Showing papers in "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences in 2023"
TL;DR: In this article , a fusion strategy in Fourier domain was proposed to replace the low spatial frequencies of PS with the corresponding photogrammetric frequencies in order to have correct low frequencies while maintaining high frequencies from PS.
Abstract: Abstract. Image-based 3D reconstruction has been successfully employed for micro-measurements and industrial quality control purposes. However, obtaining a highly-detailed and reliable 3D reconstruction and inspection of non-collaborative surfaces is still an open issue. Photometric stereo (PS) offers the high spatial frequencies of the surface, but the low frequency is erroneous due to the mathematical model's assumptions and simplifications on how light interacts with the object surface. Photogrammetry, on the other hand, gives precise low-frequency information but fails to utilize high frequencies. As a result, in this research, we present a fusion strategy in Fourier domain to replace the low spatial frequencies of PS with the corresponding photogrammetric frequencies in order to have correct low frequencies while maintaining high frequencies from PS. The proposed method was tested on three different objects. Different cloud-to-cloud comparisons were provided between reference data and the 3D points derived from the proposed method to evaluate high and low frequency information. The obtained 3D findings demonstrated how the proposed methodology generates a high-detail 3D reconstruction of the surface topography (below 20 µm) while maintaining low-frequency information (0.09 µm on average for three different testing objects) by fusing photogrammetric and PS depth data with the proposed FFT-based method.
2 citations
TL;DR: In this article , a multi-kernel convolution neural network (CNN) deep learning model was proposed based on elevation, wind direction, and speed, minimum and maximum temperatures, humidity, precipitation, drought index, normalized difference vegetation index (NDVI), and energy release component to predict wildfire spread across the United States.
Abstract: Abstract. In the last twenty years, destructive wildfires have affected the environment to the tune of billions of dollars. An accurate model is crucial for predicting the spreading of wildfires in a variety of conditions. In this study, a multi-kernel convolution neural network (CNN) deep learning model was proposed based on elevation, wind direction, and speed, minimum and maximum temperatures, humidity, precipitation, drought index, normalized difference vegetation index (NDVI), and energy release component to predict wildfire spread across the United States. Using multi-kernel CNN, it is possible to predict whether a pixel will be on fire at a future time. Compared to the model presented by other authors, the multi-kernel CNN model achieved high accuracy and F1 score. In comparison with CNNs without a multi-kernel mechanism and fixed kernel size, the proposed model predicted more accurate results based on the test data set. The multi-kernel CNN model reached an overall accuracy of 98.6 and F1 score of 70.97 on test data.
2 citations
TL;DR: In this paper, two rainfall runoff models based on Ground-based Rain Gauge stations (GRGs) and PERSIANN-CDR precipitation records were developed for the Chelgerd sub-basin, which is the main branch of the Zayandeh-Roud Basin in central Iran, in order to analyze how accurate the simulated runoff by PERSiann-cDR database is.
Abstract: Abstract. Easy access to valid climatic data has always played a fundamental role in progressing hydrological studies. That is why numerous satellite-based precipitation products (SPPs) have been generated in the contemporary era. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is considered one of the most popular climatic databases which started to produce daily rainfall data with 0.25° × 0.25° temporal and spatial resolutions in 1983. The aim of this research is to evaluate how well PERSIANN-CDR has performed in a rainfall-runoff modeling application over the period of 1994 to 2015. In this regard, using Soil & Water Assessment Tool (SWAT), two rainfall-runoff models based on Ground-based Rain Gauge stations (GRGs) and PERSIANN-CDR precipitation records were developed for the Chelgerd sub-basin, which is the main branch of the Zayandeh-Roud Basin in central Iran, in order to analyze how accurate the simulated runoff by PERSIANN-CDR database is. Comparing the developed SWAT model calibration results using the satellite database precipitation (NS = 0.78, P-Factor = 0.52, and R-Factor = 0.41) to calibration results of the developed model based on GRGs (NS = 0.81, P-Factor = 0.54, and R-Factor = 0.42) showed that although PERSIANN-CDR precipitation magnitudes were typically less than GRGs records, accuracy indicators of simulated runoffs to Ghale-Shahrokh were almost the same.
