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22 pages, 15750 KiB  
Article
Assessing Four Decades of Land Use and Land Cover Change: Policy Impacts and Environmental Dynamics in the Min River Basin, Fujian, China
by Weixuan Huang, Anil Shrestha, Yifan Xie, Jianwu Yan, Jingxin Wang, Futao Guo, Yuee Cao and Guangyu Wang
Abstract
Land use and land cover change (LULCC) is crucial in sustainable land management. Over the past four decades, the Min River Basin (MRB) has experienced significant LULCC. This study investigated the dynamics of LULCC over these decades (1980–2020) and discusses the key drivers [...] Read more.
Land use and land cover change (LULCC) is crucial in sustainable land management. Over the past four decades, the Min River Basin (MRB) has experienced significant LULCC. This study investigated the dynamics of LULCC over these decades (1980–2020) and discusses the key drivers of land use change in different stages. First, we mapped and quantified changes (i.e., LULCC and landscape indices) in forests, croplands, urban areas, and water bodies from 1980 to 2020 using the China National Land Use/Cover Change (CNLUCC) and ArcGIS Pro 2.3. Second, by analyzing existing policies, we categorized four decades of LULCC trends from 1980 to 2020, delineating three distinct stages: (1) the Economic Restoration (ER) stage (1978–1989), when the ecological impacts of LULCC on forests, croplands, and water bodies received limited policy attention; (2) the Construction of Ecological Protection and Economic Development (EPED) stage (1989–2012), which saw a significant increase in forest coverage, primarily driven by various central and provincial environmental conservation policies, such as the Green for Grain and the “Three-Five-Seven Reforestation Project” in Fujian; and (3) the Ecological Civilization (EC) stage (2012–2020), in which policy focus shifted from expanding forest land areas to enhancing the quality of these areas. However, the cropland area has decreased due to urbanization policies and population migration from rural to urban areas, including the above-mentioned forest policies. Thus, this study highlights the complex relationship between different land use land cover policies, as some policies had synergistic effects between the policies and positive outcomes, while other policies showed conflicting outcomes. Our results emphasize the importance of integrated land and water resource management and provide insights for policymakers to balance development and environmental conservation policies in the MRB. Full article
(This article belongs to the Section Land – Observation and Monitoring)
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22 pages, 5126 KiB  
Article
An Analysis of Factors Contributing to Cost Overruns in the Global Construction Industry
by Ahmed Mohammed Abdelalim, Maram Salem, Mohamed Salem, Manal Al-Adwani and Mohamed Tantawy
Abstract
The construction industry builds infrastructure and strengthens the global economy, but it struggles with cost overruns. This systematic study used scientometrics and Social Network Analysis to examine the multifaceted factors causing construction project cost escalations. After reviewing 405 scholarly works, the research mapped [...] Read more.
The construction industry builds infrastructure and strengthens the global economy, but it struggles with cost overruns. This systematic study used scientometrics and Social Network Analysis to examine the multifaceted factors causing construction project cost escalations. After reviewing 405 scholarly works, the research mapped and analyzed 66 interconnected cost overrun factors in 69 high-impact studies between 2000 and 2024. To uncover the patterns, the study used two main research methods. First, this study applied a scientometric analysis that reviewed trends and gaps from the previous studies. Second, this study used Social Network Analysis (SNA) to examine how different factors were connected and which factors had the strongest influence on cost overruns. The methodology comprised a systematic literature search, document selection, scientometric analysis, factor standardization, and an SNA application. Seven critical drivers with high network centrality were identified: planning and scheduling issues, project estimation inaccuracies, design inefficiencies, negative weather conditions, scope definition challenges, contractual ambiguities, and unforeseeable site conditions. By applying the SNA degree centrality (DC), the analysis quantified the significance of each factor within the network. With the use of this dual analysis, a novel mapping of the main causes of cost overruns was produced, leading to the discovery of seven core factors that significantly affected project outcomes, including planning and scheduling issues, project estimation problems, and design inefficiencies. The findings advance the knowledge of the dynamics of cost overruns and offer practical insights for enhancing cost management practices in the construction industry. Full article
(This article belongs to the Section Construction Management, and Computers & Digitization)
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29 pages, 53708 KiB  
Article
Optimizing Site Selection for Construction: Integrating GIS Modeling, Geophysical, Geotechnical, and Geomorphological Data Using the Analytic Hierarchy Process
by Doaa Wahba, Awad A. Omran, Ashraf Adly, Ahmed Gad, Hasan Arman and Heba El-Bagoury
ISPRS Int. J. Geo-Inf. 2025, 14(1), 3; https://doi.org/10.3390/ijgi14010003 - 25 Dec 2024
Abstract
Identifying suitable sites for urban, industrial, and tourist development is important, especially in areas with increasing population and limited land availability. Kharga Oasis, Egypt, stands out as a promising area for such development, which can help reduce overcrowding in the Nile Valley and [...] Read more.
