Rapid Mapping Research Articles

Horodynamic Mapping of Open Sands in the East of the Stavropol Region

The desertification processes that are dynamically occurring in the north-east of the Stavropol Territory are closely interrelated with climatic conditions, which necessitates the rapid mapping of qualitative changes in the state of open sands in accordance with various scenarios. The mapping was carried out on the basis of the technique of chorodynamic (diachronic) mapping for two periods (July-August 2022 and June-July 2023), data on the monthly dynamics of open sand areas, precipitation and dust storms were used to select time slices. The mapping was carried out visually on the basis of multispectral satellite images of Landsat for each month. In 2022, 14 dust storms were registered, in 2023 none were registered, and days with critical wind speeds were accompanied by heavy precipitation, in connection with which a significant decrease in the visible areas of open sands was revealed as a result of overgrowth. Based on the available data, using overlay operations, schemes of qualitative transitions in the test area (part of the Levokumsky district) corresponding to qualitative transitions in the “dry” and “wet” scenarios were compiled. Despite the progressive overgrowth of the sands, stimulated by the absence of dust storms and abundant precipitation in 2023, starting from areas with the lowest sediment capacity, the restoration of the original plant communities without carrying out phytomeliorative measures is extremely unlikely due to high anthropogenic load and seed desertification, as a result of which low-value annual crops grow on open sands (weed solyanka, kumarchik, nightshade horned, etc.).

Rapid and precise large area mapping of rare-earth doping homogeneity in luminescent materials

Rapid and precise large area mapping of rare-earth doping homogeneity in luminescent materials

Monitoring soil salinity in coastal wetlands with Sentinel-2 MSI data: Combining fractional-order derivatives and stacked machine learning models

Monitoring soil salinity is essential for understanding the behavior of coastal wetland ecosystems and implementing effective management strategies. Despite the advantages of the Multi-Spectral Instrument (MSI) data for large-scale, high-frequency soil salinity monitoring, challenges remain in data preprocessing and model construction. We combined fractional-order derivative (FOD) technology with stacked machine learning models to monitor and map soil salinity using Sentinel-2 MSI data. The base models included Elastic Net Regression, Support Vector Regression, Artificial Neural Network, Extreme Gradient Boosting, and Random Forest, with Non-Negative Least Squares as the meta-learner. The results showed that low-order FOD enhanced image gradients and maintained a high peak signal-to-noise ratio, thereby improving the correlation with soil salinity. Notably, the 0.25-order FOD showed the best performance, increasing the correlation coefficient with soil salinity by up to 13 %. The stacked machine learning models effectively combined the strengths of different base models, enhancing prediction accuracy by more than 8 % compared to single models. Furthermore, combining stacked models with FOD further improved prediction accuracy, with an increase in R² of up to 9 %. The combination of 0.25-order FOD and the stacked machine learning model achieved the best performance (R² = 0.82, RMSE = 10.19 ppt, RPD = 2.38, RPIQ = 4.69). This approach provides a reference for rapid and effective large-scale digital mapping of soil salinity in coastal wetlands.

Complexity‐Building Exhaustive Dearomatization of Benzenoid Aromatics within an ESIPT‐Initiated Three‐Step Photochemical Cascade.

AbstractDearomative cycloadditions offer rapid access to complex 3D molecular architectures, commonly via a sp2‐to‐sp3 rehybridization of two atoms of an aromatic ring. Here we report that the 6e π‐system of a benzenoid aromatic pendant could be exhaustively depleted within a single photochemical cascade. An implementation of this approach involves the initial dearomative [4+2] cycloaddition of the Excited State Intramolecular Proton Transfer (ESIPT)‐generated azaxylylene, followed by two consecutive [2+2] cycloadditions of auxiliary π moieties strategically positioned in the photoprecursor. Such photochemical cascade fully dearomatizes the benzenoid aromatic ring, saturating all six sp2 atoms to yield a complex sp3‐rich scaffold with high control of its 3D molecular shape, rendering it a robust platform for rapid systematic mapping of underexplored chemical space. Significant growth of molecular complexity—starting with a modular synthesis of photoprecursors from readily available building blocks—is quantified by Böttcher score calculations.

