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Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R2 of 0.73 and RPD of 1.91 for SOM, R2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry.
Xiaoyu Yang; Nisha Bao; Wenwen Li; Shanjun Liu; Yanhua Fu; Yachun Mao. Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry. Sensors 2021, 21, 3919 .
AMA StyleXiaoyu Yang, Nisha Bao, Wenwen Li, Shanjun Liu, Yanhua Fu, Yachun Mao. Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry. Sensors. 2021; 21 (11):3919.
Chicago/Turabian StyleXiaoyu Yang; Nisha Bao; Wenwen Li; Shanjun Liu; Yanhua Fu; Yachun Mao. 2021. "Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry." Sensors 21, no. 11: 3919.
This paper introduces a new GeoAI solution to support automated mapping of global craters on the Mars surface. Traditional crater detection algorithms suffer from the limitation of working only in a semiautomated or multi-stage manner, and most were developed to handle a specific dataset in a small subarea of Mars’ surface, hindering their transferability for global crater detection. As an alternative, we propose a GeoAI solution based on deep learning to tackle this problem effectively. Three innovative features are integrated into our object detection pipeline: (1) a feature pyramid network is leveraged to generate feature maps with rich semantics across multiple object scales; (2) prior geospatial knowledge based on the Hough transform is integrated to enable more accurate localization of potential craters; and (3) a scale-aware classifier is adopted to increase the prediction accuracy of both large and small crater instances. The results show that the proposed strategies bring a significant increase in crater detection performance than the popular Faster R-CNN model. The integration of geospatial domain knowledge into the data-driven analytics moves GeoAI research up to the next level to enable knowledge-driven GeoAI. This research can be applied to a wide variety of object detection and image analysis tasks.
Chia-Yu Hsu; Wenwen Li; Sizhe Wang. Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection. Remote Sensing 2021, 13, 2116 .
AMA StyleChia-Yu Hsu, Wenwen Li, Sizhe Wang. Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection. Remote Sensing. 2021; 13 (11):2116.
Chicago/Turabian StyleChia-Yu Hsu; Wenwen Li; Sizhe Wang. 2021. "Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection." Remote Sensing 13, no. 11: 2116.
In spatial analysis applications, measuring the shape similarity of polygons is crucial for polygonal object retrieval and shape clustering. As a complex cognition process, measuring shape similarity should involve finding the difference between polygons, as objects in observation, in terms of visual perception and the differences of the regions, boundaries, and structures formed by the polygons from a mathematical point of view. In existing approaches, the shape similarity of polygons is calculated by only comparing their mathematical characteristics while not taking human perception into consideration. Aiming to solve this problem, we use the features of context and texture of polygons, since they are basic visual perception elements, to fit the cognition purpose. In this paper, we propose a contour diffusion method for the similarity measurement of polygons. By converting a polygon into a grid representation, the contour feature is represented as a multiscale statistic feature, and the region feature is transformed into condensed grid of context features. Instead of treating shape similarity as a distance between two representations of polygons, the proposed method observes similarity as a correlation between textures extracted by shape features. The experiments show that the accuracy of the proposed method is superior to that of the turning function and Fourier descriptor.
Hongchao Fan; Zhiyao Zhao; Wenwen Li. Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors. ISPRS International Journal of Geo-Information 2021, 10, 279 .
AMA StyleHongchao Fan, Zhiyao Zhao, Wenwen Li. Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors. ISPRS International Journal of Geo-Information. 2021; 10 (5):279.
Chicago/Turabian StyleHongchao Fan; Zhiyao Zhao; Wenwen Li. 2021. "Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors." ISPRS International Journal of Geo-Information 10, no. 5: 279.
