Map Of Environment Research Articles

Research on Intelligent Wheelchair Multimode Human–Computer Interaction and Assisted Driving Technology

The traditional wheelchair focuses on the “human-chair” motor function interaction to ensure the elderly and people with disabilities’ basic travel. For people with visual, hearing, physical disabilities, etc., the current wheelchairs show shortcomings in terms of accessibility and independent travel for this group. Therefore, this paper develops an intelligent wheelchair with multimodal human–computer interaction and autonomous navigation technology. Firstly, it researches the multimodal human–computer interaction technology of occupant gesture recognition, speech recognition, and head posture recognition and proposes a wheelchair control method of three-dimensional head posture mapping the two-dimensional plane. After testing, the average accuracy of the gesture, head posture and voice control modes of the motorized wheelchair proposed in this study reaches more than 95 percent. Secondly, the LiDAR-based smart wheelchair indoor autonomous navigation technology is investigated to realize the autonomous navigation of the wheelchair by constructing an environment map, using A* and DWA algorithms for global and local path planning, and adaptive Monte Carlo simulation algorithms for real-time localization. Experiments show that the position error of the wheelchair is within 10 cm, and the heading angle error is less than 5° during the autonomous navigation. The multimode human–computer interaction and assisted driving technology proposed in this study can partially compensate and replace the functional deficiencies of the disabled population and improve the quality of life of the elderly and disabled population.

Citizens as Environmental Sensors: Noise Mapping and Assessment on Lemnos Island, Greece, Using VGI and Web Technologies

Noise caused by industrial activities, urban life and traffic has a negative impact on people's health and quality of life. Environmental noise mapping is an important tool to study and address this problem. Through specialized measurement and analysis techniques, it is possible to identify areas at high risk of noise and under-stand the extent of the problem. This information can be used to develop strategies to protect the population exposed to high noise levels. In this paper, a method for collecting, processing, managing and mapping spatial noise data based on Volunteered Geographic Information (VGI) and web mapping technologies is present-ed. The proposed method uses noise pollution data collected from citizens using mobile devices. This includes both on-site measurements and information on users' subjective perceptions, providing a comprehensive picture of the problem. This enables authorities to take into account the individual perceptions and needs of citizens when planning noise mitigation measures, which has not been the case in the inter-national literature to date. To achieve this goal, a prototype of a free and open-source tool called "Noise Pollution" was developed. The proposed methodology and the resulting system are intended to help improve the quality of life in noisy environments and protect the health of citizens. They provide a practical tool for collecting and analyzing noise data and enable the development of noise mitigation strategies for the benefit of the population exposed to high noise levels.

Geospatial analysis for environmental noise mapping: A land use regression approach in a metropolitan city

Environmental noise can lead to adverse health outcomes. Understanding the spatial variability of environmental noise is crucial for mitigating potential health risks and developing influential urban strategies for reducing noise levels. This study aimed to measure noise levels and develop a land use regression (LUR) model to determine the spatial variability of environmental noise in Shiraz, Iran. A grid-based technique was used to establish 191 noise measurement sites (summer) across the city to generate the LUR model based on two noise metrics: Lden and Lnight. Leave-one-out cross-validation (LOOCV) and 38 additional measurement sites (winter) were used for the LUR model assessment. The mean values of Lden and Lnight during summer were 68.20 (±8.05) and 58.95 (±9.55), respectively, while during winter, the corresponding values were 69.46 (±5.46) and 58.81 (±6.79). The LUR models explained 67% and 65% of the spatial variability in Lden and Lnight, respectively. LOOCV analysis demonstrated R2 values of 0.64 and 0.61. Moreover, findings indicated mean absolute error (MAE) values of 3.96 dB(A) for Lden and 4.74 dB(A) for Lnight. Validation based on an additional set of 38 measurement sites revealed R2 values of 0.62 for both Lden and Lnight, with MAE of 2.78 and 3.31, respectively. In addition, the adjusted R2 values were 0.54 and 0.53. The results indicated no significant temporal variations between summer and winter. The results revealed that road-related variables significantly influenced noise levels. Moreover, the results indicated that Lden and Lnight levels were higher than the World Health Organization recommendations for exposure to road traffic noise. The results of our study showed that the LUR modeling approach based on geographical predictors is an effective tool for assessing changes in ambient noise levels in other cities in Iran and around the globe.

