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Using PlanetScope-derived time-series elevation models and orthophotos to track glacier 3D dynamics in mid-latitude mountain regions

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Mapping Sciences and Remote Sensing 2025年第1期62卷

作者： Shengxi Gui Rongjun Qin a Geospatial Data Analytics Lab The Ohio State University Columbus OH USAb Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus OH USA a Geospatial Data Analytics Lab The Ohio State University Columbus OH USAb Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus OH USAc Department of Electrical and Computer Engineering The Ohio State University Columbus OH USAd Translational Data Analytics Institute The Ohio State University Columbus OH USA

ABSTRACTAssessing glacier surface height changes provides crucial insights into glacier mass loss and the impact of climate variability. Remote sensing images play a vital role in providing data points for monitoring. Oftentimes, the glacier height change observation is sparse with notable time intervals, yet it is unclear whether these sparse observations are within seasonal variations. Thus, data with high-temporal resolution are necessary to 1) establish sufficiently dense observations to achieve enhanced conclusions; 2) derive higher-level data, such as motion velocity, to assess the level of global warming and climate change. PlanetScope satellite constellations can provide global daily/weekly observations with a 4 m ground sample distance (GSD). In this work, we report a study on monitoring fast dynamic glaciers in mid-latitude mountain regions in America and Asia using the derived time-series 3D elevation models from PlanetScope. The study includes three sites: La Perouse Glacier (North America), Viedma Glacier (South America), and Skamri Glacier (Central Asia). Based on PlanetScope data, we derived near bi-monthly 4-m resolution 3D elevation models for the year of 2019–2023 using satellite stereo photogrammetry, to track the ice flow in 3D. The results can be used to decorrelate the factors from seasonable variation: the Viedma Glacier is observed thinner over time with a slower flow rate; although through a single and short-term observation, the thinning rate of La Perouse Glacier is shown accelerating, we did not observe evidence that La Perouse Glacier and Skamri Glacier have obvious thinning other than its seasonable variations within a few years of time frames (since the PlanetScope images were available). By focusing on the local surface motion variations, our study shows that the glacier velocity varies with regional climate, geography, and hydrology, with a notable 45-day lag response between climate factors and flow speed in Viedma and Skamri Glacie

关键词： Glacier dynamics 3D reconstruction satellite stereo matching glacier surface motion PlanetScope

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Towards a deep learning powered query engine for urban planning 21

Towards a deep learning powered query engine for urban plann...

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21st International Conference on Asian Language Processing, IALP 2017

作者： Shin, Teo Yon Zihong, Yuan Siong, Ng Wee Yangfan, Zhang Phangt, Valerie Data Analytics Department Institute for Infocomm Research ASTAR Singapore Singapore Information Systems and Geospatial Group Digital Planning Lab URA Singapore Singapore

ISBN: (纸本)9781538619803

Urban planning is crucial to sustainable growth. In order for the planners to make informed decisions, data from multiple sources have to be retrieved and cross-referenced efficiently. We discuss the implementation of a query engine which accepts natural language as input, using machine learning and NLP techniques namely word embedding, CNN, rule-based system and NER to produce accurate output enriched with geographical insights to facilitate the planning process. The query engine classifies the query into one of the planning domains, as well as determines the category, location and the size of buffer. Processed results are presented on the ePlanner, which is a map service on the GIS implemented by the Urban Redevelopment Authority (URA) of Singapore. © 2017 IEEE.