Recent interest in geospatial artificial intelligence (GeoAI) has fostered a wide range of applications using artificial intelligence (AI), especially deep learning for geospatial problem solving. Major challenges, however, such as a lack of training data and ignorance of spatial principles and spatial effects in AI model design remain, significantly hindering the in-depth integration of AI with geospatial research. This article reports our work in developing a cutting-edge deep learning model that enables object detection, especially of natural features, in a weakly supervised manner. Our work has made three innovative contributions: First, we present a novel method of object detection using only weak labels. This is achieved by developing a spatially explicit model according to Tobler’s first law of geography to enable weakly supervised object detection. Second, we integrate the idea of an attention map into the deep learning–based object detection pipeline and develop a multistage training strategy to further boost detection performance. Third, we have successfully applied this model for the automated detection of Mars impact craters, the inspection of which often involved tremendous manual work prior to our solution. Our model is generalizable for detecting both natural and man-made features on the surface of the Earth and other planets. This research has made a major contribution to the enrichment of the theoretical and methodological body of knowledge of GeoAI.
Wenwen Li; Chia-Yu Hsu; Maosheng Hu. Tobler’s First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection under Weak Supervision. Annals of the American Association of Geographers 2021, 1 -19.
AMA StyleWenwen Li, Chia-Yu Hsu, Maosheng Hu. Tobler’s First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection under Weak Supervision. Annals of the American Association of Geographers. 2021; ():1-19.
Chicago/Turabian StyleWenwen Li; Chia-Yu Hsu; Maosheng Hu. 2021. "Tobler’s First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection under Weak Supervision." Annals of the American Association of Geographers , no. : 1-19.
This paper synthesizes vulnerability, risk, resilience, and sustainability (VRRS) in a way that can be used for decision evaluations about sustainable systems, whether such systems are called coupled natural–human systems, social–ecological systems, coupled human–environment systems, and/or hazards influencing global environmental change, all considered geospatial open systems. Evaluations of V-R-R-S as separate concepts for complex decision problems are important, but more insightful when synthesized for improving integrated decision priorities based on trade-offs of V-R-R-S objectives. A synthesis concept, called VRRSability, provides an overarching perspective that elucidates Tier 2 of a previously developed four-tier framework for organizing measurement-informed ontology and epistemology for sustainability information representation (MOESIR). The new synthesis deepens the MOESIR framework to address VRRSability information representation and clarifies the Tier 2 layer of abstraction. This VRRSability synthesis, composed of 13 components (several with sub-components), offers a controlled vocabulary as the basis of a conceptual framework for organizing workflow assessment and intervention strategies as part of geoinformation decision support software. Researchers, practitioners, and machine learning algorithms can use the vocabulary results for characterizing functional performance relationships between elements of geospatial open systems and the computing technology systems used for evaluating them within a context of complex sustainable systems.
Timothy Nyerges; John Gallo; Steven Prager; Keith Reynolds; Philip Murphy; Wenwen Li. Synthesizing Vulnerability, Risk, Resilience, and Sustainability into VRRSability for Improving Geoinformation Decision Support Evaluations. ISPRS International Journal of Geo-Information 2021, 10, 179 .
AMA StyleTimothy Nyerges, John Gallo, Steven Prager, Keith Reynolds, Philip Murphy, Wenwen Li. Synthesizing Vulnerability, Risk, Resilience, and Sustainability into VRRSability for Improving Geoinformation Decision Support Evaluations. ISPRS International Journal of Geo-Information. 2021; 10 (3):179.
Chicago/Turabian StyleTimothy Nyerges; John Gallo; Steven Prager; Keith Reynolds; Philip Murphy; Wenwen Li. 2021. "Synthesizing Vulnerability, Risk, Resilience, and Sustainability into VRRSability for Improving Geoinformation Decision Support Evaluations." ISPRS International Journal of Geo-Information 10, no. 3: 179.
