Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

Machine learning in geosciences and remote sensing

Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficultto-program applications, and software applications. It is a collection of a variety of algorithms（e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.） that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore,nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.

Machine learning empowered COVID-19 patient monitoring using non-contact sensing:An extensive review

The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which caused the coronavirus disease 2019(COVID-19)pandemic,has affected more than 400 million people worldwide.With the recent rise of new Delta and Omicron variants,the efficacy of the vaccines has become an important question.The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions,particularly for healthcare workers.In this paper,we discuss the current literature on invasive/contact and non-invasive/noncontact technologies(including Wi-Fi,radar,and software-defined radio)that have been effectively used to detect,diagnose,and monitor human activities and COVID-19 related symptoms,such as irregular respiration.In addition,we focused on cutting-edge machine learning algorithms(such as generative adversarial networks,random forest,multilayer perceptron,support vector machine,extremely randomized trees,and k-nearest neighbors)and their essential role in intelligent healthcare systems.Furthermore,this study highlights the limitations related to non-invasive techniques and prospective research directions.

Non-intrusive soil carbon content quantification methods using machine learning algorithms:A comparison of microwave and millimeter wave radar sensors

Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water cycling.However,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients.To address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine learning.These methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or aerial.The paper provides details on the materials,methods,machine learning workflow,and results of our investigations.The significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content accounting.We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field.The results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.

Hazelnut mapping detection system using optical and radar remote sensing:Benchmarking machine learning algorithms

Mapping hazelnut orchards can facilitate land planning and utilization policies,supporting the development of cooperative precision farming systems.The present work faces the detection of hazelnut crops using optical and radar remote sensing data.A comparative study of Machine Learning techniques is presented.The system proposed utilizes multi-temporal data from the Sentinel-1 and Sentinel-2 datasets extracted over several years and processed with cloud tools.We provide a dataset of 62,982 labeled samples,with 16,561 samples belonging to the‘hazelnut’class and 46,421 samples belonging to the‘other’class,collected in 8 heterogeneous geograph-ical areas of the Viterbo province.Two different comparative tests are conducted:firstly,we use a Nested 5-Fold Cross-Validation methodology to train,optimize,and compare different Machine Learning algorithms on a single area.In a second experiment,the algorithms were trained on one area and tested on the remaining seven geo-graphical areas.The developed study demonstrates how AI analysis applied to Sentinel-1 and Sentinel-2 data is a valid technology for hazelnut mapping.From the results,it emerges that Random Forest is the classifier with the highest generalizability,achieving the best performance in the second test with an accuracy of 96%and an F1 score of 91%for the‘hazelnut’class.

Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves

Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality.

Accurate Location Estimation of Smart Dusts Using Machine Learning

Traditional wireless sensor networks(WSNs)are not suitable for rough terrains that are difficult or impossible to access by humans.Smart dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough terrains.The tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraftwithout manually setting their locations.Although it is clear that a number of remote sensing applications can benefit from this technology,but the small size of smart dust fundamentally restricts the integration of advanced hardware on tiny sensors.This raises many challenges including how to estimate the location of events sensed by the smart dusts.Existing solutions on estimating the location of events sensed by the smart dusts are not suitable for monitoring rough terrains as these solutions depend on relay sensors and laser patterns which have their own limitations in terms of power constraint and uneven surfaces.The study proposes a novel machine learning based localization algorithm for estimating the location of events.The approach utilizes timestamps(time of arrival)of sensed events received at base stations by assembling them into a multidimensional vector and input to a machine learning classifier for estimating the location.Due to the unavailability of real smart dusts,we built a simulator for analysing the accuracy of the proposed approach formonitoring forest fire.The experiments on the simulator show reasonable accuracy of the approach.

Deriving big geochemical data from high-resolution remote sensing data via machine learning:Application to a tailing storage facility in the Witwatersrand goldfields

Remote sensing data is a cheap form of surficial geoscientific data,and in terms of veracity,velocity and volume,can sometimes be considered big data.Its spatial and spectral resolution continues to improve over time,and some modern satellites,such as the Copernicus Programme’s Sentinel-2 remote sensing satellites,offer a spatial resolution of 10 m across many of their spectral bands.The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data.The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data,which can be used for numerous downstream activities,particularly where data timeliness,volume and velocity are important.Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry,which currently entirely relies on manually derived data that is primarily guided by scientific reduction.Furthermore,it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis.Currently,no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences.In this paper,we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation.We use gold grade data from a South African tailing storage facility(TSF)and data from both the Landsat-8 and Sentinel remote sensing satellites.We show that various machine learning algorithms can be used given the abundance of training data.Consequently,we are able to produce a high resolution(10 m grid size)gold concentration map of the TSF,which demonstrates the potential of our method to be used to guide extraction planning,online resource exploration,environmental monitoring and resource estimation.