Identifying suitable sites for urban, industrial, and tourist development is important, especially in areas with increasing population and limited land availability. Kharga Oasis, Egypt, stands out as a promising area for such development, which can help reduce overcrowding in the Nile Valley and Delta. However, soil and various environmental factors can affect the suitability of civil engineering projects. This study used Geographic Information Systems (GISs) and a multi-criteria decision-making approach to assess the suitability of Kharga Oasis for construction activities. Geotechnical parameters were obtained from seismic velocity data, including Poisson’s ratio, stress ratio, concentration index, material index, N-value, and foundation-bearing capacity. A comprehensive analysis of in situ and laboratory-based geological and geotechnical data from 24 boreholes examined soil plasticity, water content, unconfined compressive strength, and consolidation parameters. By integrating geotechnical, geomorphological, geological, environmental, and field data, a detailed site suitability map was created using the analytic hierarchy process to develop a weighted GIS model that accounts for the numerous elements influencing civil project design and construction. The results highlight suitable sites within the study area, with high and very high suitability classes covering 56.87% of the land, moderate areas representing 27.61%, and unsuitable areas covering 15.53%. It should be noted that many settlements exist in highly vulnerable areas, emphasizing the importance of this study. This model identifies areas vulnerable to geotechnical and geoenvironmental hazards, allowing for early decision-making at the beginning of the planning process and reducing the waste of effort. The applied model does not only highlight suitable sites in the Kharga Oasis, Egypt, but, additionally, it provides a reproducible method for efficiently assessing land use suitability in other regions with similar geological and environmental conditions around the world. Full article
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14 pages, 7631 KiB  
Article
Restoration of Posidonia oceanica Meadow Using Cuttings from an Area Impacted by Harbor Extension Project
by Mario De Luca, Luigi Piazzi, Ivan Guala, Maria Francesca Cinti, Paolo Marras, Arianna Pansini, Federico Pinna, Alessandra Puccini, Antonio Santonastaso, Myriam Stelletti, Patrizia Stipcich and Vincenzo Pascucci
J. Mar. Sci. Eng. 2025, 13(1), 3; https://doi.org/10.3390/jmse13010003 - 24 Dec 2024
Abstract
In the Mediterranean Sea, restoration of marine habitats has mostly focused on the endemic seagrass Posidonia oceanica. Despite several transplanting experiments, large-scale projects are rare, and their success is poorly known. The present work describes a restoration project of a large, degraded [...] Read more.