Ocean-moored buoys play an important role in global ocean environment monitoring. Motivated by building a sustainable ocean buoy observational network, a spatial optimization approach is proposed to site buoy stations to maximize spatial monitoring efficiency (SME). To achieve this goal, a non-linear, continuous maximum coverage location model named CMCP-Ocean was established, associated with a measurement method of the SME. Meanwhile, a heuristic framework based on the particle swarm optimization (PSO) algorithm was built to solve the CMCP-Ocean model, and optimization strategies including the multi-core parallel computing strategy, the particle velocity updating strategy based on spatial matching, and two potential station selection strategies related to the centroid-based random radiation method (CRRM) and random grid division method (RGDM) were established to improve computing performance. The effectiveness and efficiency of the PSO-based algorithm and the CMCP-Ocean model were verified by a series of experiments; the proposed computing schema named PSO-for-CMCP-Ocean has also proven to be practical and efficient. Finally, the PSO-for-CMCP-Ocean was applied to the buoy station selection of water mass monitoring in the Laizhou Bay of China, and a multi-scale sustainable site planning solution is reported.
Miaomiao Song; Shixuan Liu; Wenqing Li; Shizhe Chen; Wenwen Li; Keke Zhang; Dingfeng Yu; Lin Liu; Xiaoyan Wang. A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored Buoys. IEEE Access 2021, 9, 32249 -32262.
AMA StyleMiaomiao Song, Shixuan Liu, Wenqing Li, Shizhe Chen, Wenwen Li, Keke Zhang, Dingfeng Yu, Lin Liu, Xiaoyan Wang. A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored Buoys. IEEE Access. 2021; 9 ():32249-32262.
Chicago/Turabian StyleMiaomiao Song; Shixuan Liu; Wenqing Li; Shizhe Chen; Wenwen Li; Keke Zhang; Dingfeng Yu; Lin Liu; Xiaoyan Wang. 2021. "A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored Buoys." IEEE Access 9, no. : 32249-32262.
Incidence angles (θ ) and polarization modes (p) are the essential factors for detecting earthquake (EQ)-related microwave radiation anomalies. However, their influences are still not well studied. In this article, research was carried out to simulate the observation of satellite on EQ under different θ and p. The microwave observation experiments were performed on granite samples pressed with θ from 0° to 30°, as well as vertical (v) to horizontal (h) polarizations under weak radiation environment. It was found that the stress-induced changes (Δ TB) in the samples' microwave brightness temperature (TB) are anisotropic and polarized. It was observed that the trends of Δ TB may be reversed under different θ (p), with a probability of 57.14% (14.29%). The magnitude of Δ TB at h is also prone to greater changes than that at v, with a probability of 67.86%. Theoretical analysis was performed based on crystal optics, Fresnel formulas, and dielectric physics. This phenomenon may relate to the combined action of piezoelectric effect, extension and contraction of ionic bonds, and turning-direction polarization. The sensitivity of TB to the change in permittivity was analyzed by theoretical simulation in order to reveal the effects of modification in θ and p on the TB change in stress of rock. For rocks with permittivity ϵ = 7 - i0.3, the most and least sensitive θ are near 52° at h polarization and 69° at v polarization, respectively. Finally, the significance of these results was discussed.
Xiang Gao; Shanjun Liu; Lixin Wu; Wenwen Li; Jianwei Huang; Wenfei Mao. The Anisotropy and Polarization of Stress-Induced Microwave Radiation Changes in Granite and Their Significance for Seismic Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 7603 -7617.
AMA StyleXiang Gao, Shanjun Liu, Lixin Wu, Wenwen Li, Jianwei Huang, Wenfei Mao. The Anisotropy and Polarization of Stress-Induced Microwave Radiation Changes in Granite and Their Significance for Seismic Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (9):7603-7617.
Chicago/Turabian StyleXiang Gao; Shanjun Liu; Lixin Wu; Wenwen Li; Jianwei Huang; Wenfei Mao. 2020. "The Anisotropy and Polarization of Stress-Induced Microwave Radiation Changes in Granite and Their Significance for Seismic Remote Sensing." IEEE Transactions on Geoscience and Remote Sensing 59, no. 9: 7603-7617.
John P. Wilson; Kevin Butler; Song Gao; Yingjie Hu; Wenwen Li; Dawn J. Wright. A Five-Star Guide for Achieving Replicability and Reproducibility When Working with GIS Software and Algorithms. Annals of the American Association of Geographers 2020, 111, 1311 -1317.
