An automated machine learning framework for geospatial data named PAIRS AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform. The framework simplifies the development of industrial machine learning solutions leveraging geospatial data to the extent that the user inputs are minimized to merely a text file containing labeled GPS coordinates. PAIRS AutoGeo automatically gathers required data at the location coordinates, assembles the training data, performs quality check, and trains multiple machine learning models for subsequent deployment. The framework is validated using a realistic industrial use case of tree species classification. Open-source tree species data are used as the input to train a random forest classifier and a modified ResNet model for 10-way tree species classification based on aerial imagery, which leads to an accuracy of $59.8\%$ and $81.4\%$, respectively. This use case exemplifies how PAIRS AutoGeo enables users to leverage machine learning without extensive geospatial expertise.
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 …
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）
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）
iWatchRoad: Scalable Detection and Geospatial Visualization of Potholes for Smart Cities
National Institute of Science Education and Research (NISER), An OCC of Homi Bhabha National Institute（国家科学教育研究所（NISER），霍米·巴哈瓦国家研究所的下属机构）；Silicon University（硅大学）
iWatchRoadv2: Pothole Detection, Geospatial Mapping, and Intelligent Road Governance
National Institute of Science Education and Research (NISER), An OCC of HBNI（国家科学教育与研究 institute（NISER），HBNI 的一个办事处）；Silicon Institute of Technology（硅技术学院）
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）
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…
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente（特文特大学地理信息科学与地球观测学院）；Swinburne Business School, Swinburne University of Technology（斯威本科技大学斯威本商学院）
Unmanned aerial vehicles (UAV) are evolving as an alternative tool to acquire land tenure data. UAVs can capture geospatial data at high quality and resolution in a cost-effective, transparent and flexible manner, from which visible land parcel boundaries, i.e., cadastral boundaries are delineable. This delineation is to no extent automated, even though physical objects automatically retrievable through image analysis methods mark a large portion of cadastral boundaries. This study proposes (i) a workflow that automatically extracts candidate cadastral boundaries from UAV orthoimages and (ii) …
University of Southern California（南加州大学）；Institute for Creative Technologies（创意技术研究所）；Army Futures Command（陆军未来司令部）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；US Army’s Synthetic Training Environment（美国陆军合成训练环境）
In the globalized economic world, it has become important to understand the purpose behind infrastructural and construction initiatives occurring within developing regions of the earth. This is critical when the financing for such projects must be coming from external sources, as is occurring throughout major portions of the African continent. When it comes to imagery analysis to research these regions, ground and aerial coverage is either non-existent or not commonly acquired. However, imagery from a large number of commercial, private, and government satellites have produced enormous dataset…
University of Stuttgart（斯图加特大学）；Leibniz University Hannover（汉诺威莱布尼茨大学）；University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Delft University of Technology（代尔夫特理工大学）
Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such datasets are quite scarce. Within this paper, we aim to alleviate this issue by introducing a new annotated 3D dataset that is unique in three ways: i) The dataset consists of both an Unmanned Aerial Vehicle (UAV) laser scanning point cloud and a 3D textured mesh. ii) The point cloud features a mean point density of a…
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder（科罗拉多大学博尔德分校环境科学合作研究所）；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Minnesota（明尼苏达大学）；Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；University of Southern California（南加州大学）
Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integra…