In the Mediterranean Sea, restoration of marine habitats has mostly focused on the endemic seagrass Posidonia oceanica. Despite several transplanting experiments, large-scale projects are rare, and their success is poorly known. The present work describes a restoration project of a large, degraded area in northern Sardinia (Italy) using cuttings harvested from a donor meadow that was destined for destruction due to harbor expansion. The receiving site was selected through a multidisciplinary study including acoustic mapping, ROV surveys, sediment assessment, and analyses of satellite images across ten years to evaluate the site suitability. Plants were manually uprooted from the donor meadow and cuttings were selected and transplanted within 24 h by environmental engineering techniques. The cuttings were transplanted onto degradable mats of natural coconut nets coupled with a double-twist steel mesh and anchored to the bottom. Overall, 7000 patches, each containing 20 cuttings, were transplanted in three periods: June–July 2022, October–November 2022, and February–March 2023. One year after the restoration, all the patches were in situ, with an overall cutting survival of 59%. The results are comparable to those of previous small-scale projects using the same technique and also endorse its suitability for the restoration of large, degraded areas. Full article
(This article belongs to the Topic Conservation and Management of Marine Ecosystems)
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15 pages, 3254 KiB  
Article
Mapping Drug-Resistant Tuberculosis Treatment Outcomes in Hunan Province, China
by Temesgen Yihunie Akalu, Archie C. A. Clements, Zuhui Xu, Liqiong Bai and Kefyalew Addis Alene
Trop. Med. Infect. Dis. 2025, 10(1), 3; https://doi.org/10.3390/tropicalmed10010003 - 24 Dec 2024
Abstract
Background: Drug-resistant tuberculosis (DR-TB) remains a major public health challenge in China, with varying treatment outcomes across different regions. Understanding the spatial distribution of DR-TB treatment outcomes is crucial for targeted interventions to improve treatment success in high-burden areas such as Hunan Province. [...] Read more.
Background: Drug-resistant tuberculosis (DR-TB) remains a major public health challenge in China, with varying treatment outcomes across different regions. Understanding the spatial distribution of DR-TB treatment outcomes is crucial for targeted interventions to improve treatment success in high-burden areas such as Hunan Province. This study aimed to map the spatial distribution of DR-TB treatment outcomes at a local level and identify sociodemographic and environmental factors associated with poor treatment outcomes in Hunan Province, China. Methods: A spatial analysis was conducted using DR-TB data from the Tuberculosis Control Institute of Hunan Province, covering the years 2013 to 2018. The outcome variable, the proportion of poor treatment outcomes, was defined as a composite measure of treatment failure, death, and loss to follow-up. Sociodemographic, economic, healthcare, and environmental variables were obtained from various sources, including the WorldClim database, the Malaria Atlas Project, and the Hunan Bureau of Statistics. These covariates were linked to a map of Hunan Province and DR-TB notification data using R software version 4.4.0. The spatial clustering of poor treatment outcomes was analyzed using the local Moran’s I and Getis–Ord statistics. A Bayesian logistic regression model was fitted, with the posterior parameters estimated using integrated nested Laplace approximation (INLA). Results: In total, 1381 DR-TB patients were included in the analysis. An overall upward trend in poor DR-TB treatment outcomes was observed, peaking at 14.75% in 2018. Deaths and treatment failures fluctuated over the years, with a notable increase in deaths from 2016 to 2018, while the proportion of patients lost to follow-up significantly declined from 2014 to 2018. The overall proportion of poor treatment outcomes was 9.99% (95% credible interval (CI): 8.46% to 11.70%), with substantial spatial clustering, particularly in Anxiang (50%), Anren (50%), and Chaling (42.86%) counties. The proportion of city-level indicators was significantly associated with higher proportions of poor treatment outcomes (odds ratio (OR): 1.011; 95% CRI: 1.20 December 2024 001–1.035). Conclusions: This study found a concerning increase in poor DR-TB treatment outcomes in Hunan Province, particularly in certain high-risk areas. Targeted public health interventions, including enhanced surveillance, focused healthcare initiatives, and treatment programs, are essential to improve treatment success. Full article
(This article belongs to the Special Issue Emerging and Re-emerging Infectious Diseases and Public Health)
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28 pages, 37855 KiB  
Article
Regional-Scale Equidistance Optimizing Method Considering the Equidistance Patterns of Discrete Global Grid Systems
by Fuli Luo, Lei Wang, Yue Yu, Tengfei Cui and Li Han
ISPRS Int. J. Geo-Inf. 2024, 13(12), 467; https://doi.org/10.3390/ijgi13120467 - 23 Dec 2024
Abstract
The Discrete Global Grid System (DGGS) provides a foundational framework for the digital Earth, where uniform intercell distances are essential for accurate numerical simulations. However, due to the spherical topology, achieving strictly equidistant spherical grid cells is impractical. Most existing studies have focused [...] Read more.