AMA StyleJohn P. Wilson, Kevin Butler, Song Gao, Yingjie Hu, Wenwen Li, Dawn J. Wright. A Five-Star Guide for Achieving Replicability and Reproducibility When Working with GIS Software and Algorithms. Annals of the American Association of Geographers. 2020; 111 (5):1311-1317.
Chicago/Turabian StyleJohn P. Wilson; Kevin Butler; Song Gao; Yingjie Hu; Wenwen Li; Dawn J. Wright. 2020. "A Five-Star Guide for Achieving Replicability and Reproducibility When Working with GIS Software and Algorithms." Annals of the American Association of Geographers 111, no. 5: 1311-1317.
Michael F. Goodchild; A. Stewart Fotheringham; Peter Kedron; Wenwen Li. Introduction: Forum on Reproducibility and Replicability in Geography. Annals of the American Association of Geographers 2020, 111, 1271 -1274.
AMA StyleMichael F. Goodchild, A. Stewart Fotheringham, Peter Kedron, Wenwen Li. Introduction: Forum on Reproducibility and Replicability in Geography. Annals of the American Association of Geographers. 2020; 111 (5):1271-1274.
Chicago/Turabian StyleMichael F. Goodchild; A. Stewart Fotheringham; Peter Kedron; Wenwen Li. 2020. "Introduction: Forum on Reproducibility and Replicability in Geography." Annals of the American Association of Geographers 111, no. 5: 1271-1274.
A cornerstone of the scientific method, the ability to reproduce and replicate the results of research has gained widespread attention across the sciences in recent years. A corresponding burst of energy into how to make research more reproducible and replicable has led to numerous innovations. This article outlines some of the opportunities for geospatial researchers to contribute to and learn from the broader reproducibility literature. We review practices developed in related disciplines to improve the reproducibility and replicability of research and outline current efforts to adapt those practices to geospatial analyses. The article then highlights the open questions, opportunities, and potential new directions in geospatial research related to R&R. We stress that the path ahead will likely require a mixture of computational, geospatial, and behavioral research that collectively addresses the many sides of reproducibility and replicability issues.
Peter Kedron; Wenwen Li; Stewart Fotheringham; Michael Goodchild. Reproducibility and replicability: opportunities and challenges for geospatial research. International Journal of Geographical Information Science 2020, 35, 427 -445.
AMA StylePeter Kedron, Wenwen Li, Stewart Fotheringham, Michael Goodchild. Reproducibility and replicability: opportunities and challenges for geospatial research. International Journal of Geographical Information Science. 2020; 35 (3):427-445.
Chicago/Turabian StylePeter Kedron; Wenwen Li; Stewart Fotheringham; Michael Goodchild. 2020. "Reproducibility and replicability: opportunities and challenges for geospatial research." International Journal of Geographical Information Science 35, no. 3: 427-445.
Developing spatial analytical methods as open source libraries is an important endeavor to enable open and replicable science. However, despite the fact that large geospatial data and geospatial cyberinfrastructure (GeoCI) resources are becoming available, many libraries and toolkits are only initialized and designed for analytics in a desktop environment. Coupling spatial analytical functionality with big data and high-performance computing will result in immediate benefits for multidisciplinary research in terms of addressing challenging socioeconomic and environmental problems, as well as supporting remote collaboration between participants from physically distributed research groups, and assisting informed decision-making. In this article, we present the design and implementation of a general workflow to integrate state-of-the-art open source libraries with GeoCI resources. We also solve various interoperability and replicability issues that arise during the implementation process. The popular open source Python Spatial Analysis Library (PySAL) was selected to build the interoperable Web service, WebPySAL, which was then successfully integrated in GeoCI. With this integration between spatial analytics and cyberinfrastructure, the new GeoCI platform provides easy-to-use, efficient, and interactive exploratory spatial analysis functions to public users. The GeoCI capability is demonstrated through two regional economic case studies of (1) evaluating global spatial autocorrelation and identifying local clusters in the spatial pattern of median household incomes for US counties (with global and local Moran’s I statistics) and (2) modeling the space-time dynamics of per capita incomes at the state level (with spatial Markov statistics).