Rapid lithological mapping using multi-source remote sensing data fusion and automatic sample generation strategy

ABSTRACT The advancement of remote sensing technology aids geologists in obtaining lithological maps more quickly, comprehensively, and accurately. However, key challenges in lithological mapping include the limited spectral information from individual sensors and the difficulties in visually interpreting lithological samples. In this study, we integrated 241 scenes of optical data and 106 scenes of radar data on the Google Earth Engine (GEE) platform, proposing a rapid lithological identification framework that combines an automatic lithological sample data generation strategy with multi-source data. Using various machine learning algorithms, we evaluated the classification capabilities of heterogeneous predictive factors, feature optimization algorithms, and object-based algorithms. Results indicate that: (1) Combining optical and radar data improves prediction accuracy, with terrain data further enhancing mapping capabilities; (2) Terrain factors contribute most to classification, but SWIR and TIR bands of optical data are critical for lithological identification; (3) The feature optimization algorithm reduces feature redundancy and efficiency issues from multi-source data, achieving 96.51% accuracy with the optimal feature model, an improvement of 0.1%−2.02% over original features; (4) Object-based algorithms show significant potential in mapping areas with large rock outcrops. This study offers new insights for medium- to large-scale lithological maps and provides essential data support for geological work.

Regional Rapid Mapping for First Responders - Turkey 2023 Earthquake

Regional Rapid Mapping for First Responders - Turkey 2023 Earthquake

A Methodological Framework for High-Resolution Surface Urban Heat Island Mapping: Integration of UAS Remote Sensing, GIS, and the Local Climate Zoning Concept

The urban heat island effect (UHI) is among the major challenges of urban climate, which is continuously intensifying its impact on urban life and functioning. Against the backdrop of increasingly prolonged heatwaves observed in recent years, practical questions about adaptation measures in cities are growing—questions that traditional meteorological monitoring can hardly answer adequately. On the other hand, UHI has long been the focus of research interest, but due to the technological complexity of providing accurate spatially referenced data at high spatial resolution and the requirement to survey at strictly defined parts of the day, information provision is becoming a major challenge. This is one of the main reasons why UHI research results are less often used directly in urban spatial planning. However, advances in geospatial technologies, including unmanned aerial systems (UASs), are providing more and more reliable tools that can be applied to achieve better and higher-quality information resources that adequately characterize the UHI phenomenon. This paper presents a developed and tested methodology for the rapid and efficient assessment and mapping of the effects of surface urban heat island (SUHI). It is entirely based on the integrated use of data from unmanned aerial systems (UAS)-based remote sensing methods, including thermal photogrammetry and GIS-based analysis methods. The study follows the understanding that correct SUHI research depends on a proper understanding of the urban geosystem, its spatial and structural heterogeneity, and its functional systems, which in turn can only be achieved by supporting the research process with accurate and reliable information resources. In this regard, the possibilities offered by the proposed methodological scheme for efficient geospatial registration of SUHI variations at the microscale, including the calculation of intra-urban SUHI intensity, are discussed in detail. The methodology builds on classical approaches for using local climate zoning (LCZ), adding capabilities for precise delineation of individual zone types and for geostatistical characterization of the urban surface heat island (SUHI). Finally, the proposed scheme is based on state-of-the-art technological tools that provide flexible and automated capabilities to investigate the phenomenon at microscales, including by enabling flexible observation of its dynamics in terms of heat wave manifestation and evolution. Results are presented from a series of sequential tests conducted on the largest residential area in Bulgaria’s capital city, Sofia, in terms of area and population, over a relatively long period from 2021 to 2024.

DiScO: novel rapid systems mapping to inform digital transformation of health systems.

