Change Detection Research Articles

Utilising dynamic motor control index to identify age-related differences in neuromuscular control

PurposeConsidering the relationship between aging and neuromuscular control decline, early detection of age-related changes can ensure that timely interventions are implemented to attenuate or restore neuromuscular deficits. The dynamic motor control index (DMCI), a measure based on variance accounted for (VAF) by one muscle synergy (MS), is a metric used to assess age-related changes in neuromuscular control. The aim of the study was to investigate the use of one-synergy VAF, and consecutively DMCI, in assessing age-related changes in neuromuscular control over a range of exercises with varying difficulty. MethodsThirty-one subjects walked on a flat and inclined treadmill, as well as performed forward and lateral stepping up tasks. Motion and muscular activity were recorded, and muscle synergy analysis was conducted using one-synergy VAF, DMCI, and number of synergies. ResultsDifference between older and younger group was observed for one-synergy VAF, DMCI for forward stepping up task (one-synergy VAF difference of 2.45 (0.22, 4.68) and DMCI of 9.21 (0.81, 17.61), p = 0.033), but not for lateral stepping up or walking. ConclusionThe use of VAF based metrics and specifically DMCI, rather than number of MS, in combination with stepping forward exercise can provide a low-cost and easy to implement approach for assessing neuromuscular control in clinical settings.

Lower Childhood Socioeconomic Status Is Associated with Greater Neural Responses to Ambient Auditory Changes in Adulthood.

Humans' early life experience varies by socioeconomic status (SES), raising the question of how this difference is reflected in the adult brain. An important aspect of brain function is the ability to detect salient ambient changes while focusing on a task. Here, we ask whether subjective social status during childhood is reflected by the way young adults' brain detecting changes in irrelevant information. In two studies (total n = 58), we examine electrical brain responses in the frontocentral region to a series of auditory tones, consisting of standard stimuli (80%) and deviant stimuli (20%) interspersed randomly, while participants were engaged in various visual tasks. Both studies showed stronger automatic change detection indexed by MMN in lower SES individuals, regardless of the unattended sound's feature, attended emotional content, or study type. Moreover, we observed a larger MMN in lower-SES participants, although they did not show differences in brain and behavior responses to the attended task. Lower-SES people also did not involuntarily orient more attention to sound changes (i.e., deviant stimuli), as indexed by the P3a. The study indicates that individuals with lower subjective social status may have an increased ability to automatically detect changes in their environment, which may suggest their adaptation to their childhood environments.

Analyzing Urbanization using Satellite Image Processing

Abstract: Urbanization is directly proportional to the increase in population, which contributes to the increase in country’s GDP but affects the environment and ecosystem. This paper introduces an analysis of the satellite imagery of Mumbai for past 5 years (2019-2023). Mumbai has witnessed significant changes in the geographic region. In order to control and prevent the effects of urbanization, it is necessary to analyze the geographical changes and effects of the same. In this proposed analysis, we have analyzed urbanization with respect to changes in water, forest, urban land and barren land. Use of satellite imagery plays an important role in providing the opportunity for this analysis. Google Earth Engine (GEE) is used for the change detection for the selected area using the satellite imagery dataset provider COPERNICUS named Harmonized Sentinel-2 MSI as an input to this analysis. For this analysis we have used 3 classification methods i.e. supervised, unsurprised and NDVI

Spatio-temporal dynamic analysis of greenness base on NDVI equation for change detection from 2004 to 2024 at River Nile State, Atbara City, Sudan by using remote sensing and geographical information system

Urban regions' ability to maintain high environmental standards is largely dependent on their vegetation. A decrease in the amount of vegetated area on land has resulted from city expansion and population growth. An examination of land cover change in urban settings is necessary, particularly for urban regional planning that takes green, open space into account. The purpose of this study was to examine changes in urban vegetation cover in two Medan sub-districts from 2004 to 2014. Change analysis and the Normalized Difference Vegetation Index (NDVI) were performed in the study. The range of plant diversity in these locations was Observed. The results showed changes in vegetation cover areas in the mentioned years. In 2004, most of the areas were under a highly dense vegetation class while in 2014, they were under a low-density vegetation class with new sub-class. This indicates a decrease in vegetation cover due to changes to non-vegetation cover or land cover areas with less vegetation.