The Discrete Global Grid System (DGGS) provides a foundational framework for the digital Earth, where uniform intercell distances are essential for accurate numerical simulations. However, due to the spherical topology, achieving strictly equidistant spherical grid cells is impractical. Most existing studies have focused on regional scales, which are constrained by data acquisition limitations and render global equidistant optimization algorithms economically infeasible. The equidistant characteristics of cells are influenced by map projections and often exhibit regional variations. In this paper, we analyze these equidistant characteristics and construct an equidistant pattern for an icosahedral hexagonal DGGS. By integrating this pattern into the icosahedral orientation method, we develop a regional-scale equidistant optimization method for DGGS. Experiments on river network extraction in the Yangtze River Basin demonstrate significant improvements: the equidistance of grid cells covering the region increased by over 34.2%, while the accuracy of river network extraction improved by 51.41%. Moreover, this method is extensible to other grid models, enhancing the broader applicability of DGGS. Full article
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24 pages, 31029 KiB  
Article
InCrowd-VI: A Realistic Visual–Inertial Dataset for Evaluating Simultaneous Localization and Mapping in Indoor Pedestrian-Rich Spaces for Human Navigation
by Marziyeh Bamdad, Hans-Peter Hutter and Alireza Darvishy
Sensors 2024, 24(24), 8164; https://doi.org/10.3390/s24248164 - 21 Dec 2024
Viewed by 237
Abstract
Simultaneous localization and mapping (SLAM) techniques can be used to navigate the visually impaired, but the development of robust SLAM solutions for crowded spaces is limited by the lack of realistic datasets. To address this, we introduce InCrowd-VI, a novel visual–inertial dataset specifically [...] Read more.
Simultaneous localization and mapping (SLAM) techniques can be used to navigate the visually impaired, but the development of robust SLAM solutions for crowded spaces is limited by the lack of realistic datasets. To address this, we introduce InCrowd-VI, a novel visual–inertial dataset specifically designed for human navigation in indoor pedestrian-rich environments. Recorded using Meta Aria Project glasses, it captures realistic scenarios without environmental control. InCrowd-VI features 58 sequences totaling a 5 km trajectory length and 1.5 h of recording time, including RGB, stereo images, and IMU measurements. The dataset captures important challenges such as pedestrian occlusions, varying crowd densities, complex layouts, and lighting changes. Ground-truth trajectories, accurate to approximately 2 cm, are provided in the dataset, originating from the Meta Aria project machine perception SLAM service. In addition, a semi-dense 3D point cloud of scenes is provided for each sequence. The evaluation of state-of-the-art visual odometry (VO) and SLAM algorithms on InCrowd-VI revealed severe performance limitations in these realistic scenarios. Under challenging conditions, systems exceeded the required localization accuracy of 0.5 m and the 1% drift threshold, with classical methods showing drift up to 5–10%. While deep learning-based approaches maintained high pose estimation coverage (>90%), they failed to achieve real-time processing speeds necessary for walking pace navigation. These results demonstrate the need and value of a new dataset to advance SLAM research for visually impaired navigation in complex indoor environments. Full article
(This article belongs to the Section Sensors and Robotics)
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20 pages, 15726 KiB  
Article
Point Cloud Wall Projection for Realistic Road Data Augmentation
by Kana Kim, Sangjun Lee, Vijay Kakani, Xingyou Li and Hakil Kim
Sensors 2024, 24(24), 8144; https://doi.org/10.3390/s24248144 - 20 Dec 2024
Viewed by 243
Abstract
Several approaches have been developed to generate synthetic object points using real LiDAR point cloud data for advanced driver-assistance system (ADAS) applications. The synthetic object points generated from a scene (both the near and distant objects) are essential for several ADAS tasks. However, [...] Read more.