Global health systems are confronting challenges that intersect climate change with evolving communicable and non-communicable public health risks. Addressing these challenges requires systems integration via citizen big data that exist outside health systems. However, systems integration across jurisdictions is a complex challenge that requires stakeholder input. This study's purpose was to conduct rapid systems mapping with international health system stakeholders to inform the development and implementation of a global digital citizen science observatory (DiScO), which aims to catalyze digital transformation of health systems across jurisdictions. A rapid qualitative systems mapping study was conducted during the International Society for Behavioral Nutrition and Physical Activity Annual Global Summit in Uppsala, Sweden, in June 2023. The choice of the venue and approach was informed by three key criteria: (1) Established evidence linking physical activity and nutrition with non-communicable diseases; (2) Concrete existing methods of obtaining citizen big data by physical activity and nutrition researchers; (3) Precedence of physical activity and nutrition researchers conducting citizen science as well behavioral/clinical big data collection. The design of this study was an innovative pre-post systems map development, which consisted of (1) real-time rapid systems mapping (pre/initial map) by engaging with international stakeholders and (2) adjustment of the real-time systems map (post/final map) after analyzing stakeholder discussion data. Rapid systems mapping resulted in a complex network that included key themes to successfully develop and implement DiScO: priorities, opportunities, risks, challenges, partnerships, and resources. Additionally, a new theme emerged organically through stakeholder group discussions - mitigation strategies. The adapted rapid systems map (i.e., after data analyses) depicts 23 key nodes of intervention across the seven key themes. Rapid systems mapping at international symposia is a novel methodological approach to capture stakeholder input, particularly to understand complexity across international jurisdictions - an approach that can be replicated across disciplines and sectors to inform digital transformation of health systems. The development and implementation of DiScO, a platform for decentralization and democratization of technology, will take into consideration all the key nodes of intervention identified in the rapid systems map to promote digital health for equity across global jurisdictions.

Updated Profile of Clinical Manifestations of Onchocerciasis after Repeated Ivermectin Treatment in Middle Imo River Basin, Nigeria

We conducted a cross-sectional survey on the impact of ivermectin after repeated treatment in five communities in Imo State, Nigeria. Rapid epidemiological mapping of onchocerciasis (REMO) carried out in 450 subjects in 1994 prior to the launching of mass drug administration were compared with re-examinations of 540 subjects in 2023. We found palpable nodule reduction from 47.2% to 12.2% and popular dermatitis decreased from 42.9% to 5.9%. Reductions in other clinical features were observed. Overall, a significantly higher proportion of males were infected than females (P<0.05). The implications of these results were discussed in line with the reported benefits of ivermectin therapy.

Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data

Flood mapping using Synthetic Aperture Radar (SAR) data impose limitations in fully distinguishing flood under vegetation due to false double bounce returns from inundated tree trunks along with seasonal heterogeneities devised from changing land cover settings. In addition, rapid mapping of flooded vegetation is challenging during near real time applications. In this paper a fully automatic novel supervised classification approach called polarimetric Naïve Bayes is proposed that combines polarimetric information with series of Gaussian mixture models in Naïve Bayes framework to detect various flooded vegetation classes. It also allows the user to choose class configuration and eliminates creation of Region of Interest (ROI) for supervised training. The proposed approach uses scattering information from pre monsoon PolSAR dataset in training step to create ROIs for buildings and other features. In the next step series of Gaussian Mixtures are used for density estimation for different features in Bayesian multiclass problem. The newly developed classifier applied on 2016 Assam flood event resulted in precise mapping of at least three different vegetation classes under flood such as submerged vegetation, wetlands and floating vegetation. Under optimal class configuration, the approach showed better performance compared to other supervised techniques applied on the same data set such as MLE, Mahalanobis, Minimum Euclidean distance, and SVM classifications in delineating flood, submerged vegetation, wetlands and floating vegetation with Producer’s Accuracy of 98.6%, 81.1%, 94% and 51.5% respectively and combined Overall accuracy of 95.5% for flooded vegetation class. This method also detected multiple vegetation classes with better accuracy compared to similar methods.