2 citations
TL;DR: Wang et al. as mentioned in this paper proposed a method for detecting doors that connect two scanned rooms, which requires neither trajectory nor scanning position, and could accurately detect 91% of the inner doors.
Abstract: Abstract. Indoor navigation is a critical service providing safe paths for humans in an emergency. Since doors connect different parts of a building, door detection is essential in creating a navigation map and walkable spaces. Considering the Manhattan World Assumption (MWA), this paper proposes a method for detecting doors that connect two scanned rooms. In contrast with most existing approaches, the proposed method requires neither trajectory nor scanning position. This method consists of two main steps. At first, with the help of multi-layer thresholding, a raster will be created from the point cloud that its Digital Numbers (DNs) correspond to the ceiling elevation. Then, this raster's pixels will be segmented based on their DNs, and those segments whose elevations are local minimums are chosen as door candidates. The second step extracts the part of the point cloud corresponding to each door candidate and analyses its coordinates components' histograms to decide whether there is a door or not. The proposed scheme has been tested on two different datasets and could accurately detect 91% of the inner doors. Although this method is designed to detect inner doors, it also detected 65% of marginal doors.
1 citations
TL;DR: In this paper , the authors found out the level of compliance of petrol pumps to set standards for distances and location in Lahore Metropolitan Corporation and performed site suitability analysis using Analytical Hierarchy Process technique.
Abstract: Abstract. The research demonstrated the spatial analysis of site suitability using physical parameters in the Metropolitan expanse that requires environmental safety and sustainability. The location of petrol filling stations that are already developed without following any standard criteria is drastic. This research finds out the level of compliance of petrol pumps to set standards for distances and location in Lahore Metropolitan Corporation. 195 petrol pumps were sampled and eight standard criteria were developed. Which were used to perform site suitability analysis using Analytical Hierarchy Process technique. The results revealed that 88% of petrol pumps in the study area meet the standard criteria while 12 % did not meet the criteria due to improper planning of the management. Furthermore, by incorporating all the essential factors for petrol pumps suitable sites and spatial prediction of urban sprawl using Cellular automata have been developed. Therefore, this study also indicates the LULC pattern of metropolitan area with past and present existence of LULC feature. On the basis of past and present trends the future prediction has been performed using cellular automata modelling to observed the future scenario of urban development’s patterns. The prediction of future builtup pattern results are very alarming and need serious concentration regarding future road map. The study showed that geographic information system is essential tool that can assist decision and policy makers for a new development project to take appropriate measures.
1 citations
TL;DR: In this article , the BADI spectral index has been designed to take benefit of the Sentinel-2 spectral bands and use a spectral combination of bands that are reasonable for post-fire burned regions detection.
Abstract: Abstract. Forest fires are natural events that occur in numerous ecosystems worldwide and cause significant damage to human, ecological and socio-economic factors. It is also crucial to obtain useful information on the distribution and density of burned areas on large scale. An efficient way to map large regions is through remote sensing (RS). Nevertheless, the complex scenario and similar spectral signature of features in multispectral bands can lead to many false positives, making it difficult to extract the burned areas accurately. Multispectral data from Sentinel-2 satellite images allow the development of novel burned area indices, as more spectral data is recorded in the Red-Edge region. This research aims to develop a new burned area detection index (BADI) at 20 m spatial resolution in the google earth engine platform to detect the wildfire-affected areas in southwest of Iran using Sentinel-2 satellite imagery. The BADI spectral index has been specially designed to take benefit of the Sentinel-2 spectral bands and use a spectral combination of bands that are reasonable for post-fire burned regions detection. The final results indicated that the proposed index by applying a post-processing stage works well in the case of the study area to identify the burned areas. At the same time, it can satisfactorily suppress the complicated and irrelevant changes in the scene. Furthermore, the BADI index is rapid and can provide the burned areas map in near real-time. According to the Copernicus Emergency Management Service (EMS) reference data, maps of the burned areas were produced with a kappa coefficient of 0.92 and an overall accuracy of 92.15%, which demonstrated a good result in comparison to similar spectral indices.