Hu Shao; Wenwen Li; Wei Kang; Sergio J. Rey. When Spatial Analytics Meets Cyberinfrastructure: an Interoperable and Replicable Platform for Online Spatial-Statistical-Visual Analytics. Journal of Geovisualization and Spatial Analysis 2020, 4, 1 -16.
AMA StyleHu Shao, Wenwen Li, Wei Kang, Sergio J. Rey. When Spatial Analytics Meets Cyberinfrastructure: an Interoperable and Replicable Platform for Online Spatial-Statistical-Visual Analytics. Journal of Geovisualization and Spatial Analysis. 2020; 4 (2):1-16.
Chicago/Turabian StyleHu Shao; Wenwen Li; Wei Kang; Sergio J. Rey. 2020. "When Spatial Analytics Meets Cyberinfrastructure: an Interoperable and Replicable Platform for Online Spatial-Statistical-Visual Analytics." Journal of Geovisualization and Spatial Analysis 4, no. 2: 1-16.
Generally, ground stress accumulates in the process of earthquake (EQ) preparation. The change trend of microwave brightness temperature (TB) of rock with stress change is an important factor for the understanding of the microwave anomalies associated with EQs. However, it is not yet clear whether the downtrend of rocks' TB is associated with increased stress. To confirm this, in this article, the instantaneous field of view of the microwave radiometer was identified first. Then, the microwave observation experiments were conducted on granite samples at 6.6 GHz under cyclic loading and outdoor conditions with weak background radiation. It was found that besides uptrend and fluctuation, the downtrend of granite samples' TB also correlates with stress, with an occurrence probability of 47.62% and a maximum rate of -0.038 K/MPa. The variation trends of TB with stress are not uniform across different areas of the same sample. To reveal the cause of this phenomenon, the permittivity of single-crystal quartz, one of the main mineral compositions of granite, was measured when it was under compassion loading at the direction perpendicular or parallel to the optical axis. For quartz, the real part of the permittivity rises (falls) when the two directions are perpendicular (parallel), causing the TB to fall (rise). The optic axes of minerals are randomly distributed in granite samples, which make the variation in the permittivity of minerals also random, thereby resulting in the nonuniformity of stress-induced TB variation in granite samples. Finally, the implications of these results were discussed.
Xiang Gao; Shanjun Liu; Lixin Wu; Wenfei Mao; Wenwen Li; Jianwei Huang. The Variation in Microwave Brightness Temperature of Granite Pressed Under Weak Background Radiation. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 1369 -1381.
AMA StyleXiang Gao, Shanjun Liu, Lixin Wu, Wenfei Mao, Wenwen Li, Jianwei Huang. The Variation in Microwave Brightness Temperature of Granite Pressed Under Weak Background Radiation. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (2):1369-1381.
Chicago/Turabian StyleXiang Gao; Shanjun Liu; Lixin Wu; Wenfei Mao; Wenwen Li; Jianwei Huang. 2020. "The Variation in Microwave Brightness Temperature of Granite Pressed Under Weak Background Radiation." IEEE Transactions on Geoscience and Remote Sensing 59, no. 2: 1369-1381.
