AI-Driven Ground Points Extraction for Rugged Terrains in Coastal Landscape – A Case Study

In order to implement the spatial Inverse Distance Weighting (IDW) interpolation in parallel, we use the quadtree approach to decompose the spatial domain and execute the IDW interpolation on these quads through the parametric tasks on Windows HPC platform. The whole implementation includes the executables of QuadDC, IDW, output gathering and data visualization which should be run serially. Compared with the traditional method to integrate each executable manually, the Job Manager of Windows HPC utilizes multi-task job to serialize all the executables as a workflow. The experiments test two cases of IDW0 and IDW6 with different domain clustering regions, and show that our implementation improves the functionality integration and efficiency of the spatial IDW interpolation.

A novel mesh deformation technique is developed based on the Delaunay graph mapping method and the inverse distance weighting (IDW) interpolation. The algorithm maintains the advantages of the efficiency of Delaunay graph mapping mesh deformation while it also possesses the ability of better controlling the near surface mesh quality. The Delaunay graph is used to divide the mesh domain into a number of sub-domains. On each sub-domain, the inverse distance weighting interpolation is applied, resulting in a similar efficiency as compared to the fast Delaunay graph mapping method. The paper will show how the near-wall mesh quality is controlled and improved by the new method

This article explores the application of the Inverse Distance Weighting (IDW) interpolation method in determining normal heights, utilizing a network of points with measured ellipsoidal and normal heights by the use of GNSS/levelling method. The IDW method, which assumes that points closer to each other are more alike, is employed to locate points within the correct quadrant and calculate their normal height based on the determined average height anomaly across the entire area. The accuracy of this method is validated using check points. The article further discusses the methodology of normal height determination at a country level by using generated coefficients, representing the average difference between various height-systems, which could be used as an integral to the interpolation methods used for generating a refined Digital Elevation Model (DEM) across the country. This study contributes to the ongoing discourse on the effective use of interpolation methods in geospatial analysis and DEM generation. It provides valuable insights into the practical application of the IDW method and its potential for enhancing the accuracy of normal height determination and DEM refinement. Importantly, such methods hold significant value for countries lacking gravimetric data, as they provide a viable means of determining normal heights in the absence of a local quasi geoid model.

The social and economic development from the coastal area benefits human life. However, growing human and environmental pressures at coastal areas also bring significant impacts on coastal erosion and coastline changes. Nowadays, geospatial technology which utilized remote sensing and GIS techniques has been widely used to detect coastal erosion for controlling the development and coastal region sustainability. Thus, this study was conducted to assess the coastal erosion and accretion for the potential risk zone based on the characteristics of the landscape and land use land cover (LULC) at the Kelantan Coast using land use classification and GIS spatial interpolation techniques. In this study, the vulnerability level of the coastal profile at the Kelantan coast was determined from the beach profile survey and the Inverse Distance Weighting (IDW) interpolation method. Then, the Land Use Land Cover (LULC) along a 1 km buffer zone were classified by performing a supervised classification method on Sentinel 2 satellite images year 2020. The effect of these parameters on coastal erosion was determined from separated five management units (MU 1, MU 2, MU 3, MU 4, MU 5) along the Kelantan Coast based on the Malaysian Department of Irrigation and Drainage (DID). It is found that MU 3 (0.8%) and MU 5 (0.6%) were the area with the lowest average slope profile percentages which is located at Pantai Sabak and Pantai Kemayang respectively. Thus, the infrastructure, LULC and coastal communities in Pantai Sabak and Pantai Kemayang were potentially vulnerable to erosion. This finding supports the significant use of geospatial techniques of important decision-making to protect and mitigate steps toward sustainable coastal management along the coastline.