Modeling soil moisture from in situ portable X-ray spectrometer measurements: A novel approach for correcting geochemical data across different environments and climatic conditions

The portable X-ray fluorescence (pXRF) spectrometer is widely employed for in situ analysis of both contaminated and uncontaminated soils. However, the accuracy of the measurements can be significantly affected by soil moisture, resulting in unreliable soil pollution monitoring. This effect has already been studied and quantified, but this is ineffective if the soil moisture in the field is unknown. Given the considerable variability of soil moisture conditions across time and space, significant bias during in situ investigations remains a main issue. This study introduces a novel method to estimate soil moisture directly from pXRF field measurements, enabling its reliable use in almost any field condition. The study was conducted using soil samples and in situ pXRF soil surface measurements in Estarreja (Portugal) and Vicdessos (France). In the first experiment, the innovative approach involved modeling soil moisture directly from the raw XRF measurement errors obtained in moist soils using multiple regression. In the second experiment, metal concentrations were modeled as an exponential function of the moisture content. The final model integrates both approaches to correct field data from geochemical mapping in diverse environments, including a coastal region in Portugal and a mountainous region in France. Our findings demonstrate that this simple, efficient and cost-effective method accurately predicts soil moisture (U) using pXRF, as shown by the equation Umeasured = 1.0028 x Uestimated (r2 = 0.9715). The model effectively corrected up to 70% of moisture-induced errors in metal concentrations in the wettest soils and produced more reliable soil Fe, Pb, and Zn maps. Specifically, the accuracy improvement was at least 32% in drier soils (Portugal) and at least 55% in wetter soils (France). This study offers a cost-effective, efficient solution for employing pXRF in geochemical mapping across different climatic conditions and soil environments.

Evaluation of the Accuracy of Photogrammetric Reconstruction of Bathymetry Using Differential GNSS Synchronized with an Underwater Camera

Abstract. Photogrammetry is a valuable tool for 3D documentation, mapping, and monitoring of underwater environments. However, the ground control surveys necessary for georeferencing and validation of the reconstructed bathymetry are difficult and time consuming to perform underwater, and thus impractical to scale to larger areas. Underwater direct georeferencing, using a differential GNSS receiver synchronized with an underwater camera system, offers an attractive alternative to surveying underwater ground control points in conditions when the seafloor is clearly visible from the surface. In this paper, the design of an underwater direct georeferencing system using mostly commercial off the shelf components is presented. The accuracy of the system is evaluated against geodetic survey based on trilateration and leveling, as well as by RTK (real time kinematic) positioning using a tilt-compensated GNSS receiver mounted on an extended pole to allow measurements of points in up to 7 m in water depth. Tests were conducted in a controlled outdoor pool setting with depths from 1–3 m, as well as in a 10 m × 10 m test plot established on the seafloor in a near-shore environment by Catalina Island, California at depths from 4–10 m. Comparing the geometry of the photogrammetric reconstruction with the geodetic survey yielded sub centimeter consistency, and 1 mm accuracy in length measurement was achieved when compared with calibrated 0.5 m scale bars. Through repeated surveys of the same area, repeatability of georeferencing is demonstrated within expectations for differential GNSS positioning, with horizontal errors at sub centimeter level, and vertical errors of up to 3 cm in the worst cases. These tests demonstrate the benefits of the underwater direct georeferencing approach in shallow waters, which can be scaled up much more easily than measuring underwater ground control points with traditional approaches, making this an ideal option for collecting accurate bathymetry of the seafloor over large coastal areas with clear waters.

SLAM for Indoor Mapping of Wide Area Construction Environments

Abstract. Simultaneous localization and mapping (SLAM), i.e., the reconstruction of the environment represented by a (3D) map and the concurrent pose estimation, has made astonishing progress. Meanwhile, large scale applications aiming at the data collection in complex environments like factory halls or construction sites are becoming feasible. However, in contrast to small scale scenarios with building interiors separated to single rooms, shop floors or construction areas require measures at larger distances in potentially texture less areas under difficult illumination. Pose estimation is further aggravated since no GNSS measures are available as it is usual for such indoor applications. In our work, we realize data collection in a large factory hall by a robot system equipped with four stereo cameras as well as a 3D laser scanner. We apply our state-of-the-art LiDAR and visual SLAM approaches and discuss the respective pros and cons of the different sensor types for trajectory estimation and dense map generation in such an environment. Additionally, dense and accurate depth maps are generated by 3D Gaussian splatting, which we plan to use in the context of our project aiming on the automatic construction and site monitoring.