1 citations
TL;DR: In this paper , the authors investigated the influence of transferring pre-trained weights on the performance of a Siamese CD network using a benchmark dataset and showed that transferring the encoder weights from the autoencoder results in a relatively lower performance but offers a considerable amount of temporal efficiency in the training phase.
Abstract: Abstract. Change Detection (CD) is one of the most crucial applications in remote sensing which identifies meaningful changes from bitemporal images taken from the same location. Enhancing the temporal efficiency and accuracy of this task is of great importance and one way to achieve this is through transfer learning. In this study, we investigate the influence of transferring pre-trained weights on the performance of a Siamese CD network using a benchmark dataset. For this purpose, an autoencoder with the same encoder architecture as in the Siamese model is trained on the whole dataset. Then, the encoder weights are transferred from the autoencoder and the Siamese model is trained in two modes. In the first mode, the transferred weights are frozen and only the decoder section of the Siamese models is trained while the second mode trains the whole model without freezing any part of the model. Moreover, the Siamese model is also trained without using the pre-trained weights to set the basis for comparisons. The results indicate that freezing the encoder results in a relatively lower performance but offers a considerable amount of temporal efficiency in the training phase. On the other hand, training the whole model after the weight transfer acquires the best result with an improvement of 12.43% in the Intersection over Union (IoU) metric.
1 citations
TL;DR: In this article , a full-resolution perspective of the Cathedral of Como, northern Italy, was used to validate the accuracy of vertical displacement velocities derived from InSAR-derived vertical displacements with a high-precision geodetic levelling measurements.
Abstract: Abstract. Towards revealing the potential of satellite Synthetic Aperture Radar (SAR) Interferometry (InSAR) for efficient detection and monitoring of Cultural Heritage (CH) encouraging resilient built CH, this study is devoted to the validation of InSAR-derived vertical displacements with a full-resolution perspective taking advantage of high-precision geodetic levelling measurements. Considering the Cathedral of Como, northern Italy, as the case study, two different Persistent Scatterer Interferometry (PSI) techniques have been applied to Cosmo-SkyMed high-resolution SAR images acquired in both ascending and descending orbit tacks within the time interval of 2010–2012. Besides using the simplified approach for obtaining the vertical displacement velocity from Line of Sight (LOS) velocity, a weighted, localized, multi-track Vertical Displacement Extraction (VDE) approach is proposed and evaluated, which uses the technical outcome of Differential InSAR (DInSAR) and spatial information. The results, using a proper PSI technique, showed that the accuracy level of extracted vertical displacement velocities in a full-resolution application is ca. 0.6 [mm/year] with a dense concentration of InSAR-Levelling absolute errors lower than 0.3 [mm/year] which are reliable and reasonable levels based on the employed validation framework in this study. Also, the weighted localized VDE can significantly decrease the InSAR-Levelling errors, adding to the reliability of the InSAR application for CH monitoring and condition assessment in practice.
1 citations
TL;DR: In this paper , the authors assessed land subsidence susceptibility for Kashan Plain in Iran using Random Forest (RF) and XGBoost machine learning algorithms, and the results showed that the XGB-oost had a higher R² equal to 0.9032 compared to that of the RF which was equal to0.8355.
Abstract: Abstract. Land subsidence (LS) is one of the most challenging natural disasters that has potential consequences such as damage to infrastructures and buildings, creating sinkholes, and leading to soil destruction. To mitigate the damages caused by LS, it is necessary to determine the LS-prone areas. In this paper, LS susceptibility was assessed for Kashan Plain in Iran using Random Forest (RF) and XGBoost machine learning algorithms. For the susceptibility analysis, twelve influential factors including elevation, slope, aspect, curvature, topographic wetness index (TWI), groundwater drawdown (GWD), normalized difference vegetation index (NDVI), distance to stream (DtS), distance to road (DtR), distance to fault (DtF), lithology, and land use were taken into account. 291 LS points were used in this study which was divided into two parts of 70% and 30% for training and testing the models, respectively. The prediction power of the models and their produced LS susceptibility maps (LSSMs) were validated using the Root Mean Square Error (RMSE), R-Squared (R2), and Mean Absolute Error (MAE) values. The results showed that the XGBoost had a higher R² equal to 0.9032 compared to that of the RF which was equal to 0.8355. XGBoost model had an RMSE equal to 0.3764 cm compared to that of the RF model which was equal to 0.4906 cm. MAE for the XGBoost model was 0.1217 cm and for the RF model was 0.3050 cm. Therefore, the achieved results proved that XGBoost had better performance in this research for predicting LS values based on the measured ones.