Longitudinal assessment of extreme climate events in Kinnaur district, Himachal Pradesh, north-western Himalaya, India.

It is vital to keep an eye on changes in climatic extremes because they set the stage for current and potential future climate, which usually have a reasonable adverse impact on ecosystems and society. The present study examines the variability and trends in precipitation and temperature across seasons in the Kinnaur district, offering valuable insights into the complex dynamics ofthe Himalayan climate. Using Climatic Research Unit gridded Time Series (CRU TS) datasets from 1951 to 2021, the study analyzes the data to produce 28 climate indices based on India Meteorological Department (IMD) convention indices and Expert Team on Climate Change Detection and Indices (ETCCDI). Although there may be considerable variation in climate indices in terms of absolute values within different products, there is consensus in both long-term trends and inter-annual variability. Analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Initially, the datawere subjected to rigorous quality control procedures, which involved identifying anomalies. Statistical analysis like trend analysis, employing Mann-Kendall test and Sen's slope estimator, reveal significant (p < 0.05) increase in consecutive dry days (CDD) at 0.03days/year and decrease in consecutive wet days (CWD) at 0.02days/year. Notably, the frequency of heavy precipitation occurrences showed an increasing trend. Changes in precipitation in the Western Himalaya are driven by a complex interplay of orographic effects, monsoonal dynamics, atmospheric circulation patterns, climate change, and localized factors such as topography, atmospheric circulation patterns, moisture sources, land-sea temperature contrasts, and anthropogenic influences. Moreover, in case of temperature indices, there is significant increasing trend observed. Temperature indices indicate a significant annual increase in warm nights (TN90p) at 0.06%/year and warm days (TX90p) at 0.11%/year. Extreme temperature events have been trending upward, with monthly daily maximum temperature (TXx) increasing by 1.5°C yearly. This study enhances our comprehension of the global warming phenomenon and underscores the importance of acknowledging alterations in the water cycle and their repercussions on hydrologic resources, agriculture, and livelihoods in the cold desert of the northwestern Indian Himalaya.

Machine learning-based detection of sudden air pollutant level changes: impacts on public health

Machine learning-based detection of sudden air pollutant level changes: impacts on public health

Testing for distributional structural change with unknown breaks: application to pricing crop insurance contracts

Abstract Agriculture in developed countries is produced under heavily subsidized insurance. The pricing of these insurance contracts, termed premium rates, directly influences farmers profits, their financial solvency, and indirectly, global food security. Changing climate and technology have likely caused significant shifting of mass in crop yield distributions and, if so, has rendered some of the historical yield data irrelevant for estimating premium rates. Insurance is primarily interested in lower tail probabilities and as such the detection of structural change in tail probabilities or higher moments is of great concern for the efficacy of crop insurance programs. We propose a test for structural change with an unknown break(s) which has power against structural change in any moment and can be tailored to a specific range of the underlying distribution. Simulations demonstrate better finite sample performance relative to existing methods and reasonable performance at identifying the break. The asymptotic distribution is shown to follow the Kolmogorov distribution. Our proposed test finds structural change in most major U.S. field crop yields leading to significant premium rate differences. Results of an out-of-sample premium rating game indicate that incorporating structural change in crop yields leads to more accurate premium rates.

Multi-task contrastive learning for change detection in remote sensing images

ABSTRACT This letter proposes a novel multi-task contrastive learning (MTCL) approach for change detection of high-resolution remote sensing images. Current self-supervised learning approaches have limitations in utilizing multiview information of remote sensing images, and they cannot directly train the change detection backbone network for feature learning. The investigated method exploits multiview information contained in remote sensing images to directly train the change detection backbone network. Specifically, multiview views are constructed from high-resolution remote sensing images by handcrafted feature extraction approach, which contain complementary information from different domains. Then each constructed view is utilized for contrastive learning, and the multi-task learning strategy of parameter sharing is used to learn more rich and robust information. Furthermore, instead of learning feature representations from single remote sensing image, the proposed approach directly utilizes image pairs to train the change detection backbone, further improving the performance of change detection network. Two benchmark datasets are employed for comparative experiments, and experimental results certify the effectiveness and superiority of the investigated approach.