Hyperspectral Leaf Area Index and Chlorophyll Retrieval over Forest and Row-Structured Vineyard Canopies

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

Session 10. Oral Presentation for: Gross depositional environment mapping in the onshore South Australian Otway Basin: an example of data integration

Session 10. Oral Presentation for: Gross depositional environment mapping in the onshore South Australian Otway Basin: an example of data integration

A Context-Aware Navigation Framework for Ground Robots in Horticultural Environments.

Environmental mapping and robot navigation are the basis for realizing robot automation in modern agricultural production. This study proposes a new autonomous mapping and navigation method for gardening scene robots. First, a new LiDAR slam-based semantic mapping algorithm is proposed to enable the robots to analyze structural information from point cloud images and generate roadmaps from them. Secondly, a general robot navigation framework is proposed to enable the robot to generate the shortest global path according to the road map, and consider the local terrain information to find the optimal local path to achieve safe and efficient trajectory tracking; this method is equipped in apple orchards. The LiDAR was evaluated on a differential drive robotic platform. Experimental results show that this method can effectively process orchard environmental information. Compared with vnf and pointnet++, the semantic information extraction efficiency and time are greatly improved. The map feature extraction time can be reduced to 0.1681 s, and its MIoU is 0.812. The resulting global path planning achieved a 100% success rate, with an average run time of 4ms. At the same time, the local path planning algorithm can effectively generate safe and smooth trajectories to execute the global path, with an average running time of 36 ms.

Analysis of Priority Conservation Areas Using Habitat Quality Models and MaxEnt Models.

This study investigated core habitat areas for yellow-throated martens (Martes flavigula) and leopard cats (Prionailurus bengalensis), two endangered forest species sensitive to habitat fragmentation in Korea. Overlaying the InVEST-HQ and MaxEnt models, priority conservation areas were identified by analyzing gaps in currently protected areas. The InVEST-HQ model showed that habitat quality ranged from 0 to 0.86 on a scale from 0 to 1, and the majority of the most suitable areas on the Environmental Conservation Value Assessment Map, designated as grade 1, were derived correctly. The MaxEnt model analysis accurately captured the ecological characteristics of the yellow-throated marten and the leopard cat and identified probable regions of occurrence. We analyzed the most suitable yellow-throated marten and leopard cat habitats by superimposing the two results. Gap analysis determined gaps in existing protected areas and identified priority conservation areas. The core area (14.7%) was mainly distributed in forests such as the Baekdudaegan Mountains Reserve in regions such as Gyeongbuk, Gyeongnam, and Gangwon; 12.9% was outside protected areas, and only 1.8% was protected. The overlap results between protected and non-protected areas were compared with different land use types. Conservation priority areas were identified as those with more than 95% forest cover, offering an appropriate habitat for the two species. These findings can be used to identify priority conservation areas through objective habitat analysis and as a basis for protected area designation and assessment of endangered species habitat conservation, thereby contributing to biodiversity and ecosystem conservation.

DMOT-SLAM: visual SLAM in dynamic environments with moving object tracking

Visual simultaneous localization and mapping (SLAM) in dynamic environments has received significant attention in recent years, and accurate segmentation of real dynamic objects is the key to enhancing the accuracy of pose estimation in such environments. In this study, we propose a visual SLAM approach based on ORB-SLAM3, namely dynamic multiple object tracking SLAM (DMOT-SLAM), which can accurately estimate the camera’s pose in dynamic environments while tracking the trajectories of moving objects. We introduce a spatial point correlation constraint and combine it with instance segmentation and epipolar constraint to identify dynamic objects. We integrate the proposed motion check method into DeepSort, an object tracking algorithm, to facilitate inter-frame tracking of dynamic objects. This integration not only enhances the stability of dynamic features detection but also enables the estimation of global motion trajectories for dynamic objects and the construction of object-level semi-dense semantic maps. We evaluate our approach on the public TUM, Bonn, and KITTI dataset, and the results show that our approach has a significant improvement over ORB-SLAM3 in dynamic scenes and performs better compared to other state-of-the-art SLAM approaches. Moreover, experiments in real-world scenarios further substantiate the effectiveness of our approach.