Decision makers need cost-effective methods for accurately depicting land surface characteristics as basic tools for locating construction project. ASTER Global Digital Elevation Model V002 with 30 m resolution was used to extract information on terrain surface and drainage network at the micro-catchment level. ASTER data was compared with 39 reference points from integrated Global Positioning System (GPS) and topographic maps. However, there was a gap between ASTER dataset and reference points; thus, a readjustment process of ASTER dataset was required. The results indicate that Global Positioning System (GPS) and topographic maps data sets have good connection with a range of difference around ± 32.7 m. Pearson correlation coefficient of ASTER Pixel values in the connection with GPS and topographic maps data sets indicates a strong positive correlation 0.8, 0.827 respectively. Therefore, a Multiple Linear Regression (MLR) model was used to readjust ASTER data based on topographic maps, and ground points. The ‘best’ fit of MLR model for ASTER was chosen and used to interpolate a multiscale temporal and spatial distribution. The research applied two interpolation techniques: the Inverse Distance Weighting (IDW) and Kriging to better understand the spatial distribution. The results show that there is a slight difference between ASTER data and the other two types of elevation models.

Though unmanned aircraft systems (UAS) are widely used in agriculture, their current positioning accuracy in a radius of 0.5 to 2 meters is still too low to pinpoint a crop row or to precisely overlay temporal multi-source field maps together without a valid geometric calibration. The positioning accuracy of UAS deployed with real time kinematic (RTK) global navigation satellite system (GNSS) can be largely increased to a centimeter level, which was claimed from the manufacturers. This paper includes the preliminary test results of positioning accuracy of a commercial RTK UAS over a set of fixed position panels in our customized scenarios. Images were collected in three GNSS modes (regular GNSS without RTK, RTK mode 1 - not corrected by the positioning error of the base station, and RTK mode 2 - corrected by the positioning error of the base station) in static and in-flight settings. In the static setting, horizontal accuracies were 2.17 cm for the RTK mode 2, 12.11 cm for the RTK mode 1, and 11.46 cm for the regular GNSS mode. The significant result of horizontal accuracy in the in-flight setting was that RTK mode 2 without GCPs (2.82 cm) showed comparable accuracy with the commonly used regular GNSS mode with GCPs (1.34 cm). The vertical positioning accuracy in the static setting were 6.01 cm for the RTK mode 2, 5.65 cm for the RTK mode 1, and 10.48 cm for the regular GNSS mode. The accuracy of height measurement from digital surface models (DSMs) without and with GCPs in RTK mode 2 were 4.81 cm and 3.72 cm, respectively, which were the best performance among the three modes. In summary, the RTK UAS tested in this study showed great potential in eliminating the requirement of using GCPs and in high-positioning-accuracy application. The next phase is to test the system in field for accurate crop height measurement at different growth stages in agricultural application.

Excessive streambank erosion is a significant source of fine sediments and associated nutrients in many river systems as well as poses risk to infrastructure. Geomorphic change detection using high-resolution topographic data is a useful method for monitoring the extent of bank erosion along river corridors. Recent advances in an unmanned aircraft system (UAS) and structure from motion (SfM) photogrammetry techniques allow acquisition of high-resolution topographic data, which are the methods used in this study. To evaluate the effectiveness of UAS-based photogrammetry for monitoring bank erosion, a fixed-wing UAS was deployed to survey 20 km of river corridors in central Vermont, in the northeastern United States multiple times over a two-year period. Digital elevation models (DEMs) and DEMs of difference allowed quantification of volumetric changes along selected portions of the survey area where notable erosion occurred. Results showed that UAS was capable of collecting high-quality topographic data at fine resolutions even along vegetated river corridors provided that the surveys were conducted in early spring, after snowmelt but prior to summer vegetation growth. Longer term estimates of streambank movements using the UAS showed good comparison to previously collected airborne lidar surveys and allowed reliable quantification of significant geomorphic changes along rivers.

