by their activity, as well as the region-based information abstracted from the region graph. Furthermore, we propose a decision fusion network that integrates the decisions from several neural networks and machine learning classifiers, and the final decision is made considering both the visual cue from the RS images and the social information from the GBD data. Through quantitative evaluation, we demonstrate that our model achieves overall accuracy at 92.75, outperforming the state-of-the-art by 10 percent.
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…
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 betwe…
University of Southern California（南加州大学）；University of Minnesota（明尼苏达大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）
Thousands of scanned historical topographic maps contain valuable information covering long periods of time, such as how the hydrography of a region has changed over time. Efficiently unlocking the information in these maps requires training a geospatial objects recognition system, which needs a large amount of annotated data. Overlapping geo-referenced external vector data with topographic maps according to their coordinates can annotate the desired objects' locations in the maps automatically. However, directly overlapping the two datasets causes misaligned and false annotations because the …
Stanford University（斯坦福大学）；Renmin University of China（中国人民大学）
Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods. Existing works relied on either inter- or intra-region connectivities to generate neighborhood representations but failed to fully utilize the informative yet heterogeneous data within neighborhoods. In this work, we propose Urban2Vec, an unsupervised multi-modal framework which incorporates both street view imagery and point-of-interest (POI) data to learn neighborhood embeddings. Specifically, we use a convolutional neural network to extract …
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）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Microsoft（微软）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make the application of deep learning methodology to remotely sensed data nontrivial. For example, satell…
We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables the translation of one geospatial dataset…
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…
German Aerospace Center（德国航空航天中心）；IBM T.J. Watson Research Center（IBM T.J.沃森研究中心）
A key challenge of supervised learning is the availability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. It is based on rasterized statistical features extracted from surveys such as e.g. LiDAR measurements. Using simple combinations of the rasterized statistical layers, it is demonstrated that multiple classes can be generated at accuracies of ~0.9. As proof of concept, we utilize the big geo-data platform IBM PAIRS to dynamically generate such labels in dense urban areas with multiple land cover classes. The general method …
Land use as contained in geospatial databases constitutes an essential input for different applica-tions such as urban management, regional planning and environmental monitoring. In this paper, a hierarchical deep learning framework is proposed to verify the land use information. For this purpose, a two-step strategy is applied. First, given high-resolution aerial images, the land cover information is determined. To achieve this, an encoder-decoder based convolutional neural net-work (CNN) is proposed. Second, the pixel-wise land cover information along with the aerial images serves as input f…
School of Information Technology, Halmstad University（哈姆斯塔德大学信息技术学院）；RISE Viktoria
Action and intention recognition of pedestrians in urban settings are challenging problems for Advanced Driver Assistance Systems as well as future autonomous vehicles to maintain smooth and safe traffic. This work investigates a number of feature extraction methods in combination with several machine learning algorithms to build knowledge on how to automatically detect the action and intention of pedestrians in urban traffic. We focus on the motion and head orientation to predict whether the pedestrian is about to cross the street or not. The work is based on the Joint Attention for Autonomou…
With the acceleration of the urban expansion, urban change detection (UCD), as a significant and effective approach, can provide the change information with respect to geospatial objects for dynamical urban analysis. However, existing datasets suffer from three bottlenecks: (1) lack of high spatial resolution images; (2) lack of semantic annotation; (3) lack of long-range multi-temporal images. In this paper, we propose a large scale benchmark dataset, termed Hi-UCD. This dataset uses aerial images with a spatial resolution of 0.1 m provided by the Estonia Land Board, including three-time phas…
Building and Road Recognition in Dense Urban Informal Settlements: A Dataset and Benchmark
Guangdong Provincial Project（广东省项目）；Guangzhou-HKUST(GZ) Joint Funding Program（广州-香港科技大学联合基金计划）；AI Research and Learning Base of Urban Culture Project（城市文化AI研究与学习基地项目）
TraRA: Trajectory-level Recognition Aggregation for Video Text Spotting in Urban Surveillance
RIKEN（日本理化学研究所）；Hanoi University of Science and Technology（河内科学技术大学）；Nagoya University（名古屋大学）；Lawrence Technological University（劳伦斯技术大学）；Ritsumeikan University（立命馆大学）
Automated Quality Assessment of Geospatial Vector Data: A GeoAI Approach using Spatial Representation Learning
Department of Geography, National University of Singapore, Singapore（新加坡国立大学地理系）；University of Southern California（南加州大学）；Department of Architecture, National University of Singapore, Singapore（新加坡国立大学建筑系）；Department of Real Estate, National University of Singapore, Singapore（新加坡国立大学房地产系）
Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as higher-level concepts such as land use classes (which encode expert understanding of socio-economic end uses). This kind of data is expensive and labor-intensive to obtain, which limits its availability (particularly in developing countries). We analyze patterns in land use in urban neighborhoods using large-scale satellite imagery data (which is available w…
University of Kentucky（肯塔基大学）；Indiana University Bloomington（印第安纳大学伯明顿分校）
We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead and ground-level images in an end-to-end trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level images into a dense feature map. The output of this network is a dense estimate of the geospatial function in the form of a pixel-level labeling of the overhead image. To evaluate our approach, we created a large dataset of overhead and groun…
Building age estimation based on widely available multi-source1 geospatial data
The University of Hong Kong（香港大学）；Institute for Climate and Carbon Neutrality, the University of Hong Kong（香港大学气候与碳中和研究所）；The University of Hong Kong Shenzhen Institute of Research and Innovation（香港大学深圳研究院）；School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）；Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University（深圳大学建筑与城市规划学院智慧城市研究院）；College of Urban and Environmental Sciences, Academy of Frontier Interdisciplinary Research, Central China Normal University（华中师范大学城市与环境科学学院前沿交叉研究院）；University of North Carolina（北卡罗来纳大学）；Chulalongkorn University（朱拉隆功大学）
Mapping Urban Villages in China: Progress and Challenges
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shenzhen University（深圳大学）；Technical University of Munich（慕尼黑工业大学）；National University of Singapore（新加坡国立大学）
L2RSI: Cross-view LiDAR-based Place Recognition for Large-scale Urban Scenes via Remote Sensing Imagery
Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China（福建智能城市感知与计算重点实验室，厦门大学，中国）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China（多媒体可信感知与高效计算重点实验室，中华人民共和国教育部，厦门大学，中国）；University of Science and Technology of China, China（中国科学技术大学，中国）
Technical University of Denmark（丹麦技术大学）；Otovo AS（Otovo公司）
The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data from the Danish national building registry. We explore the performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach as recent works.…
A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake
Department of Electrical, Computer and Biomedical Engineering, University of Pavia（帕维亚大学电气、计算机与生物医学工程系）；Department of Engineering, University of Sannio（萨诺尼奥大学工程系）；Italian Space Agency (ASI)（意大利航天局）
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…
WHU-PCPR: A cross-platform heterogeneous point cloud dataset for place recognition in complex urban scenes
School of Advanced Manufacturing, Nanchang University（南昌大学先进制造学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）；Department of Geomatics Engineering, University of Calgary（卡尔加里大学测绘工程系）；School of Earth Sciences and Engineering, Hohai University（河海大学地球科学与工程学院）；School of Geography and Environment, Jiangxi Normal University（江西师范大学地理与环境学院）；School of Information Engineering, Nanchang University（南昌大学信息工程学院）；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）；Hubei Luojia Laboratory（湖北省珞珈实验室）