关键词： Deep learning

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A volumetric change detection framework using UAV oblique photogrammetry–a case study of ultra-high-resolution monitoring of progressive building collapse

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International Journal of Digital Earth 2021年第11期14卷 1705-1720页

作者： Ningli Xu Debao Huang Shuang Song Xiao Ling Chris Strasbaugh Alper Yilmaz Halil Sezen Rongjun Qin Geospatial Data Analytics Lab The Ohio State UniversityColumbusOHUSA Photogrammetric Computer Vision Lab The Ohio State UniversityColumbusOHUSA Department of Civil Environmental and Geodetic EngineeringThe Ohio State UniversityColumbusOHUSA Department of Electrical and Computer Engineering The Ohio State UniversityColumbusOHUSA Translational Data Analytics Institute The Ohio State UniversityColumbusOHUSA Engineering Technology Services The Ohio State UniversityColumbusOHUSA

In this paper,we present a case study that performs an unmanned aerial vehicle(UAV)based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition ***-temporal oblique photogrammetry images are collected with 3D point clouds generated at different stages of the *** geometric accuracy of the generated point clouds has been evaluated against both airborne and terrestrial LiDAR point clouds,achieving an average distance of 12 cm and 16 cm for roof and façade *** propose a hierarchical volumetric change detection framework that unifies multi-temporal UAV images for pose estimation(free of ground control points),reconstruction,and a coarse-to-fine 3D density change *** work has provided a solution capable of addressing change detection on full 3D time-series datasets where dramatic scene content changes are presented *** change detection results on the building demolition event have been evaluated against the manually marked ground-truth changes and have achieved an F-1 score varying from 0.78 to 0.92,with consistently high precision(0.92–0.99).Volumetric changes through the demolition progress are derived from change detection and have been shown to favorably reflect the qualitative and quantitative building demolition progression.

关键词： 3D change detection multitemporal data registration oblique photogrammetry

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Large-scale DSM registration via motion averaging

Large-scale DSM registration via motion averaging

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2024 ISPRS Technical Commission I Mid-term Symposium on Intelligent Sensing and Remote Sensing Application

作者： Xu, Ningli Qin, Rongjun Geospatial Data Analytics Lab The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

Generating wide-area digital surface models (DSMs) requires registering a large number of individual, and partially overlapped DSMs. This presents a challenging problem for a typical registration algorithm, since when a large number of observations from these multiple DSMs are considered, it may easily cause memory overflow. Sequential registration algorithms, although can significantly reduce the computation, are especially vulnerable for small overlapped pairs, leading to a large error accumulation. In this work, we propose a novel solution that builds the DSM registration task as a motion averaging problem: pair-wise DSMs are registered to build a scene graph, with edges representing relative poses between DSMs. Specifically, based on the grid structure of the large DSM, the pair-wise registration is performed using a novel nearest neighbor search method. We show that the scene graph can be optimized via an extremely fast motion average algorithm with O(N) complexity (N refers to the number of images). Evaluation of high-resolution satellite-derived DSM demonstrates significant improvement in computation and accuracy. © 2024 Ningli Xu.

关键词： Nearest neighbor search

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An Adaptive and Image-guided Fusion for Stereo Satellite Image Derived Digital Surface Models

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Journal of Geodesy and Geoinformation Science 2022年第4期5卷 1-9页

作者： Hessah ALBANWAN Rongjun QIN Department of Civil Engineering Kuwait UniversityKuwait 72504Kuwait Geospatial Data Analytics Lab The Ohio State UniversityColumbus 43210USA Department of Civil Environmental and Geodetic EngineeringThe Ohio State UniversityColumbus 43210USA Department of Electrical and Computer Engineering The Ohio State UniversityColumbus 43210USA Translational Data Analytics Institute The Ohio State UniversityColumbus 43210USA

The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo *** has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced,in which multiple DSMs are combined through computational approaches into a single,more accurate,and complete ***,accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different ***,we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation *** experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.