1 citations
TL;DR: In this paper , an attempt has been made to compare the results of two different algorithms, CMCD and Selection Signal, based on SNR elevation-dependent in detecting the effect of multipath on smartphones to check their performance for detecting measurements contaminated with multipath.
Abstract: Abstract. Regarding increasing smartphone receivers' usage in science and industry, they must improve their positioning algorithms to increase positioning accuracy and location-based software's productivity. For this goal, various studies have been presented to remove or adjust the errors in the GNSS signal received by smartphones. Nevertheless, so far, no study has been conducted to investigate the effect of the multipath effect on smartphone observations. Various algorithms have been performed to study the effect of multipath, from detecting and removing this effect by correcting errors in the signal processing step to weighting the measurements to reduce the effect of multipath on the observations in the positioning step.In this article, an attempt has been made to compare the results of two different algorithms, CMCD and Selection Signal, based on SNR elevation-dependent in detecting the effect of multipath on smartphones to check their performance for detecting measurements contaminated with multipath.
1 citations
TL;DR: In this paper , a new building detection method based on a density of LiDAR point clouds is proposed, where trees, vegetation, and any objects that have points in a vertical plane or column are removed.
Abstract: Abstract. In this paper, a new building detection method based on a density of LiDAR point clouds is proposed. In this method, trees, vegetation, and any objects that have points in a vertical plane or column are removed. In the density-based method, a cube is utilized to calculate the density therein. For each point, the cube is used to determine the number of neighbouring points. The density is calculated in two cases: 3D and 2D space. In 3D space, the volumetric density is calculated using the cube. In 2D space, all points are projected onto the horizontal plane, and the surface density is calculated using a square. Next, the two densities are compared and the points with different values in both cases are removed. The method leads to promising results in the removal of vegetation and trees. Moreover, the results achieve more than 94% completeness and correctness at the per-area level.
TL;DR: In this paper , a mini-UAV is used to detect litter in the environment and catch it with an accuracy at decimeter level for objects not too far from locations recognizable in the map.
Abstract: Abstract. Most of the anthropic pollution arriving to seas and oceans is carried by rivers, leading to a dramatic impact on the aquatic flora and fauna. Hence, several strategies have already been considered to reduce the use of plastic (and non biodegradable) objects, fostering recycling, detect litter in the environment, and catch it. This work aims at implementing a litter detection approach usable also in urban areas, hence considering a mini-UAV (Unmanned Aerial Vehicle) in order to reduce the issues related to flight restrictions. The implemented strategy exploits a high resolution map of the area of interest, a properly trained deep learning litter object detector, and a vision based localization system, which allows to remarkably reduce the positioning error of the UAV navigation system, in order to provide estimates of the litter object positions with an accuracy at decimeter level for objects not too far from locations recognizable in the map.
TL;DR: In this paper , the first observations of solar UV radiation ever made in NW Iran, obtained in June 2019 are reported in a short but intense measurement campaign in Ardabil-Sarein indicate the trends for geographical latitude, longitude and altitude from surface UV measurements.