Cognitive radio network architecture for GEO and LEO satellites shared downlink spectrum

The fixed spectrum assignment policy in the space sector and large constellations of Low Earth Orbit (LEO) satellites left little or no spectrum available for future LEO satellite communications services. Cognitive Radio (CR) technology enables spectrum sharing between primary and secondary users without limiting the transmission power, and thus is of great interest to commercial and defense entities. A number of Radio Environment Map (REM) techniques have been making Cognitive Radio Networks (CRNs) practical by constructing a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations. In this paper, we investigate spectrum sharing for a network comprised of a Geostationary Orbit (GEO) and a LEO satellite with a multibeam antenna array. A CRN architecture of GEO and LEO satellites shared downlink spectrum is proposed and details are provided covering its architecture, REM structure and channel utilization data aggregation, as well as a frequency slot assignment mechanism.

User Localization and Environment Mapping With the Assistance of RIS

User Localization and Environment Mapping With the Assistance of RIS

Integrating artificial intelligence with SLAM technology for robotic navigation and localization in unknown environments

In the era of advancing technology, unmanned inspection robots have become indispensable for their efficiency, precision, and safety. Key to their autonomous operation is Simultaneous Localization and Mapping (SLAM) technology, which allows robots to navigate and create maps of unknown environments in real-time. This article explores the integration of SLAM with artificial intelligence, highlighting its role in robotic navigation, localization, and obstacle avoidance. Specifically, we delve into SLAM's principles, its implementation with LiDAR technology, and its application in autonomous robot localization. Furthermore, we introduce a collaborative mapping algorithm based on ORB-SLAM3, enhancing map construction efficiency and real-time performance. Through our exploration, we illustrate the transformative potential of SLAM technology, paving the way for safer and more efficient robotic inspection systems across various industries.

Geophysical Investigation and 3D Modeling of Bedrock Morphology in an Urban Sediment-Filled Basin: The Case of Bolzano (Northern Italy)

Bedrock mapping is essential for understanding seismic amplification, particularly in sediment-filled valleys or basins. However, this can be hard in urban environments. We conducted a geophysical investigation of the sediment-filled Bolzano basin in Northern Italy, where three valleys converge. This study uses low-impact, single-station geophysical methods suitable for urban areas to address the challenges of mapping in such environments. A dataset of 574 microtremor and gravity measurements, along with three seismic reflection lines, allows for the inference of the basin’s deep bedrock morphology, even without direct stratigraphic data. The dataset facilitates a detailed analysis of the spatial patterns of resonance frequencies and amplitudes, revealing 1D and 2D characteristics of the resonances. Notably, 2D resonances predominate along the Adige valley, i.e., the deepest part of the basin with depths up to 900 m. These 2D resonances, which cannot be interpreted through simple 1D frequency-depth relationships, are better understood by integrating gravity data to develop a depth model. The study identifies resonance frequencies ranging from 0.27 to over 3 Hz in Bolzano, affecting different building types during earthquakes. Maximum resonance amplitudes occur at lower frequencies, specifically at 2D resonance sites, therefore primarily impacting high structures. The 2D resonances are directional, with the most significant amplification occurring longitudinally along the valley axes. The resulting 3D bedrock model aids in seismic site response modeling, hydrogeological studies, and geothermal exploration and provides insights into the geological history of the basin, highlighting the role of the Adige Valley as a major drainage pathway.

A Comparative Review on Enhancing Visual Simultaneous Localization and Mapping with Deep Semantic Segmentation.

Visual simultaneous localization and mapping (VSLAM) enhances the navigation of autonomous agents in unfamiliar environments by progressively constructing maps and estimating poses. However, conventional VSLAM pipelines often exhibited degraded performance in dynamic environments featuring mobile objects. Recent research in deep learning led to notable progress in semantic segmentation, which involves assigning semantic labels to image pixels. The integration of semantic segmentation into VSLAM can effectively differentiate between static and dynamic elements in intricate scenes. This paper provided a comprehensive comparative review on leveraging semantic segmentation to improve major components of VSLAM, including visual odometry, loop closure detection, and environmental mapping. Key principles and methods for both traditional VSLAM and deep semantic segmentation were introduced. This paper presented an overview and comparative analysis of the technical implementations of semantic integration across various modules of the VSLAM pipeline. Furthermore, it examined the features and potential use cases associated with the fusion of VSLAM and semantics. It was found that the existing VSLAM model continued to face challenges related to computational complexity. Promising future research directions were identified, including efficient model design, multimodal fusion, online adaptation, dynamic scene reconstruction, and end-to-end joint optimization. This review shed light on the emerging paradigm of semantic VSLAM and how deep learning-enabled semantic reasoning could unlock new capabilities for autonomous intelligent systems to operate reliably in the real world.