In the face of climate change and other environmental challenges, an increasing number of cities are turning to land design to enhance urban sustainability. Land system architecture (LSA)—which examines the role of size, shape, distribution, and connectivity of land units in relation to the system’s social-environmental dynamics—can be a useful perspective for examining how land contributes to the social and environmental aspects of urban sustainability. There are two gaps, however, that prevent LSA from fully contributing to urban sustainability dialogues. First, it is not well understood how urban design goals, as expressed by urban planners and other practitioners, relate to LSA and environmental outcomes. Second, most LSA work focuses on individual environmental outcomes, such as the urban heat island effect, instead of considering the broader suite of outcomes that LSA changes impact. Here, we undertake an integrated assessment of LSA impacts on surface urban heat island (based on land surface temperature), vegetation presence/health (based on NDVI), and bird biota at two riparian sites with different design intentions in the Phoenix, Arizona metropolitan area. The Rio Salado in Tempe underwent a city-led, infill redevelopment that mixed economic, recreational, and flood control design goals. The New River in Peoria experienced a more typical developer-driven urbanization. The contexts and design goals of the sites generated differences in their LSA, but only a few of these differences were sufficiently unique to contribute to divergent environmental outcomes. These differences reside in (1) the greater distribution of recreational land-covers and (2) increased surface water at the Rio Salado site compared to the New River site. Both changes are linked to land-cover patches becoming greener and cooler as well as a greater presence of waterbird and warbler species at the Rio Salado site. The distinctions between the sites provide insight for crafting design goals for redeveloping or restoring urban riparian landscapes in the Phoenix metropolitan area that are grounded in LSA. With the incorporation of additional relevant variables, especially socioeconomic ones, the research approach employed in this study provides a foundation for the assessment of other urban land system change.
Michelle Stuhlmacher; Riley Andrade; B.L. Turner Ii; Amy Frazier; Wenwen Li. Environmental Outcomes of Urban Land System Change: Comparing Riparian Design Approaches in the Phoenix Metropolitan Area. Land Use Policy 2020, 99, 104615 .
AMA StyleMichelle Stuhlmacher, Riley Andrade, B.L. Turner Ii, Amy Frazier, Wenwen Li. Environmental Outcomes of Urban Land System Change: Comparing Riparian Design Approaches in the Phoenix Metropolitan Area. Land Use Policy. 2020; 99 ():104615.
Chicago/Turabian StyleMichelle Stuhlmacher; Riley Andrade; B.L. Turner Ii; Amy Frazier; Wenwen Li. 2020. "Environmental Outcomes of Urban Land System Change: Comparing Riparian Design Approaches in the Phoenix Metropolitan Area." Land Use Policy 99, no. : 104615.
Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain.
Samantha T. Arundel; Wenwen Li; Sizhe Wang. GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning. Transactions in GIS 2020, 24, 556 -572.
AMA StyleSamantha T. Arundel, Wenwen Li, Sizhe Wang. GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning. Transactions in GIS. 2020; 24 (3):556-572.
Chicago/Turabian StyleSamantha T. Arundel; Wenwen Li; Sizhe Wang. 2020. "GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning." Transactions in GIS 24, no. 3: 556-572.
Three-dimensional (3D) modeling of geological surfaces, such as coal seams and strata horizons, from sparsely sampled data collected in the field, is a crucial task in geological modeling. Interpolation is a common approach for this task to construct continuous geological surface models. However, this problem becomes challenging considering the impact of the faults on geological surfaces. Existing methods tend to solve this problem through three steps, including interpolating stratum and fault surface, applying a fault modeling method to modify the geological surface, and optimizing the modified surface to pass sample points fallen into the fault displacement zone. This paper presents a more concise method to generate a faulted geological surface, in which 1) a constrained Delaunay triangulated irregular network (CD-TIN) is constructed to facilitate the neighborhood search process of the ordinary kriging (OK) interpolation, 2) the CD-TIN is also directly constrained by horizon cut-off lines formed from theoretical fault displacement profiles, and 3) subsequently, neighbors of the location to be estimated are selected effectively in the CD-TIN considering the fault topology. The proposed method significantly improves the time efficiency of the OK interpolation by utilizing the CD-TIN and incorporates fault effects directly into the interpolation process by inserting fault horizontal cut-off lines into CD-TIN. Moreover, by integrating the fault effects directly into the interpolation process, the surface modeling process is accomplished in a single stage instead of two separate stages of interpolation first and then modifying the surface in the fault area. By this strategy, the proposed method significantly improves the time efficiency of the OK interpolation algorithm and achieves more accurate modeling of the faulted geological surface. Experiments were designed to compare the performance of our method with several commonly used approaches, and the results indicate that the proposed TIN-constrained OK method achieves better accuracy and efficiency in modeling faulted geological surfaces than other methods. This method could also be used in geospatial interpolation studies, such as meteorological data interpolation.