Posture-dependent modulation of marmoset cortical motor maps detected via rapid multichannel epidural stimulation

Recent neuroimaging and electrophysiological studies have suggested substantial short-term plasticity in the topographic maps of the primary motor cortex (M1). However, previous methods lack the temporal resolution to detect rapid modulation of these maps, particularly in naturalistic conditions. To address this limitation, we previously developed a rapid stimulation mapping procedure with implanted cortical surface electrodes. In this study, employing our previously established procedure, we examined rapid topographical changes in forelimb M1 motor maps in three awake male marmoset monkeys. The results revealed that although the hotspot (the location in M1 that elicited a forelimb muscle twitch with the lowest stimulus intensity) remained constant across postures, the stimulus intensity required to elicit the forelimb muscle twitch in the perihotspot region and the size of motor representations were posture-dependent. Hindlimb posture was particularly effective in inducing these modulations. The angle of the body axis relative to the gravitational vertical line did not alter the motor maps. These results provide a proof of concept that a rapid stimulation mapping system with chronically implanted cortical electrodes can capture the dynamic regulation of forelimb motor maps in natural conditions. Moreover, they suggest that posture is a crucial variable to be controlled in future studies of motor control and cortical plasticity. Further exploration is warranted into the neural mechanisms regulating forelimb muscle representations in M1 by the hindlimb sensorimotor state.

FaSS-MVS: Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-Borne Monocular Imagery.

With FaSS-MVS, we present a fast, surface-aware semi-global optimization approach for multi-view stereo that allows for rapid depth and normal map estimation from monocular aerial video data captured by unmanned aerial vehicles (UAVs). The data estimated by FaSS-MVS, in turn, facilitate online 3D mapping, meaning that a 3D map of the scene is immediately and incrementally generated as the image data are acquired or being received. FaSS-MVS is composed of a hierarchical processing scheme in which depth and normal data, as well as corresponding confidence scores, are estimated in a coarse-to-fine manner, allowing efficient processing of large scene depths, such as those inherent in oblique images acquired by UAVs flying at low altitudes. The actual depth estimation uses a plane-sweep algorithm for dense multi-image matching to produce depth hypotheses from which the actual depth map is extracted by means of a surface-aware semi-global optimization, reducing the fronto-parallel bias of Semi-Global Matching (SGM). Given the estimated depth map, the pixel-wise surface normal information is then computed by reprojecting the depth map into a point cloud and computing the normal vectors within a confined local neighborhood. In a thorough quantitative and ablative study, we show that the accuracy of the 3D information computed by FaSS-MVS is close to that of state-of-the-art offline multi-view stereo approaches, with the error not even an order of magnitude higher than that of COLMAP. At the same time, however, the average runtime of FaSS-MVS for estimating a single depth and normal map is less than 14% of that of COLMAP, allowing us to perform online and incremental processing of full HD images at 1-2 Hz.

Efficient and low-drift point-cloud mapping method for GNSS weak and obstructed areas

Abstract Road modelling based on point-cloud data often encounters signal-rejection areas, where the absence of absolute position constraints leads to rapid error accumulation in the constructed map. To mitigate error growth, the manual setting of control points is necessary, which compromises automation in the mapping process and causes a challenging trade-off between efficiency and accuracy. To address these issues, in the present study, we designed a fast and efficient map-construction method that provides absolute pose constraints for simultaneous localisation and mapping (SLAM) based on the bidirectional filter position of global navigation satellite system (GNSS)/inertial measurement unit (IMU) integrated navigation. First, we designed an absolute position constraint factor (BF_Factor) updated at each epoch according to the reference-position difference and time-delay error, which can significantly mitigate error divergence during mobile mapping in a GNSS-rejected region. An adaptive noise model was then constructed for the above factors so that the weights of different factors could be adjusted automatically to achieve a more accurate mapping result. Finally, we conducted experimental tests on the proposed mapping approach in two scenarios that included multiple GNSS-rejected areas. Even when several hundred metres of GNSS signal-obstructed areas were present in the scene, the relocation results indicated that the three-dimensional position error obtained using point-cloud maps was <0.15 m and the pitch/roll error was better than 2°. Compared with Fastlio2 and Fastlio2-SC (Fastlio2 with Scan Context), the proposed method had a better mapping effect and higher mapping accuracy in the aforementioned scenarios. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications.