Cross‐attention neural network for land cover change detection with remote sensing images

AbstractLand cover change detection (LCCD) with remote sensing images (RSIs) is important for observing the land cover change of the Earth's surface. Considering the insufficient performance of the traditional self‐attention mechanism used in a neural network to smoothen the noise of LCCD with RSIs, in this study a novel cross‐attention neural network (CANN) was proposed for improving the performance of LCCD with RSIs. In the proposed CANN, a cross‐attention mechanism was achieved by employing another temporal image to enhance attention performance and improve detection accuracies. First, a feature difference module was embedded in the backbone of the proposed CANN to generate a change magnitude image and guide the learning progress. A self‐attention module based on the cross‐attention mechanism was then proposed and embedded in the encoder of the proposed network to make the network pay attention to the changed area. Finally, the encoded features were decoded to obtain binary change detection with the ArgMax function. Compared with five methods, the experimental results based on six pairs of real RSIs well demonstrated the feasibility and superiority of the proposed network for achieving LCCD with RSIs. For example, the improvement for overall accuracy for the six pairs of real RSIs improved by our proposed approach is about 0.72–2.56%.

Applications of satellite data for rapid inundation assessment - A case study in Thua Thien Hue province

Floods are one of the most significant devastating natural hazards around the globe. Recent development in remote sensing technology supports faster and low-cost analysis of flood hazards. This study used the Sentinel-1 SAR data for flood mapping and damage assessment. We selected the October 2022 flood event in Thua Thien Hue province, Vietnam. The Change Detection and Thresholding (CDAT) method was adopted to detect the inundation areas. The results showed that The flood event affected to several districts with Quang Dien being the worst hit, followed by Phu Loc and Huong Tra. Phu Vang had the smallest flooded area. The total inundated area was up to 33,384.08ha. The derived flood map was overlaid onto the current land cover map to conduct an initial evaluation of the potential flood damage corresponding to each land use category. The damage to the land cover was estimated that most of the affected areas were in the cropland and accounted for 77.07% of the total inundated area. The results can assist decision-makers in monitoring and assessing flood damage in Central Vietnam

Matching the building footprints of different vector spatial datasets at a similar scale based on one-class support vector machines

Automatic matching of multisource data is an important technique for achieving change detection, fusion and updating spatial data. However, most current learning methods for building footprint matching require a large number of samples, and labeling these samples is costly in terms of labor and time. Moreover, multisource building footprint data are complex and diverse leading to recognizing the different matching relationships is a hard task. Thus, this study proposes a learning-based method for recognizing multisource building footprints matching relationships by using a one-class support vector machine (OCSVM). The OCSVM was trained using only positive samples. First, a set of geometric indicators was designed to train a model and realize initial matching recognition. Then, a contextual metric was calculated based on the rough matching results, and geometric and contextual metrics were combined to train the model and realize relaxed matching recognition. Relaxed matching is an optimization process implemented after initial matching to recognize more relaxed matching relationships. In relaxed matching, a convex hull is used to recognize matching relationships besides 1:1, such as 1:n, m:1 and m:n. The experimental results showed that the proposed method outperformed indicator-weighted (weighted average) and learning-based matching methods, such as traditional SVMs and decision trees (DTs). The precision scores of the proposed model were 97.1%, 95% and 97.2% for the Wuhan (China), Beijing (China) and Richmond Hill (Canada) datasets, respectively. Furthermore, the proposed model identified the matching relationships of buildings with complex geometric features and high-density spatial distributions.

Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network

Vector polygons represent crucial survey data, serving as a cornerstone of national geographic censuses and forming essential data sources for detecting geographical changes. The timely update of these polygons is vital for governmental decision making and various industrial applications. However, the manual intervention required to update existing vector polygons using up-to-date high-resolution remote sensing (RS) images poses significant challenges and incurs substantial costs. To address this, we propose a novel change detection (CD) method for land cover vector polygons leveraging high-resolution RS images and deep learning techniques. Our approach begins by employing the boundary-preserved masking Simple Linear Iterative Clustering (SLIC) algorithm to segment RS images. Subsequently, an adaptive cropping approach automatically generates an initial sample set, followed by denoising using the efficient Visual Transformer and Class-Constrained Density Peak-Based (EViTCC-DP) method, resulting in a refined training set. Finally, an enhanced attention-based multi-scale ConvTransformer network (AMCT-Net) conducts fine-grained scene classification, integrating change rules and post-processing methods to identify changed vector polygons. Notably, our method stands out by employing an unsupervised approach to denoise the sample set, effectively transforming noisy samples into representative ones without requiring manual labeling, thus ensuring high automation. Experimental results on real datasets demonstrate significant improvements in model accuracy, with accuracy and recall rates reaching 92.08% and 91.34%, respectively, for the Nantong dataset, and 93.51% and 92.92%, respectively, for the Guantan dataset. Moreover, our approach shows great potential in updating existing vector data while effectively mitigating the high costs associated with acquiring training samples.

Semantic Segmentation for Change Detection in Satellite Imaging

Change detection is a common and actual problem in the field of remote sensing. The classical approaches using raw pixel information are very sensitive to noise. In this study we propose the usage of additional semantic information for change detection. We use the semantic segmentation methods like geospatial Segment Anything Model and encoder based U-Net to evaluate the predictions and tracing the semantic information as well as raw information in change detection. Later the multidimensional time series data is used via the Vector Autoregression model to predict the future changes in the landscape. The observations which fall out of the prediction interval are considered as the changes in the landscape. The proposed method is evaluated on the dataset of the random locations across the Baltic region. The research is accompanied by the data and reproducible code at Github repository.

Quantitative MRI imaging of parenchyma and venation networks in Brassica napus leaves: effects of development and dehydration

BackgroundCharacterisation of the structure and water status of leaf tissues is essential to the understanding of leaf hydraulic functioning under optimal and stressed conditions. Magnetic Resonance Imaging is unique in its capacity to access this information in a spatially resolved, non-invasive and non-destructive way. The purpose of this study was to develop an original approach based on transverse relaxation mapping by Magnetic Resonance Imaging for the detection of changes in water status and distribution at cell and tissue levels in Brassica napus leaves during blade development and dehydration.ResultsBy combining transverse relaxation maps with a classification scheme, we were able to distinguish specific zones of areoles and veins. The tissue heterogeneity observed in young leaves still occurred in mature and senescent leaves, but with different distributions of T2 values in accordance with the basipetal progression of leaf blade development, revealing changes in tissue structure. When subjected to severe water stress, all blade zones showed similar behaviours.ConclusionThis study demonstrates the great potential of Magnetic Resonance Imaging in assessing information on the structure and water status of leaves. The feasibility of in planta leaf measurements was demonstrated, opening up many opportunities for the investigation of leaf structure and hydraulic functioning during development and/or in response to abiotic stresses.

Disrupting human resource management with people analytics: a study of applications, value, enablers and barriers in Italy

PurposeThis research aims to explore whether or how organizations adopt people analytics (PA), its value and potential socio-technical factors that can enable or hinder PA projects by disrupting and reshaping human resource management. We do this by focusing on the Italian context.Design/methodology/approachWe conduct a scoping review of data collected between 2018 and 2022 via Google Alerts (GA), a content change detection and notification service that is gaining popularity in scholarly research.FindingsOur findings suggest that the diffusion of PA applications in Italy, especially those of a descriptive nature, is growing. Most of the existing PA applications are positioned in a positive technocratic light, envisioning the value of PA for both employees and organizations. The value for the latter appears to be direct, while the value for employees is realized through organizational initiatives. The findings also suggest that while enablers can vary between PA application types, the barriers, especially technological and environmental, are generic for both descriptive and predictive/prescriptive PA applications.Originality/valueTheoretically, we propose a framework for analyzing PA applications, their values, enablers and barriers. Methodologically, we present and describe in detail a novel approach, drawing on GA that can be used to study PA in specific contexts. Practically, our study serves as a helpful point of reference for managers planning or implementing PA in Italy, for benchmarking PA in Italy over time and for comparative international studies.