Abstract: Abstract. The UV radiation level at the Earth’s surface is generally affected by several factors such as time, geographic location, and climate. The first observations of solar UV radiation ever made in NW Iran, obtained in June 2019 are reported in this work. The analysis of hourly values of UV irradiances measured in the study area reveals significant diurnal variation during daylight hours, with lower values in the morning and afternoon and higher around noon. Mean hourly UV (A+B) ranged from 2755 to 10434 µW/cm2 with an average value being about 7960 µW/cm2. Mean hourly UV (C) ranged from 40 to 91 µW/cm2 with an average value being about 76 µW/cm2. The results of a short but intense measurement campaign in Ardabil-Sarein indicate the trends for geographical latitude, longitude, and altitude from surface UV measurements. The UV intensity is associated with geographical longitude (r2= 0.15 for UV (A+B); r2= 0.13 for UV (C)). Furthermore, UV intensity varies with the local latitude in the study area. There is a strong linear relationship between average UV and altitude and a trend of rising UV with increasing altitude is obtained. A decrease in UV radiation with increasing solar zenith (°) was observed. However, the correlation between UV radiation and solar azimuth (°) was not significant. Understanding the factors influencing near-surface UV radiation through systematic ground-based UV will help determine whether long-term changes occur as a result of changes in cloud cover or climate change, and how specific it means to identifying the causes.
TL;DR: In this paper , the effect of keyframes extraction from the thermal infrared video sequence on the geometric accuracy of the dense point cloud generated is assessed. And the performance evaluation criteria for keyframe extraction in the generation of thermal infrared dense point clouds are evaluated.
Abstract: Abstract. Keyframes extraction is required and effective for the 3D reconstruction of objects from a thermal video sequence to increase geometric accuracy, reduce the volume of aerial triangulation calculations, and generate the dense point cloud. The primary goal and focus of this paper are to assess the effect of keyframes extraction from the thermal infrared video sequence on the geometric accuracy of the dense point cloud generated. The method of keyframes extraction of thermal infrared video presented in this paper consists of three basic steps. (A) The ability to identify and remove blur frames from non-blur frames in a sequence of recorded frames. (B) The ability to apply the standard baseline condition between sequence frames to establish the overlap condition and prevent the creation of degeneracy conditions. (C) Evaluating degeneracy conditions and keyframes extraction using Geometric Robust Information Criteria (GRIC). The performance evaluation criteria for keyframes extraction in the generation of the thermal infrared dense point cloud in this paper are to assess the increase in density of the generated three-dimensional point cloud and reduce reprojection error. Based on the results and assessments presented in this paper, using keyframes increases the density of the thermal infrared dense point cloud by about 0.03% to 0.10% of points per square meter. It reduces the reprojection error by about 0.005% of pixels (2 times).
TL;DR: In this article , a thresholding process on Modified Normalized Difference Water Index (MNDWI) was used to extract the water extent of the International Shadegan Wetland, Iran.
Abstract: Abstract. Understanding the variation of Water Extent (WE) can provide insights into Wetland conservation and management. In this study, and-inter inner-annual variations of WE were analyzed during 2019–2021 to understand the spatiotemporal changes of the International Shadegan Wetland, Iran. We utilized a thresholding process on Modified Normalized Difference Water Index (MNDWI) to extract the WE quickly and accurately using the Google Earth Engine (GEE) platform. The water surface analysis showed that: (1) WE had a downward trend from 2019 to 2021, with the overall average WE being 1405.23 km2; (2) the water area reached its peak due to the water supply to International Shadegan Wetland through the Jarahi River and upstream reservoirs at the end of 2019 and the beginning of 2020, and the largest water body appeared in Winter 2019, reaching 1953.31 km2. In contrast, the smallest water body appeared in Autumn 2021, reaching 563.56 km2; (3) The WE of the wetland showed predictable seasonal characteristics. The water area in Winter was the largest, with an average value of 1829.1 km2, while it was the smallest in Summer, with an average value of 1100.3 km2; (4) The average water area in 2019 was 1490.5 km2 whereas in 2020 and 2021 decreased by 9% and 25%, respectively, and reached 968.6 km2 and 811.9 km2. Finally, to evaluate the proposed model, its results were compared with the Random Forest (RF) classification results. Accordingly, Histogram Analysis (HA) classification achieved 94.6% of the average overall accuracy and the average Kappa coefficient of 0.93, but the RF method obtained 95.38% of the average overall accuracy and an average Kappa coefficient of 0.94.