Particulate matter is one of the life threathening pollutants that are harmful to human health. The aim of this study is to assess PM2.5 distribution using spatial interpolation techniques of inverse distance weighted (IDW) by predicting their concentrations at distinct unmonitored locations. The IDW interpolation was developed using Arcmap for Klang Valley area covering six districts in central Peninsular Malaysia. The Department of Environment (DOE) continous air quality monitoring stations (CAQMS) were installed at 6 districts in Klang Valley throughout period of 9 months between January and September 2022 for data collection. The results from IDW showed that PM2.5 concentrations were highest in February and lowest in March. The IDW spatial mapping demonstrated that PM2.5 distributed higher in Seremban and Petaling districts throughout 9 months while Kuala Lumpur and Putrajaya demonstrated contrary lower. The IDW cross-validation results showed an acceptable predictive accuracy with low RMSE values ranging from 1.790 to 5.073 and high R squared value with range from 0.0267 to 0.5081. The results showed a very good fit of the IDW model to the observed points, confirming that the results of these analyses can monitor and predict PM2.5 concentrations with high accuracy. The interpolation maps that result can help identify key regions that require air quality management mitigation strategies.

The combination of extreme weather conditions and economic activities has increased the number of corrosive agents in the air. This phenomenon will lead to rust formation in various assets, such as vehicles and steel properties. A Corrosion Hazard Map of Peninsular Malaysia was produced by considering parameters from climate and wet deposition information that influenced atmospheric corrosion: wetness, rainfall, chloride, nitrate, and sulphate. The inverse distance weighting interpolation technique was applied to generate the map. The study discovered that the corrosion rate at the southern and central part of Peninsular Malaysia is at level five, while the highland area is at level one. The Corrosion Hazard Map produced from this study would become as additional information that possibly will benefit various sectors such as metal industries, construction, land development, automotive coating technology, and others.

Incorporation of principal component analysis, geostatistical interpolation approaches and frequency-space-based models for portraying the Cu-Au geochemical prospects in the Feizabad district, NW Iran

<p>The assessment of nutrient potential is crucial for enhancing tea production and achieving sustainable agricultural goals. Thus, the present study was carried out to assess and map the soil nutrient potential for tea plants through laboratory and geostatistical methods. A total of 74 random soil samples with 3 replications were collected from 0 to 9 cm (topsoil) and 9 to 18 cm (subsoil) depths from Oodaleah Tea Garden (OTG) which belongs to Fatikchhari Upazila of Chattogram District in Bangladesh. All the laboratory analyses were done following standard procedures and maps of individual soil indices were prepared through the Inverse Distance Weighted (IDW) interpolation technique using ArcGIS 10.7 software. The integration of maps for nutrient potential was performed following the Analytic Hierarchy Process (AHP). The mean concentrations of organic matter (OM), total nitrogen (TN), available phosphorus (AvP), available potassium (AvK), and available calcium (AvCa) were found higher at 0 to 9 cm compared to 9 to 18 cm soil depth with significant (<em>p</em> < 0.05) difference among the sampling sites. The maps of individual soil parameters revealed distinct spatial differences at 0 to 9 cm and 9 to 18 cm soil depths. The total study area covered 5.99 km<sup>2</sup>. Integrated nutrient potential mapping showed that moderately suitable (44.15%) accounted for the largest proportion, followed by highly suitable (26.03%), generally suitable (21.67%), marginally suitable (7.06%), and unsuitable area (1.08%) of the tea plantation suitability evaluation. The integrated map of nutrient potential will aid in better management of the tea soils.</p>

Six criteria pollutants were monitored in varied zones of Coimbatore City during a period of one year and correlated with the natural meteorological conditions such as temperature, relative humidity (RH), wind speed and precipitation using SPSS 23. The Spatial distribution analysis was determined by Inverse Distance Weighted (IDW) Interpolation Technique using ArcGIS 10.5. The interpolated values of the particulate matter (PM) i.e., PM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</inf> (102-117 $\mu g/m^{3}$) and PM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</inf> (66-75 $\mu g/m^{3}$) showed a higher concentration in comparison with the permissible levels in 3 locations and Carbon monoxide (CO) in all the 5 locations. The Air Quality Index (AQI) in the different zones ranged between 77.04 to 151.01 indicating unhealthy condition. A significant impact of meteorological parameters in the air pollution distribution, showing positive correlation between the concentration of air pollutants and temperature (0.6- 0.8), wind speed and turbulence (0.1- 0.4) and a negative correlation with precipitation (-0.1 - -0.4) was found.