关键词： Digital Surface Model(DSM) DSM Fusion adaptive fusion satellite stereo images

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On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing data

arXiv

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arXiv 2023年

作者： Qin, Rongjun Zhang, Guixiang Tang, Yang The Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering The Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio state University Columbus United States The Geospatial Data Analytics Lab Department of Electrical and Computer Engineering The Ohio State University Columbus United States The Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering The Ohio State University Columbus United States

Recent deep learning-based methods outperform traditional learning methods on remote sensing (RS) semantic segmentation/classification tasks. However, they require large training datasets and are generally known for lack of transferability due to the highly disparate RS image content across different geographical regions. Yet, there is no comprehensive analysis of their transferability, i.e., to which extent a model trained on a source domain can be readily applicable to a target domain. Therefore, in this paper, we aim to investigate the raw transferability of traditional and deep learning (DL) models, as well as the effectiveness of domain adaptation (DA) approaches in enhancing the transferability of the DL models (adapted transferability). By utilizing four highly diverse RS datasets, we train six models with and without three DA approaches to analyze their transferability between these datasets quantitatively. Furthermore, we developed a straightforward method to quantify the transferability of a model using the spectral indices as a medium and have demonstrated its effectiveness in evaluating the model transferability at the target domain when the labels are unavailable. Our experiments yield several generally important yet not well-reported observations regarding the raw and adapted transferability. Moreover, our proposed label-free transferability assessment method is validated to be better than posterior model confidence. The findings can guide the future development of generalized RS learning models. The trained models are released under this link: https://***/GDAOSU/Transferability-Remote-Sensing. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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Image Fusion in Remote Sensing: An Overview and Meta Analysis

arXiv

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arXiv 2024年

作者： Albanwan, Hessah Qin, Rongjun Tang, Yang The Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering The Ohio State University Columbus United States The Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering The Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio state University Columbus United States

Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatio-temporally coherent images. It greatly facilitates downstream applications such as pan-sharpening, change detection, land-cover classification, etc. Yet, image fusion solutions are highly disparate to various remote sensing problems and thus are often narrowly defined in existing reviews as topical applications, such as pan-sharpening, and spatial-temporal image fusion. Considering that image fusion can be theoretically applied to any gridded data through pixel-level operations, in this paper, we expanded its scope by comprehensively surveying relevant works with a simple taxonomy: 1) many-to-one image fusion;2) many-to-many image fusion. This simple taxonomy defines image fusion as a mapping problem that turns either a single or a set of images into another single or set of images, depending on the desired coherence, e.g., spectral, spatial/resolution coherence, etc. We show that this simple taxonomy, despite the significant modality difference it covers, can be presented by a conceptually easy framework. In addition, we provide a meta-analysis to review the major papers studying the various types of image fusion and their applications over the years (from the 1980s to date), covering 5,926 peer-reviewed papers. Finally, we discuss the main benefits and emerging challenges to provide open research directions and potential future works. Copyright © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets

arXiv

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arXiv 2023年

作者： Elhashash, Mostafa Qin, Rongjun Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio state University Columbus United States

Many practical systems for image-based surface reconstruction employ a stereo/multi-stereo paradigm, due to its ability to scale for large scenes and its ease of implementation for out-of-core operations. In this process, multiple and abundant depth maps from stereo matching must be combined and fused into a single, consistent, and clean point cloud. However, the noises and outliers caused by stereo matching and the heterogenous geometric errors of the poses present a challenge for existing fusion algorithms, since they mostly assume Gaussian errors and predict fused results based on data from local spatial neighborhoods, which may inherit uncertainties from multiple depths resulting in lowered accuracy. In this paper, we propose a novel depth fusion paradigm, that instead of numerically fusing points from multiple depth maps, selects the best depth map per point, and combines them into a single and clean point cloud. This paradigm, called select-and-combine (SAC), is achieved through modeling the point level fusion using local Markov Netlets, a micro-network over point across neighboring views for depth/view selection, followed by a Netlets collapse process for point combination. The Markov Netlets are optimized such that they can inherently leverage spatial consistencies among depth maps of neighboring views, thus they can address errors beyond Gaussian ones. Our experiment results show that our approach outperforms existing depth fusion approaches by increasing the F1 score that considers both accuracy and completeness by 2.07% compared to the best existing method. Finally, our approach generates clearer point clouds that are 18% less redundant while with a higher accuracy before fusion. Copyright © 2023, The Authors. All rights reserved.