Qingren Jia; Wenwen Li; Defu Che. A Triangulated Irregular Network Constrained Ordinary Kriging Method for Three-Dimensional Modeling of Faulted Geological Surfaces. IEEE Access 2020, 8, 85179 -85189.
AMA StyleQingren Jia, Wenwen Li, Defu Che. A Triangulated Irregular Network Constrained Ordinary Kriging Method for Three-Dimensional Modeling of Faulted Geological Surfaces. IEEE Access. 2020; 8 (99):85179-85189.
Chicago/Turabian StyleQingren Jia; Wenwen Li; Defu Che. 2020. "A Triangulated Irregular Network Constrained Ordinary Kriging Method for Three-Dimensional Modeling of Faulted Geological Surfaces." IEEE Access 8, no. 99: 85179-85189.
The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment, urban settings, health and disease propagation, business decisions, and crisis and crime. Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena. This paper reviews the literature for different sensing capabilities, spatiotemporal event extraction methods, and categories of applications for the detected events. The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events) as an agenda for future event detection research. Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and artificial intelligence.
Manzhu Yu; Myra Bambacus; Guido Cervone; Keith Clarke; Daniel Duffy; Qunying Huang; Jing Li; Wenwen Li; Zhenlong Li; Qian Liu; Bernd Resch; Jingchao Yang; Chaowei Yang. Spatiotemporal event detection: a review. International Journal of Digital Earth 2020, 13, 1339 -1365.
AMA StyleManzhu Yu, Myra Bambacus, Guido Cervone, Keith Clarke, Daniel Duffy, Qunying Huang, Jing Li, Wenwen Li, Zhenlong Li, Qian Liu, Bernd Resch, Jingchao Yang, Chaowei Yang. Spatiotemporal event detection: a review. International Journal of Digital Earth. 2020; 13 (12):1339-1365.
Chicago/Turabian StyleManzhu Yu; Myra Bambacus; Guido Cervone; Keith Clarke; Daniel Duffy; Qunying Huang; Jing Li; Wenwen Li; Zhenlong Li; Qian Liu; Bernd Resch; Jingchao Yang; Chaowei Yang. 2020. "Spatiotemporal event detection: a review." International Journal of Digital Earth 13, no. 12: 1339-1365.
In this paper, we present a data-driven framework to support exploratory spatial, temporal, and statistical analysis of intra-urban human mobility. We leveraged a new mobility data source, the dockless bike-sharing service Mobike, to quantify short-trip transportation patterns in Shanghai, China, the world’s largest bike-share city. A data-driven framework was established to integrate multiple data sources, including transportation network data (roads, bikes, and public transit), road characteristics, and urban land use, to achieve a detailed, accurate analysis of cycling patterns at both the individual and group levels. The results provide a comprehensive view of mobility patterns in the use of shared-ride bicycles, including: (1) the temporal and spatiotemporal distribution of shared-bike usage and how this varies according to different land use; (2) the statistical distribution of Mobike trips, which are primarily short-distance; and (3) the travel behavior and road factors that influence Mobike users’ route choice. The findings offer valuable insights for city planners regarding infrastructure development, for shared-ride bike companies to offer better bike rebalancing strategies to meet user demand, and for the promotion of this new green transportation mode to alleviate traffic congestion and enhance public health.
Wenwen Li; Shaohua Wang; Xiaoyi Zhang; Qingren Jia; Yuanyuan Tian. Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories. International Journal of Geographical Information Science 2020, 34, 2451 -2474.
AMA StyleWenwen Li, Shaohua Wang, Xiaoyi Zhang, Qingren Jia, Yuanyuan Tian. Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories. International Journal of Geographical Information Science. 2020; 34 (12):2451-2474.
Chicago/Turabian StyleWenwen Li; Shaohua Wang; Xiaoyi Zhang; Qingren Jia; Yuanyuan Tian. 2020. "Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories." International Journal of Geographical Information Science 34, no. 12: 2451-2474.