Rapid SLAM Method for Star Surface Rover in Unstructured Space Environments

The space environment is characterized by unstructured features, sparsity, and poor lighting conditions. The difficulty in extracting features from the visual frontend of traditional SLAM methods results in poor localization and time-consuming issues. This paper proposes a rapid and real-time localization and mapping method for star chart surveyors in unstructured space environments. Improved localization is achieved using multiple sensor fusion to sense the space environment. We replaced the traditional feature extraction module with an enhanced SuperPoint feature extraction network to tackle the challenge of challenging feature extraction in unstructured space environments. By dynamically adjusting detection thresholds, we achieved uniform detection and description of image keypoints, ultimately resulting in robust and accurate feature association information. Furthermore, we minimized redundant information to achieve precise positioning with high efficiency and low power consumption. We established a star surface rover simulation system and created simulated environments resembling Mars and the lunar surface. Compared to the LVI-SAM system, our method achieved a 20% improvement in localization accuracy for lunar scenarios. In Mars scenarios, our method achieved a positioning accuracy of 0.716 m and reduced runtime by 18.682 s for the same tasks. Our approach exhibits higher localization accuracy and lower power consumption in unstructured space environments.

Multi-Robot Path Planning Algorithm for Collaborative Mapping under Communication Constraints

In extensive outdoor collaborative exploration tasks, multiple robots require efficient path planning methods to ensure rapid and comprehensive map construction. However, current collaborative mapping algorithms often integrate poorly with path planning, especially under limited communication conditions. Such conditions can complicate data exchange, leading to inefficiencies and missed areas in real-world environments. This paper introduces a path planning approach specifically designed for distributed collaborative mapping tasks, aimed at enhancing map completeness, mapping efficiency, and communication robustness under communication constraints. We frame the entire task as a k-Chinese Postman Problem (k-CPP) and optimize it using a genetic algorithm (GA). This method fully leverages topology maps to efficiently plan subpaths for multiple robots, ensuring thorough coverage of the mapping area without the need for prior navigation maps. Additionally, we incorporate communication constraints into our path planning to ensure stable data exchange among robots in environments with only short-range communication capabilities. Field experiment results highlight the superior performance of our method in terms of stability, efficiency, and robust inter-robot communication.

Calibrated absolute optical contrast for high-throughput characterization of horizontally aligned carbon nanotube arrays

Horizontally aligned carbon nanotube (HACNT) arrays hold significant potential for various applications in nanoelectronics and material science. However, their high-throughput characterization remains challenging due to the lack of methods with both high efficiency and high accuracy. Here, we present a novel technique, Calibrated Absolute Optical Contrast (CAOC), achieved through the implementation of differential principles to filter out stray signals and high-resolution calibration to endow optical contrast with physical significance. CAOC offers major advantages over previous characterization techniques, providing consistent and reliable measurements of HACNT array density with high throughput and non-destructive assessment. To validate its utility, we demonstrate wafer-scale uniformity assessment by rapid density mapping. This technique not only facilitates the practical evaluation of HACNT arrays but also provides insights into balancing high throughput and high resolution in nanomaterial characterization.