关键词： 3D modeling

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Trail configurational attributes and visitors’ spatial distribution in natural recreation area 12

Trail configurational attributes and visitors’ spatial dist...

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12th International Space Syntax Symposium, SSS 2019

作者： Zhai, Yujia Baran, Perver Korça College of Architecture and Urban Planning Big Data and Urban Spatial Analytics LAB Tongji University Shanghai China No.1239 Siping Road Yangpu District Shanghai200092 China Center for Geospatial Analytics College of Natural Resources College of Design North Carolina State University NC United States Box 7106 Jordan Hall RaleighNC27695-7106 United States

Visitors’ expectations for social interactions in natural recreation area is diverse;for instance, some seek solitude experience, while others may expect high level of social contacts. Better understanding of users’ spatial distribution is crucial to provide different desirable social conditions to fulfil users’ needs, which is essential to natural recreation area planning and management. This study aims at investigating how trail configurational attributes may impact the spatial distribution of visitors in natural recreation areas, such as urban neighbourhood parks and urban forest parks. Utilizing space syntax theory, we first developed an approach to measure trail configurational attributes in natural environment, and then applied the approach to two types of urban parks, i.e., a neighbourhood park and a forest park. The approach is based on the idea of a stroke-based network, and defines the boundaries of individual spaces (trails and activity areas) in a convex map as the actual boundaries of these spaces, i.e., each curving pathway segment is represented with a single convex space. We then measured visitors’ spatial distribution pattern using gate method and GPS trackers in two neighbourhood parks and two urban forest parks. The results indicate that pathway global integration value has a positive significant association with pathway use level in all the four parks, while pathway control value is also positively associated with trail use level in the two forest parks. These findings could provide direct implications for park design and management. For instance, in urban neighbourhood parks, we can locate features and facilities, such as display boards and flowers close to pathways with a high integration value, in order to serve more visitors. In park areas with a low integration and control value and tend to attract less users, we may arrange safety patrols to ensure park safety. For users who prefer to enjoy solitude, designers can incorporate peaceful areas

关键词： Parks

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Point Cloud Registration for LiDAR and Photogrammetric data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithms

arXiv

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arXiv 2023年

作者： Xu, Ningli Qin, Rongjun Song, Shuang Geospatial Data Analytics Lab The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

Three-dimensional (3D) point cloud registration is a fundamental step for many 3D modeling and mapping applications. Existing approaches are highly disparate in the data source, scene complexity, and application, therefore the current practices in various point cloud registration tasks are still ad-hoc processes. Recent advances in computer vision and deep learning have shown promising performance in estimating rigid/similarity transformation between unregistered point clouds of complex objects and scenes. However, their performances are mostly evaluated using a limited number of datasets from a single sensor (e.g. Kinect or RealSense cameras), lacking a comprehensive overview of their applicability in photogrammetric 3D mapping scenarios. In this work, we provide a comprehensive review of the state-of-the-art (SOTA) point cloud registration methods, where we analyze and evaluate these methods using a diverse set of point cloud data from indoor to satellite sources. The quantitative analysis allows for exploring the strengths, applicability, challenges, and future trends of these methods. In contrast to existing analysis works that introduce point cloud registration as a holistic process, our experimental analysis is based on its inherent two-step process to better comprehend these approaches including feature/keypoint-based initial coarse registration and dense fine registration through cloud-to-cloud (C2C) optimization. More than ten methods, including classic handcrafted, deep-learning-based feature correspondence, and robust C2C methods were tested. We observed that the success rate of most of the algorithms are fewer than 40% over the datasets we tested and there are still are large margin of improvement upon existing algorithms concerning 3D sparse corresopondence search, and the ability to register point clouds with complex geometry and occlusions. With the evaluated statistics on three datasets, we conclude the best-performing methods for each step and provi

关键词： Iterative methods

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