MANGLEE: A Tool for Mapping and Monitoring MANgrove Ecosystem on Google Earth Engine—A Case Study in Ecuador

Mangroves, integral to ecological balance and socioeconomic well-being, are facing a concerning decline worldwide. Remote sensing is essential for monitoring their evolution, yet its effectiveness is hindered in developing countries by economic and technical constraints. In addressing this issue, this paper introduces MANGLEE (Mangrove Mapping and Monitoring Tool in Google Earth Engine), an accessible, adaptable, and multipurpose tool designed to address the challenges associated with sustainable mangrove management. Leveraging remote sensing data, machine learning techniques (Random Forest), and change detection methods, MANGLEE consists of three independent modules. The first module acquires, processes, and calculates indices of optical and Synthetic Aperture Radar (SAR) data, enhancing tracking capabilities in the presence of atmospheric interferences. The second module employs Random Forest to classify mangrove and non-mangrove areas, providing accurate binary maps. The third module identifies changes between two-time mangrove maps, categorizing alterations as losses or gains. To validate MANGLEE’s effectiveness, we conducted a case study in the mangroves of Guayas, Ecuador, a region historically threatened by shrimp farming. Utilizing data from 2018 to 2022, our findings reveal a significant loss of over 2900 hectares, with 46% occurring in legally protected areas. This loss corresponds to the rapid expansion of Ecuador’s shrimp industry, confirming the tool’s efficacy in monitoring mangroves despite cloud cover challenges. MANGLEE demonstrates its potential as a valuable tool for mangrove monitoring, offering insights essential for conservation, management plans, and decision-making processes. Remarkably, it facilitates equal access and the optimal utilization of resources, contributing significantly to the preservation of coastal ecosystems.

Clinical Neurophysiological Methods Verify Improvement in the Motor Neural Transmission in Patients with Surgically Treated Idiopathic Scoliosis in Long-Term Follow-up

The evaluation of patients after the surgical correction of idiopathic scoliosis in a long-term follow-up with clinical neurophysiological methods has not been presented in detail. This study aimed to compare the results of neurophysiological studies in 45 girls with scoliosis of Lenke types 1–3 performed pre- (T0) and postoperatively, 1 week after surgery (T1) and 6 months after surgery (T2). The parameter values of the surface electromyography while attempting maximal contraction (mcsEMG) and the transcranial motor-evoked potentials (MEPs) recorded in the anterior tibial muscles, as well as the electroneurography (ENG) of the peripheral transmission in the peroneal nerve motor fibers, were compared. The results indicate that efferent neural conduction function both centrally and peripherally, and TA muscle function slightly improved immediately after the surgical correction of scoliosis, and further normalization appeared after six months in the long-term follow-up (at p = 0.03). The sEMG recordings indicate that half a year after surgical treatment in IS patients, the TA muscle motor unit recruitment function, as well as the muscle strength evaluated with Lovett’s scale, was comparable to the normal condition. The ENG recording results indicated a gradual reduction in the motor fiber injury symptoms, mainly of the axonal type, in the peroneal nerves. The surgeries also improved the lumbar ventral roots’ neural transmission to a normal functional status. The MEP amplitude parameter values recorded after the surgical scoliosis corrections in T1 indicated a slight improvement in the efferent transmission of neural impulses within the fibers of the spinal tracts; in the long-term T2 observation period, they reached values comparable to those recorded in healthy volunteers, bilaterally. Preoperatively (T0), the results of all the neurophysiological study parameters in the IS patients were asymmetrical at p = 0.036–0.05 and recorded as worse on the concave side, suggesting the lateralization of neurological motor deficits. One week postoperatively (T1), this asymmetry was recorded as gradually reduced, showing almost no difference between the right and left sides six months later (T2). The presented algorithm for the neurophysiological assessments performed in the pre-, intra-, and long-term postoperative periods using the mcsEMG, MEP, and ENG neurophysiological examinations, together with the clinical studies, may help in the comprehensive functional evaluation of the spinal cord tracts and ventral root neural conduction, which allows the detection of the subclinical neurological changes related to scoliosis itself and the consequences of the corrective surgery. Such an evaluation can also be significant in making final decisions regarding IS surgeries and their personalization after attempting conservative treatments with bracing and kinesiotherapy. Neurophysiological studies, as a sensitive biomarker, allowed us to predict and ascertain the final result of IS treatment in the long-term follow-up, which showed the health status of patients as being comparable to that of healthy volunteers.