Dynamic data driven load-carrying capacity prediction method for composite laminates with delamination

The delamination of composites has invisibility and can reduce the load-carrying capacity (LCC) of structures. In this paper, a dynamic data driven LCC prediction method for composite laminates considering delamination is proposed. The method is divided into offline and online phases: In the offline phase, finite element modeling, prior information acquisition, and database construction are carried out. In the online phase, the Bayes factor is used to realize the rapid mapping between sensor data and the sample in the database to predict the LCC of the structure. In numerical cases with noise, the prediction errors of LCC are within 2.3% with sensor data, and the introduction of prior information can improve efficiency and accuracy of the prediction. The material parameters and delamination sizes of the identification results are the samples closest to the given solution in the database. In test cases, the information from strain arrays can effectively reflect the delamination growth of the laminate. After three times data assimilation, the prediction errors of LCC are within 10.5%.

Comparison of a new MR rapid wash-out map with MR perfusion in brain tumors

BackgroundMR perfusion is a standard marker to distinguish progression and therapy-associated changes after surgery and radiochemotherapy for glioblastoma. TRAMs (Treatment Response Assessment Maps) were introduced, which are intended to facilitate the differentiation of vital tumor cells and radiation necrosis by means of late (20–90 min) contrast clearance and enhancement. The differences of MR perfusion and late-enhancement are not fully understood yet.MethodsWe have implemented and established a fully automated creation of rapid wash-out (15–20 min interval) maps in our clinic. We included patients with glioblastoma, CNS lymphoma or brain metastases who underwent our MR protocol with MR perfusion and rapid wash-out between 01/01/2024 and 30/06/2024. Since both wash-out and hyperperfusion are intended to depict the active tumor area, this study involves a quantitative and qualitative comparison of both methods. For this purpose, we volumetrically measured rCBV (relative cerebral blood volume) maps and rapid wash-out maps separately (two raters). Additionally, we rated the agreement between both maps on a Likert scale (0–10).ResultsThirty-two patients were included in the study: 15 with glioblastoma, 7 with CNS lymphomas and 10 with brain metastasis. We calculated 36 rapid wash-out maps (9 initial diagnosis, 27 follow-up).Visual agreement of MR perfusion with rapid wash-out by rating were found in 44 ± 40% for initial diagnosis, and 75 ± 31% for follow-up. We found a strong correlation (Pearson coefficient 0.92, p < 0.001) between the measured volumes of MR perfusion and rapid wash-out. The measured volumes of MR perfusion and rapid wash-out did not differ significantly. Small lesions were often not detected by MR perfusion. Nevertheless, the measured volumes showed no significant differences in this small cohort.ConclusionsRapid wash-out calculation is a simple tool that provides new information and, when used in conjunction with MR perfusion, may increase diagnostic accuracy. The method shows promising results, particularly in the evaluation of small lesions.

Complexity-Building Exhaustive Dearomatization of Benzenoid Aromatics within an ESIPT-Initiated Three-Step Photochemical Cascade.

Dearomative cycloadditions offer rapid access to complex 3D molecular architectures, commonly via a sp2-to-sp3 rehybridization of two atoms of an aromatic ring. Here we report that the 6e π-system of a benzenoid aromatic pendant could be exhaustively depleted within a single photochemical cascade. An implementation of this approach involves the initial dearomative [4+2] cycloaddition of the Exited State Intramolecular Proton Transfer (ESIPT)-generated azaxylylene, followed by two consecutive [2+2] cycloadditions of auxiliary π moieties strategically positioned in the photoprecursor. Such photochemical cascade fully dearomatizes the benzenoid aromatic ring, saturating all six sp2 atoms to yield a complex sp3-rich scaffold with high control of its 3D molecular shape, rendering it a robust platform for rapid systematic mapping of underexplored chemical space. Significant growth of molecular complexity - starting with a modular synthesis of photoprecursors from readily available building blocks - is quantified by Böttcher score calculations.

Rapid Airborne Gas-plume Mapping and Source Localization with Feedforward Gas-sensor Dynamics Compensation

Rapid Airborne Gas-plume Mapping and Source Localization with Feedforward Gas-sensor Dynamics Compensation