Several approaches have been developed to generate synthetic object points using real LiDAR point cloud data for advanced driver-assistance system (ADAS) applications. The synthetic object points generated from a scene (both the near and distant objects) are essential for several ADAS tasks. However, generating points from distant objects using sparse LiDAR data with precision is still a challenging task. Although there are a few state-of-the-art techniques to generate points from synthetic objects using LiDAR point clouds, limitations such as the need for intense compute power still persist in most cases. This paper suggests a new framework to address these limitations in the existing literature. The proposed framework contains three major modules, namely position determination, object generation, and synthetic annotation. The proposed framework uses a spherical point-tracing method that augments 3D LiDAR distant objects using point cloud object projection with point-wall generation. Also, the pose determination module facilitates scenarios such as platooning carried out by the synthetic object points. Furthermore, the proposed framework improves the ability to describe distant points from synthetic object points using multiple LiDAR systems. The performance of the proposed framework is evaluated on various 3D detection models such as PointPillars, PV-RCNN, and Voxel R-CNN for the KITTI dataset. The results indicate an increase in mAP (mean average precision) by 1.97%1.3%, and 0.46% from the original dataset values of 82.23%86.72%, and 87.05%, respectively. Full article
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22 pages, 6199 KiB  
Article
Weakly Supervised Instance Segmentation in Aerial Images via Comprehensive Spatial Adaptation
by Jingting Xu, Peng Luo and Dejun Mu
Remote Sens. 2024, 16(24), 4757; https://doi.org/10.3390/rs16244757 - 20 Dec 2024
Viewed by 240
Abstract
Weakly supervised instance segmentation (WSIS) only employs image-level supervision to identify instance class labels and create segmentation masks, drawing increasing attention. Currently, existing WSIS methods primarily focus on activating the most discriminative regions and then inferring the entire instance by analyzing inter-pixel relationships [...] Read more.
Weakly supervised instance segmentation (WSIS) only employs image-level supervision to identify instance class labels and create segmentation masks, drawing increasing attention. Currently, existing WSIS methods primarily focus on activating the most discriminative regions and then inferring the entire instance by analyzing inter-pixel relationships within those regions. However, these identification regions are typically concentrated in limited but critical regions or are mistakenly activated in the background region, making it challenging to address scale variations among instances. Furthermore, different aerial instances often appear in close proximity, resulting in the merging of multiple instances of the same class. To tackle these challenges, we propose a comprehensive approach called Comprehensive Spatial Adaptation Segmentation (CSASeg). Specifically, the self-adaptive spatial-aware enhancement network (SSE) identifies extensive regions by analyzing spatial consistency within the class semantic map. Then, we develop a multi-level projection field (MPF) module to significantly enhance instance-level discrimination through deep-to-shallow residual estimation. Additionally, a foreground enhancement module is incorporated into SSE to reduce background noise while enhancing foreground details, significantly increasing the effectiveness of instance analysis. Extensive experiments conduct on three challenging datasets, iSAID, NWPU VHR-10.v2, and SSDD, demonstrate the competitiveness of our proposed approach. Full article
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20 pages, 8870 KiB  
Article
Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
by Weiwei Liu, Shaoxiang Zeng, Kaiyue Chen and Xiaodong Pan
Appl. Sci. 2024, 14(24), 11917; https://doi.org/10.3390/app142411917 - 19 Dec 2024
Viewed by 330
Abstract
The advancement of urbanization has led to stricter requirements for the prediction and control of excavation-induced deformations. To achieve this goal, this study proposes a novel method that integrates a spatiotemporal graph attention network (ST-GAT) with a proportional–integral–derivative (PID) controller to proactively control [...] Read more.