Effect of genomic and cellular environments on gene expression noise

BackgroundIndividual cells from isogenic populations often display large cell-to-cell differences in gene expression. This “noise” in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome.ResultsTo address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we perform the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We find that the mean and noise of expression correlate with different histone modifications. We quantify the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assign the extrinsic component to differences between the CD24+ “stem-like” substate and the more “differentiated” substate. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for “safe-harbor” loci.ConclusionsTaken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome and that the data generatd by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.

Sustainability challenges in airlines contemporary environmental management

Environmental concerns attract growing attention in aviation. Airlines and other aviation stakeholders are increasingly committed to more environmentally friendly operations. This paper aims to expand the understanding of the airlines' environmental attitudes and map best practices in the sector. To achieve this goal, the authors followed a two-stage analysis. Initially, a number of interviews were conducted with a focus group of key respondents from six Greek airlines, to draw the general picture of their environmental awareness. In the second stage, the authors studied the market's dominant airlines as a case to gain a deeper understanding of how an Environmental Management System (EMS) is deployed in practice and to allow for best practices to emerge. The research findings highlight the need for EMSs to become an integral part of airlines' operational strategy and the imperative for an articulate regulatory framework able to enhance the airlines' eco-path through concrete guidelines and knowledge exchange.

Autonomous navigation system for greenhouse tomato picking robots based on laser SLAM

Aiming at the specific operation path cruising problem of greenhouse tomato picking robots, this paper designs a laser SLAM (Simultaneous Localization And Mapping)-based autonomous navigation system for greenhouse tomato picking robots.The autonomous navigation system utilizes LiDAR to detect the greenhouse environment, creates an environment map with the Gmapping algorithm employing particle filtering, locates the robot with the AMCL (Adaptive Monte Carlo Localization) algorithm, devises the robot's path for picking tasks with the A* algorithm, and dynamically navigates around obstacles during operations with the DWA (Dynamic Window Approach) algorithm. The greenhouse experiment revealed that the robot's average deviance for both directions (X and Y) is 2.87 and 0.80 centimeters, respectively, when traveling a 320 centimeter path. On the other hand, its average variation across the directions of X and Y is 1.48 versus 1.08 centimeters when it passes through a 40 centimeter zone. The robot exhibits averaged variations of 1.73 and 2.24 millimeters in the X and Y axes, each, when navigating a 40-centimeter-radius turn. Furthermore, the robot exhibits average variations of 1.12 centimeters in the X and 0.78 centimeters in the Y directions when it follows a certain "S"-shaped course. Based on the fulfillment of prescribed path cruising, the system has a high level of navigation and positioning accuracy, better meeting the requirements for greenhouse tomato harvesting robot direction and placement.

Multiscale spatiotemporal characterisation of embodied environmental performance of building structures in Geneva from 1850 to 2018

Load-bearing systems in buildings, significant in material use and embodied greenhouse gas emissions (EGHGE), have lacked detailed analysis on their environmental and functional relationships over time and space. This study evaluates the environmental impacts of building structures in Geneva, Switzerland, considering factors like material usage, EGHGE, and urban development. A new method using a similarity-weighted function projects environmental impacts onto a GIS-based building stock, analysing 48 archetypal and 84,477 stock buildings built from 1850 to 2018. Results show a 37% reduction in structural volume per floor area and a 10% increase in mass over time. Buildings predating the masonry-to-concrete transition would produce 7% more EGHGE if constructed today. Multi-residential buildings emit 14% less EGHGE than single homes. A new indicator amortizes upfront environmental effects over a building's lifespan, aiding in historical comparisons of building stocks. This approach underscores the need for spatial-temporal environmental impact mapping to understand sustainable urban development dynamics.