TL;DR: In this article , two segmentation architectures, the UNet and the Inception ResNet UNet, are implemented and then tested on the Inria aerial image datasets and the analyses show that UNet has a high rate of metrics in the training progress.
Abstract: Abstract. Buildings are one of the key components in change detection, urban planning, and monitoring. The automatic extraction of the building from high-resolution aerial imagery is still challenging due to the variations in their shapes, structures, textures, and colours. Recently, the convolutional neural networks (CNN) show a significant improvement in object detection and extraction that surpasses other methods. To extract building, in this paper two segmentation architectures, the UNet and the Inception ResNet UNet are implemented and then tested on the Inria aerial image datasets. The Inception ResNet UNet utilizes the Inception architecture and residual blocks. This makes the model wide and deep, though there are a few differences between numbers of UNet and Inception ResNet UNet parameters. The analyses show that UNet has a high rate of metrics in the training progress. However, on the unseen dataset, Inception ResNet UNet extracts buildings more accurately (97.95% accuracy and 0.96 in the dice metric) in comparison with UNet (94.30% accuracy and 0.55 in the dice metric).
TL;DR: In this article , the authors used machine learning models including regularized random forest (RRF) and Naïve Bayes (NB) algorithms to predict flood susceptibility areas using 410 sample points (205 flood points and 205 non-flood points).
Abstract: Abstract. Floods have caused significant socio-economic damage and are extremely dangerous for human lives as well as infrastructures. The aim of this study is to use machine learning models including regularized random forest (RRF) and Naïve Bayes (NB) algorithms to predict flood susceptibility areas using 410 sample points (205 flood points and 205 non-flood points). Ten flood influencing factors including elevation, topographic wetness index, rainfall, normalized difference vegetation index, curvature, land use, distance to river, slope, lithology, and aspect have been used in the modelling process. For this purpose, 70% of the data was used for training and the rest employed for testing the models. Accuracy (ACC), sensitivity, specificity, negative predictive value (NPV), and the area under the curve (AUC) of the receiver operating characteristic (ROC) were used to validate and compare the performance of the models. The results showed that the RRF model on the testing dataset had the highest performance (AUC = 0.94, ACC = 90%, Sensitivity = 0.89, Specificity = 0.92, NPV = 0.89) compared to that of the NB model (AUC = 0.93, ACC = 89%, Sensitivity = 0.84, Specificity = 0.96, NPV = 0.81). The employed models can be used as an efficient tool for flood susceptibility mapping with the purpose of planning to reduce the damages.
TL;DR: In this paper , the newly available PRISMA spectra were exploited to retrieve the leaf area index (LAI) of sugarcane using a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Network (BRANN).
Abstract: Abstract. The PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency, lunched in 2019, has provided a new generation source of hyperspectral data showing to have high potential in vegetation variable retrieval. In this study, the newly available PRISMA spectra were exploited to retrieve Leaf Area Index (LAI) of sugarcane using a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Network (BRANN). The suggested BRANN retrieval model was implemented over a dataset collected during a field campaign in Amir Kabir Sugarcane Agro-Industrial zone, Khuzestan, Iran, in 2020. Principle Component Analysis (PCA) was utilized to reduce the dimensionality of PRISMA data cube. An accuracy assessment based on the bootstrapping procedure indicated RMSE of 0.67 m2/m2 for the LAI retrieval by applying the BRANN model. This study is a confirmation of the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI.
TL;DR: In this article , the authors examined the efficiency of the variant components of the geodesic strain rate tensor in interpreting deformations of north-western Iran, using the velocity field gathered from a previous article, and also using a simple and straightforward method.
Abstract: Abstract. Northwest of Iran, as a tectonically active region, has experienced numerous devastating earthquakes. That is why it is so important to study the earth deformation in this area and to provide more precise insights. So far, most researchers have had the preference of using the invariant component of strain rate tensor for investigating the Earth's shape deformation in the region. However, to examine the efficiency of the variant components of the geodesic strain rate tensor in interpreting deformations of north-western Iran, we have in this article maps of variant components of the geodetic strain rate tensor (normal strain rate along north and eastbound). Using the velocity field gathered from a previous article, and also using a simple and straightforward method, the strain rate tensors were calculated. The obtained contraction along the north direction (from the normal strain along this axis) confirms the Eurasia-Arabia collision. Besides, the obtained extension along the east direction and the derived expansion of the dilatation, show the effect of Anatolian motion to the west and eastward movement of the central Iran plateau on the tectonic structure of the studied area. These two results showed that the variant component of strain rate tensor also provides us with useful information about a region shape deformation.