Integrating geographical information systems,remote sensing,and machine learning techniques to monitor urban expansion:an application to Luanda,Angola

According to many previous studies,application of remote sensing for the complex and heterogeneous urban environments in Sub-Saharan African countries is challenging due to the spectral confusion among features caused by diversity of construction materials.Resorting to classification based on spectral indices that are expected to better highlight features of interest and to be prone to unsupervised classification,this study aims(1)to evaluate the effectiveness of index-based classification for Land Use Land Cover(LULC)using an unsupervised machine learning algorithm Product Quantized K-means(PQk-means);and(2)to monitor the urban expansion of Luanda,the capital city of Angola in a Logistic Regression Model(LRM).Comparison with state-of-the-art algorithms shows that unsupervised classification by means of spectral indices is effective for the study area and can be used for further studies.The built-up area of Luanda has increased from 94.5 km2 in 2000 to 198.3 km2 in 2008 and to 468.4 km2 in 2018,mainly driven by the proximity to the already established residential areas and to the main roads as confirmed by the logistic regression analysis.The generated probability maps show high probability of urban growth in the areas where government had defined housing programs.

Power of SAR Imagery and Machine Learning in Monitoring Ulva prolifera:A Case Study of Sentinel-1 and Random Forest

Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Aperture Radar(SAR)imagery with the machine learning,and detect the U.prolifera of the South Yellow Sea of China(SYS)in 2021.The findings indicate that the Random Forest model can accurately and robustly detect U.prolifera,even in the presence of complex ocean backgrounds and speckle noise.Visual inspection confirmed that the method successfully identified the majority of pixels containing U.prolifera without misidentifying noise pixels or seawater pixels as U.prolifera.Additionally,the method demonstrated consistent performance across different im-ages,with an average Area Under Curve(AUC)of 0.930(+0.028).The analysis yielded an overall accuracy of over 96%,with an average Kappa coefficient of 0.941(+0.038).Compared to the traditional thresholding method,Random Forest model has a lower estimation error of 14.81%.Practical application indicates that this method can be used in the detection of unprecedented U.prolifera in 2021 to derive continuous spatiotemporal changes.This study provides a potential new method to detect U.prolifera and enhances our under-standing of macroalgal outbreaks in the marine environment.

A vector hybrid triboelectric sensor(HTS)for motion identification via machine learning

Rapidly responding and cost-effective sensors played a crucial role in industrial detection.However,the lack of versatile strategies for identifying and classifying operating states on various practical behaviors has limited the rapid development of monitoring technology.This study developed a vector hybrid triboelectric sensor(HTS)with surface nanocrystalline containing triboelectric vibration and rotation units(triboelectric vibration unit(TVU),triboelectric rotation unit(TRU))capable of detecting the vibrational and rotary states of the device.The synchronous detection of two sensing signals can be achieved due to the hierarchical structure as the basic unit of the HTS,which contributed to reducing the volume and spatial distribution of the HTS.Based on the voltage/current/charge(U-I-Q)signal amplitudes and phase features generated by the TVU,the vibration frequency and orientation of the device can be identified by using a double-layer neural network(D-LNN),in which the accuracy reaches 96.5%and 95.5%respectively.Additionally,by combining logistic regression,D-LNN,and linear regression,the accuracy of the TRU for rotary classification exceeds 93.5%in practical application.In this study,the great potential application of the HTS combined with the machine learning methods was successfully explored and exhibited and it might speed up the development of industrial detection in the near future.

Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning

Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau.In this study,combined with satellite images and bathymetric data,we comprehensively evaluate the accuracy of a multi-factor combined linear regression model(MLR)and machine learning models,create a depth distribution map and compare it with the spatial interpolation,and estimate the change of water level and water storage based on the inverted depth.The results indicated that the precision of the random forest(RF)was the highest with a coefficient of determination(R2)value(0.9311)and mean absolute error(MAE)values(1.13 m)in the test dataset and had high reliability in the overall depth distribution.The water level increased by 9.36 m at a rate of 0.47 m/y,and the water storage increased by 1.811 km3 from 1998 to 2018 based on inversion depth.The water level change was consistent with that of the Shuttle Radar Topography Mission(SRTM)method.Our work shows that this method may be employed to study the water depth distribution and its changes by combining with bathymetric data and satellite imagery in shallow lakes.

Wi-Wheat+:Contact-free wheat moisture sensing with commodity WiFi based on entropy

In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time monitoring.The proposed system is based on Commodity WiFi and is easy to deploy.Leveraging WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel entropy.The feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.

Estimation of 30 m land surface temperatures over the entire Tibetan Plateau based on Landsat-7 ETM+data and machine learning methods

Land surface temperature(LST)is an important parameter in land surface processes.Improving the accuracy of LST retrieval over the entire Tibetan Plateau(TP)using satellite images with high spatial resolution is an important and essential issue for studies of climate change on the TP.In this study,a random forest regression(RFR)model based on different land cover types and an improved generalized single-channel(SC)algorithm based on linear regression(LR)were proposed.Plateau-scale LST products with a 30 m spatial resolution from 2006 to 2017 were derived by 109,978 Landsat 7 Enhanced Thematic Mapper Plus images and the application of the Google Earth Engine.Validation between LST results obtained from different algorithms and in situ measurements from Tibetan observation and research platform showed that the root mean square errors of the LST results retrieved by the RFR and LR models were 1.890 and 2.767 K,respectively,which were smaller than that of the MODIS product(3.625 K)and the original SC method(5.836 K).

Integration of maximum crop response with machine learning regression model to timely estimate crop yield

Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan’s Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R^(2)), Root Mean Square Error (RMSE, t/ ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat;0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6- 8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress.

Feature importance:Opening a soil-transmitted helminth machine learning model via SHAP

In the field of landscape epidemiology,the contribution of machine learning(ML)to modeling of epidemiological risk scenarios presents itself as a good alternative.This study aims to break with the”black box”paradigm that underlies the application of automatic learning techniques by using SHAP to determine the contribution of each variable in ML models applied to geospatial health,using the prevalence of hookworms,intestinal parasites,in Ethiopia,where they are widely distributed;the country bears the third-highest burden of hookworm in Sub-Saharan Africa.XGBoost software was used,a very popular ML model,to fit and analyze the data.The Python SHAP library was used to understand the importance in the trained model,of the variables for predictions.The description of the contribution of these variables on a particular prediction was obtained,using different types of plot methods.The results show that the ML models are superior to the classical statistical models;not only demonstrating similar results but also explaining,by using the SHAP package,the influence and interactions between the variables in the generated models.This analysis provides information to help understand the epidemiological problem presented and provides a tool for similar studies.

Lithium-bearing Pegmatite Exploration in Western Altun,Xinjiang,using Remote-Sensing Technology

Western Altun in Xinjiang is an important area,where lithium(Li)-bearing pegmatites have been found in recent years.However,the complex terrain and harsh environment of western Altun exacerbates in prospecting for Li-bearing pegmatites.Therefore,remote-sensing techniques can be an effective means for prospecting Li-bearing pegmatites.In this study,the fault information and lithologyical information in the region were obtained using the median-resolution remotesensing image Landsat-8,the radar image Sentinel-1 and hyperspectral data GF-5.Using Landsat-8 data,the hydroxyl alteration information closely related to pegmatite in the region was extracted by principal component analysis,pseudoanomaly processing and other methods.The high spatial resolution remote-sensing data WorldView-2 and WorldView-3 short-wave infrared images were used and analyzed by principal component analysis(PCA),the band ratio method and multi-class machine learning(ML),combined with conventional thresholds specified the algorithms used to automatically extract Li-bearing pegmatite information.Finally,the Li-bearing pegmatite exploration area was determined,based on a comprehensive analysis of the faults,hydroxyl alteration lithology and Li-bearing pegmatite information.Field investigations have verified that the distribution of pegmatites in the central part of the study area is consistent with that of Li-bearing pegmatites extracted in this study.This study provides a new technique for prospecting Li-bearing pegmatites,which shows that remote-sensing technology possesses great potential for identifying lithium-bearing pegmatites,especially in areas that are not readily accessible.