The degradation of air quality is the most concerned issue of our society due to its harmful impacts on human health, especially in cities with rapid urbanization and population growth like Hanoi, the capital of Vietnam. This study aims at developing a new approach that combines data-driven models and interpolation technique to develop the PM10 concentration maps from meteorological factors for the central area of Hanoi. Data-driven models that relate the PM10 concentration with the meteorological factors at the air quality monitoring stations in the study area were developed using the Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) algorithms. Models’ performance comparison showed that ANN models yielded better goodness-of-fit indices than MLR models at all stations in the study area with average coefficient of correlation (r) and Nash–Sutcliffe Efficiency Index (NSE) of 0.51 and 0.34 for the former, and 0.7 and 0.49 for the latter. These indices indicates that the ANN-based data-driven models outperformed the MLR-based models. Thus, the ANN-based models and the Inverse Distance Weighting (IDW) interpolation technique were then combined for mapping the monthly PM10 concentration with a spatial resolution of 1 km from global meteorological data. With this combination, the PM10 concentration maps account for both local PM10 concentration and impacts of spatio-temporal variations of meteorological factors on the PM10 concentration. This study provides a promising method to predict the PM concentration with a high spatio-temporal resolution from meteorological data.

Objectives: To determine the Soil Organic Carbon Density (SOCD) and to observe distribution of Soil Organic Carbon (SOC) in the Bhima River sub-watershed of Solapur. Methods/Analysis: The spatial distribution of Soil Organic Carbon and it density was shown by Inverse Distance Weighted (IDW) interpolation technique using ArcGIS and the subsequent map generated in the process account for the fertility across the watershed and its longitudinal section. Total 36 sampling points were used for soil sampling and are processed for the output map. The land cover type of barren land and agriculture field were selected for soil sampling. Walkley and Black method was used for analysis of soil organic carbon and organic matter was calculated as a derived parameter. Findings: It is found that average soil bulk density was 1.15 gm cm-3 and the difference in SOCD from the minimum to maximum was 2.85 kg/m2 with average of 1.87 kg/m2. The lower values carbon was at high elevation and higher values were recorded nearer to mouth of river. There seems to be insignificant correlation between bulk density and SOC. Applications/Improvements: The SOC comparison between different land cover, clearly shows that agricultural terrains were richer as compared to barren lands. The outcome of the research could be of importance for the policy makers and management of agriculture, as SOC is the major nutrient in terrestrial ecosystems.

Excessive exposure schoolchildren to air pollution can lead to long-lasting health problems, allergies and respiratory disease. It is well known that the major factors contributing to increase of air pollution are motor vehicles and industries. Thus, it is important to analyze the spatial temporal air pollutant concentrations and its relation with school location as the location of schools and its surrounding can increase their exposure. In this study, six schools in Johor were selected and the land use surrounding the schools were updated using ArcGIS. The Inverse Distance Weighting (IDW) interpolation technique was used to identify which schools’ area in Johor has a higher range of air pollutant concentration. There are four air pollution parameters obtained from the Department of Environment (DOE) which are PM2.5, CO, O3 and SO2. Hourly air pollutant concentration reading was obtained from the DOE in order to analyze air pollutant concentration during school period. The results obtained from the IDW technique showed that Sekolah Menengah Pasir Gudang (2) located in Pasir Gudang, Malaysia has reached a very unhealthy and hazardous level as compared to other schools in Johor. On the other hand, Sekolah Menengah Kebangsaan Tanjung Pengelih, Pengerang, Malaysia showed good to unhealthy range as compared to other schools in Johor. The spatial autocorrelation tool was used to analyze the relationship between the air pollution concentration and the school’s location in Johor. The results showed that the Moran’s Indices is positive showing a strong relationship that is clustering. It can be stated that there is a relationship between air pollutant concentrations with the school locations.