The advancement of urbanization has led to stricter requirements for the prediction and control of excavation-induced deformations. To achieve this goal, this study proposes a novel method that integrates a spatiotemporal graph attention network (ST-GAT) with a proportional–integral–derivative (PID) controller to proactively control wall deflections caused by excavation. The ST-GAT model improves wall deflection prediction by capturing spatial relationships between monitoring points near steel struts and dynamically assigning weights based on their importance. The interpretability of the model is greatly improved by generating a feature attribution map across various input features and visualizing the weight distribution between nodes in the GAT network. A proactive control method of wall deflections is proposed by replacing current monitoring values in the PID control system with predicted values for multiple steel struts using the ST-GAT model. Compared to the standard PID method, this approach can control wall deflections before significant deformations occur. A real excavation project equipped with a servo support system is used to validate the effectiveness of the proposed method. The results show that the ST-GAT model significantly outperforms other models, and its performance improves when utilizing spatial relationships from more monitoring points. With a reasonable combination of PID hyperparameters, the proposed ST-GAT-based PID controller can control wall deflections close to a target value. Full article
(This article belongs to the Section Civil Engineering)
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18 pages, 743 KiB  
Review
Cultivating Growth: A Review of Flourishing Students in Higher Education
by Faizah Faizah, Dewi Retno Suminar and Nono Hery Yoenanto
Adolescents 2024, 4(4), 587-604; https://doi.org/10.3390/adolescents4040041 - 19 Dec 2024
Viewed by 516
Abstract
The flourishing of university students is influenced by various factors that significantly impact their well-being and academic performance, with suboptimal levels being a serious concern. Global issues of high dropout rates and low levels of flourishing among university students have prompted this study [...] Read more.
The flourishing of university students is influenced by various factors that significantly impact their well-being and academic performance, with suboptimal levels being a serious concern. Global issues of high dropout rates and low levels of flourishing among university students have prompted this study to identify factors contributing to student flourishing and describe the characteristics of students who achieve it. The review followed a rigorous protocol, including a comprehensive search across multiple databases, screening based on pre-established criteria, quality assessment using the MMAT tool, data extraction using NVivo 12 version 12.6.0.959 (64-bit), and matrix synthesis to identify patterns and gaps in the literature. Results reveal that psychological factors, meaning and purpose, personal projects, social support, social relationships, and environmental factors influence student flourishing. Flourishing students exhibit emotional and psychological well-being (37.5%), positive social functioning (31.25%), achievement and competence (18.75%), and positive psychological functioning (12.5%). These findings, consistent with previous research and flourishing theory, suggest the need for a holistic approach to promoting student flourishing through targeted interventions and recognition of flourishing characteristics. This comprehensive mapping of factors and characteristics of student flourishing can guide theory development and practical implementation in universities. Future research should consider longitudinal studies, replication in different contexts, qualitative research, and exploration of additional factors. Full article
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22 pages, 12407 KiB  
Article
Analyzing Archive Transit Multibeam Data for Nodule Occurrences
by Mark E. Mussett, David F. Naar, David W. Caress, Tracey A. Conrad, Alastair G. C. Graham, Max Kaufmann and Marcia Maia
J. Mar. Sci. Eng. 2024, 12(12), 2322; https://doi.org/10.3390/jmse12122322 - 18 Dec 2024
Viewed by 318
Abstract
We show that analyzing archived and future multibeam backscatter and bathymetry data, in tandem with regional environmental parameters, can help to identify polymetallic nodule fields in the world’s oceans. Extensive archived multibeam transit data through remote areas of the world’s oceans are available [...] Read more.