As the world’s largest crowdsourcing-based street view platform, Mapillary has received considerable attention in both research and practical applications. By February 2019, more than 20,000 users worldwide contributed approximately 6.3 million kilometers of streetscape sequences. In this study, we attempted to get a deep insight into the Mapillary project through an exploratory analysis from the perspective of contributors, including the development of users, the spatiotemporal analysis of active users, the contribution modes (walking, cycling, and driving), and the devices used to contribute. It shows that inequality exists in the distribution of contributed users, similar to that in other volunteered geographic information (VGI) projects. However, the inequality in Mapillary contribution is less than in OpenStreetMap (OSM). Compared to OSM, the other main difference is that the data collection demonstrated obvious seasonal variation because contributions to OSM can be accomplished on a computer, whereas images have to be captured on the streets for Mapillary, and this is considerably affected by seasonal weather.
Dawei Ma; Hongchao Fan; Wenwen Li; Xuan Ding. The State of Mapillary: An Exploratory Analysis. ISPRS International Journal of Geo-Information 2019, 9, 10 .
AMA StyleDawei Ma, Hongchao Fan, Wenwen Li, Xuan Ding. The State of Mapillary: An Exploratory Analysis. ISPRS International Journal of Geo-Information. 2019; 9 (1):10.
Chicago/Turabian StyleDawei Ma; Hongchao Fan; Wenwen Li; Xuan Ding. 2019. "The State of Mapillary: An Exploratory Analysis." ISPRS International Journal of Geo-Information 9, no. 1: 10.
The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R2 = 0.79 and RMSE = 22.82 g/m2) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m−2 on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management.
Nisha Bao; Wenwen Li; Xiaowei Gu; Yanhui Liu. Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery. Remote Sensing 2019, 11, 2855 .
AMA StyleNisha Bao, Wenwen Li, Xiaowei Gu, Yanhui Liu. Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery. Remote Sensing. 2019; 11 (23):2855.
Chicago/Turabian StyleNisha Bao; Wenwen Li; Xiaowei Gu; Yanhui Liu. 2019. "Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery." Remote Sensing 11, no. 23: 2855.
Massive maps have been shared as Web Map Service (WMS) from various providers, which could be used to facilitate people’s daily lives and support space analysis and management. The theme classification of maps could help users efficiently find maps and support theme-related applications. Traditionally, metadata is usually used in analyzing maps content, few papers use maps, especially legends. In fact, people usually considers metadata, maps and legends together to understand what maps tell, however, no study has tried to exploit how to combine them. This paper proposes a method to fuse them with the purpose of classifying map themes, named latent feature based multimodality fusion for theme classification (LFMF-TC). Firstly, a multimodal dataset is created that supports the supervised classification on map themes. Secondly, textual and visual features are designed for metadata, maps, and legends using some advanced techniques. Thirdly, a latent feature based fusion method is proposed to fuse the multimodal features on the feature level. Finally, a neural network classifier is implemented using supervised learning on the multimodal dataset. In addition, a web-based collaboration platform is developed to facilitate users in labeling multimodal samples through an interactive Graphical User Interface (GUI). Extensive experiments are designed and implemented, whose results prove that LFMF-TC could significantly improve the classification accuracy. In theory, the LFMF-TC could be used for other applications with few modifications.
Zelong Yang; Zhipeng Gui; Huayi Wu; Wenwen Li. A Latent Feature-Based Multimodality Fusion Method for Theme Classification on Web Map Service. IEEE Access 2019, 8, 25299 -25309.
AMA StyleZelong Yang, Zhipeng Gui, Huayi Wu, Wenwen Li. A Latent Feature-Based Multimodality Fusion Method for Theme Classification on Web Map Service. IEEE Access. 2019; 8 (99):25299-25309.
Chicago/Turabian StyleZelong Yang; Zhipeng Gui; Huayi Wu; Wenwen Li. 2019. "A Latent Feature-Based Multimodality Fusion Method for Theme Classification on Web Map Service." IEEE Access 8, no. 99: 25299-25309.