A Land Cover Change Detection Approach to Assess the Effectiveness of Conservation Projects: A Study Case on the EU-Funded LIFE Projects in São Miguel Island, Azores (2002–2021)

A Land Cover Change Detection Approach to Assess the Effectiveness of Conservation Projects: A Study Case on the EU-Funded LIFE Projects in São Miguel Island, Azores (2002–2021)

A multi-source change detection algorithm supporting user customization and near real-time deforestation detections

The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current systems. First, alerts often require multiple observations to confirm deforestation, which does not help enforcement if deforestation events are short-lived. Second, the methods can be computationally intensive and often focus on presenting binary detection alerts. Third, results are often reported as static accuracies or temporal lags, with relatively little discussion about how either could be improved for different use-cases. We therefore propose a novel, multisource NRT monitoring algorithm that addresses these issues by quickly achieving high-accuracy detections, using simple data harmonization to combine data sources while yielding probabilities of deforestation, and can be customized to meet management goals where users can explicitly change the algorithm to prioritize latency or accuracy in their detections based on their chosen data inputs. To demonstrate the algorithm's potential, we used combined Landsat-8, Sentinel-2, and Sentinel-1 SAR data in northern Myanmar to monitor areas with contrasting forest types. When testing all possible parameterizations, we achieved comparable results to previous studies, with median false positive rates and false negative rates under 10% and 25%, respectively, and median F1 scores consistently above 75% across the sensor combinations. Median post-data acquisition detection times were faster than other studies and ranged from 2 to 8 days, though the majority were between 2 and 5 days. In attempting to optimize the algorithm for our demonstrated region, we discovered there was no optimal parameterization that simultaneously provided the best accuracy and detection. In addition, we found that including Sentinel-1 data did not improve latency or accuracy, most likely due to relatively high levels of noise in our monsoonal study system that experienced high seasonal precipitation variation. We finally chose an example parameterization to create daily landscape maps of deforestation at 10 m resolution in two regions within the study area (∼60,000 ha). We assert this new, customizable method with the quantification of accuracy and latency trade-offs represents a clear advance for NRT change detection and can improve current operational systems.

Application of national early warning score in assessing postoperative illness severity in elderly patients with gastrointestinal illnesses.

Population aging is a social problem that is being faced in most countries. To apply the National Early Warning Score (NEWS) for an early warning on the vital signs and consciousness of elderly patients who are hospitalized in the gastrointestinal surgical department and to provide a reference for early detection of changes in illness severity in elderly patients by studying the correlation between NEWS value and changes in illness severity. We enrolled 528 elderly patients who were hospitalized in the gastrointestinal surgical department of a tertiary grade A hospital in Guizhou Province between June 2020 and May 2021, to analyze how NEWS max value correlates with illness severity and obtain the optimal NEWS cutoff value for both potentially critically ill and critically ill elderly patients using the receiver operating characteristic (ROC) curve. There were statistically significant differences in NEWS values between elderly patients with various illness severities (P< 0.05). NEWS values correlated positively with illness severity (r= 0.605, P< 0.001). Based on the ROC curve, early warning trigger values for NEWS to identify potentially critically ill, critically ill and terminally ill elderly patients were 6, 7 and 8, respectively. The area under the curve (AUC) for potentially critically ill, critically ill and terminally ill elderly patients was 0.907, 0.921 and 0.939, respectively. NEWS performed better in detecting patient illness severity than Modified Early Warning Score (MEWS) in AUC, sensitivity, specificity, and Youden's index, with statistically significant differences (P< 0.05). An early warning on the vital signs and consciousness of hospitalized elderly patients using NEWS can facilitate advanced detection of changes in illness severity of elderly patients by medical staff and enable timely treatment, thus significantly lowering the risks of illness deterioration.