We show that analyzing archived and future multibeam backscatter and bathymetry data, in tandem with regional environmental parameters, can help to identify polymetallic nodule fields in the world’s oceans. Extensive archived multibeam transit data through remote areas of the world’s oceans are available for data mining. New multibeam data will be made available through the Seabed 2030 Project. Uniformity of along- and across-track backscatter, backscatter intensity, angular response, water depth, nearby ground-truth data, local slope, sedimentation rate, and seafloor age provide thresholds for discriminating areas that are permissive to nodule presence. A case study of this methodology is presented, using archived multibeam data from a remote section of the South Pacific along the Foundation Seamounts between the Selkirk paleomicroplate and East Pacific Rise, that were collected during the 1997 Foundation–Hotline expedition on R/V Atalante. The 12 kHz Simrad EM12D multibeam data and the other forementioned data strongly suggest that a previously unknown nodule occurrence exists along the expedition transit. We also compare the utility of three different backscatter products to demonstrate that scans of printed backscatter maps can be a useful substitute for digital backscatter mosaics calculated using primary multibeam data files. We show that this expeditious analysis of legacy multibeam data could characterize benthic habitat types efficiently in remote deep-ocean areas, prior to more time-consuming and expensive video and sample acquisition surveys. Additionally, utilizing software other than specialty sonar processing programs during this research allows an exploration of how multibeam data products could be interrogated by a broader range of scientists and data users. Future mapping, video, and sampling cruises in this area would test our prediction and investigate how far it might extend to the north and south. Full article
(This article belongs to the Section Marine Environmental Science)
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25 pages, 17064 KiB  
Article
An Environment Recognition Algorithm for Staircase Climbing Robots
by Yanjie Liu, Yanlong Wei, Chao Wang and Heng Wu
Remote Sens. 2024, 16(24), 4718; https://doi.org/10.3390/rs16244718 - 17 Dec 2024
Viewed by 367
Abstract
For deformed wheel-based staircase-climbing robots, the accuracy of staircase step geometry perception and scene mapping are critical factors in determining whether the robot can successfully ascend the stairs and continue its task. Currently, while there are LiDAR-based algorithms that focus either on step [...] Read more.
For deformed wheel-based staircase-climbing robots, the accuracy of staircase step geometry perception and scene mapping are critical factors in determining whether the robot can successfully ascend the stairs and continue its task. Currently, while there are LiDAR-based algorithms that focus either on step geometry detection or scene mapping, few comprehensive algorithms exist that address both step geometry perception and scene mapping for staircases. Moreover, significant errors in step geometry estimation and low mapping accuracy can hinder the ability of deformed wheel-based mobile robots to climb stairs, negatively impacting the efficiency and success rate of task execution. To solve the above problems, we propose an effective LiDAR-Inertial-based point cloud detection method for staircases. Firstly, we preprocess the staircase point cloud, mainly using the Statistical Outlier Removal algorithm to effectively remove the outliers in the staircase scene and combine the vertical angular resolution and spatial geometric relationship of LiDAR to realize the ground segmentation in the staircase scene. Then, we perform post-processing based on the point cloud map obtained from LiDAR SLAM, extract the staircase point cloud and project and fit the staircase point cloud by Ceres optimizer, and solve the dimensional information such as depth and height of the staircase by combining with the mean filtering method. Finally, we fully validate the effectiveness of the method proposed in this paper by conducting multiple sets of SLAM and size detection experiments in real different staircase scenarios. Full article
(This article belongs to the Special Issue Advanced AI Technology in Remote Sensing)
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25 pages, 12205 KiB  
Article
Integrating Temporal Dimensions in Circularity of the Built Environment Analysis of Two Flemish Industrial Parks
by Charlotte Timmers, Ellen Verbiest, Sam Ottoy and Julie Marin
Sustainability 2024, 16(24), 11053; https://doi.org/10.3390/su162411053 - 17 Dec 2024
Viewed by 417
Abstract
This manuscript explores how incorporating temporal dimensions into built environment research can promote a more circular society, adding societal improvements to efficiency-driven measures closing waste or material cycles. The current circularity approaches in industrial environments mainly focus on short-term innovations reducing resource extraction [...] Read more.
This manuscript explores how incorporating temporal dimensions into built environment research can promote a more circular society, adding societal improvements to efficiency-driven measures closing waste or material cycles. The current circularity approaches in industrial environments mainly focus on short-term innovations reducing resource extraction and waste, overlooking long-term circularity potentials of natural resource management such as living soils as a basis for all life. This study addresses this gap by investigating, analyzing, and drawing interplays between regenerative soil cycles and business development cycles in two Flemish industry parks, Kortrijk-Noord and Haasrode. Using diachronic mapping, a qualitative design and action research tool, the study aims to generate a space–time composite of soil and business cycles, integrating archival research, interviews, and policy document reviews. This method visually captures interplays between geology, land valuation, and economic development, demonstrating that integrating soil and business cycles can suggest new pathways for site-specific circular practices on Flemish industry parks, which can inform site-specific project frameworks for circular built environments. As such, the research advocates a paradigm shift in industry park (re)development, from product and material innovation within a ‘time is money’ framework to an integrated ‘time is life’ approach, where time’s historical and social dimensions are part of circular landscape development. Full article
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18 pages, 4832 KiB  
Article
An Inter-Method Comparison of Drones, Side-Scan Sonar, Airplanes, and Satellites Used for Eelgrass (Zostera marina) Mapping and Management
by Jillian Carr and Todd Callaghan
Geosciences 2024, 14(12), 345; https://doi.org/10.3390/geosciences14120345 - 17 Dec 2024
Viewed by 495
Abstract
Remote sensing is heavily relied upon where eelgrass maps are needed for tracking trends, project siting and permitting, water quality assessments, and restoration planning. However, there is only a moderate degree of confidence in the accuracy of maps derived from remote sensing, thus [...] Read more.
Remote sensing is heavily relied upon where eelgrass maps are needed for tracking trends, project siting and permitting, water quality assessments, and restoration planning. However, there is only a moderate degree of confidence in the accuracy of maps derived from remote sensing, thus risking inadequate resource protection. In this study, semi-synchronous drone, side-scan sonar, airplane, and satellite missions were conducted at five Massachusetts eelgrass meadows to assess each method’s edge-detection capability and mapping accuracy. To ground-truth the remote sensing surveys, SCUBA divers surveyed the meadow along transects perpendicular to shore to locate the last shoot (i.e., meadow’s edge) and sampled quadrat locations along the transect for percent cover, canopy height, and meadow patchiness. In addition, drop frame underwater camera surveys were conducted to assess the accuracy of each remote sensing survey. Eelgrass meadow delineations derived from each remote sensing method were compared to ground-truthing data to address the following study objectives: (1) determine if and how much eelgrass was missed during manual photointerpretation of the imagery from each remote sensing method, (2) assess map accuracy, as well as the effects of eelgrass percent cover, canopy height, and meadow patchiness on method performance, and (3) make management recommendations regarding the use of remote sensing data for eelgrass mapping. Results showed that all remote sensing methods were associated with the underestimation of eelgrass. At the shallow edge, mean edge detection error was lowest for drone imagery (11.2 m) and increased with decreasing image resolution, up to 38.5 m for satellite imagery. At the deep edge, mean edge detection error varied by survey method but ranged from 72 to 106 m. Maximum edge detection errors across all sites and depths for each survey method were 112.4 m, 121.4 m, 121.7 m, and 106.7 m for drone, sonar, airplane, and satellite data, respectively. The overall accuracy of eelgrass delineations across the survey methods ranged from 76–89% and corresponded with image resolution, where drones performed best, followed by sonar, airplanes, and satellites; however, there was a high degree of site variability. Accuracy at the shallow edge was greater than at the deep edge across all survey types except for satellite, where accuracy was the same at both depths. Accuracy was influenced by eelgrass percent cover, canopy height, and meadow patchiness. Low eelgrass density (i.e., 1–10% cover), patchy eelgrass (i.e., shoots or patches spaced > 5 m) and shorter canopy height (i.e., <22 cm) were associated with reduced accuracy across all methods; however, drones performed best across all scenarios. Management recommendations include applying regulatory buffers to eelgrass maps derived from remote sensing in order to protect meadow edge areas from human disturbances, the prioritization of using SCUBA and high-resolution platforms like drones and sonar for eelgrass mapping, and for existing mapping programs to allocate more resources to ground-truthing along meadow edges. Full article
(This article belongs to the Special Issue Progress in Seafloor Mapping)
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