TL;DR: Wang et al. as discussed by the authors used the knowledge from the construction rules to label the segmented surface primitives into correct categories, and the corresponding height constraint, concave-convex constraint, and symmetry constraint were proposed as judgment conditions to mark the geometric elements belonging to the same dougong component.
Abstract: Abstract. To solve the problem that Dougong has various shapes and complex structures, the corresponding solutions are proposed in this paper. Our proposed method mainly consist of two parts. At first, the surface primitives were segmented using the machine learning (Random Forest). In this stage, the features including the curvature, normas and other features based on covariance matrix. Then, the knowledge from the construction rules were applied to label the segmented surface primitives into correct categories. The corresponding height constraint, concave-convex constraint, and symmetry constraint are proposed as the judgment conditions to mark the geometric elements belonging to the same dougong component and complete the point cloud segmentation of the dougong component. To verify the performance of our proposed method, the point cloud of a Qing-style single-arch flat-bodied Dougong was tested. The experimental results show that the classification accuracy of point cloud is 96.0%.
TL;DR: In this article , the orthophoto and digital elevation model (DTM) produced from aerial images taken by Aeria-X camera mounted on Sensefly eBee-X drone was employed to identify and map eroded areas by gully in Kajoo-Gargaroo watershed in Chabahar, south-eastern part of Iran.
Abstract: Abstract. Monitoring and mapping eroded lands by gully erosion is an essential step to control gully networks. Advances in remote sensing and aerial photography have enabled users to capture data with variant temporal and spatial resolution that is needed in different fields. In addition, introducing different types of unmanned aerial vehicles (UAV) enabled to carry imaging payload. The orthophoto and digital elevation model (DTM) produced from aerial images taken by Aeria-X camera mounted on Sensefly eBee-X drone was employed to identify and map eroded areas by gully in Kajoo-Gargaroo watershed in Chabahar, south-eastern part of Iran. Digitizing gully boarders manually is a tiring and time-consuming process for the operators. Maximum likelihood algorithm as one of the machine learning algorithm was also used to classify orthophoto in order to extract gully borders in the study area. In this study a new algorithm based on analysing geometric features and clustering of the DTM was used to map gullies automatically. The results of the proposed method and machine learning algorithm were compared with the manually digitized gully map. Quantitative evaluation demonstrates that our proposed method reaches better overall accuracy compared to machine learning algorithm with the increase of 7.2 percent in overall accuracy.
TL;DR: In this article , the root mean square error (RMSE) for the IMQ interpolation method was the lowest compared to other methods and was equal to 2.11 mm.
Abstract: Abstract. Precipitable water vapor (PWV) is one of the most critical data in many meteorological departments. This component has great spatial and temporal changes, so the global positioning system (GPS) always seeks to increase the accuracy of estimating the water vapor component in the troposphere. The waves sent by the satellites of this system are delayed due to passing through atmospheric layers such as the troposphere. In this paper, interpolation methods are used to estimate precipitable water vapor. Inverse multiquadric (IMQ) interpolation which is based on radial basis functions, artificial neural network (ANN) method, and inverse distance weighted (IDW) which are the most common method of interpolation in meteorology. A region in North America with 23 GPS stations was randomly selected. Then, the interpolation of precipitable water vapor on a summer day is done using GPS data. The root mean square error value (RMSE) for the IMQ method was the lowest compared to other methods and was equal to 2.11 mm. Finally, using the IMQ interpolation method, a dense map of Precipitable water vapor changes in the troposphere layer is developed for the study area.
TL;DR: In this paper , a case study involving the identification of optimal areas for restaurants in Babolsar, Mazandaran province was used, and the degree of similarity between the areas (polygons) proposed by citizens was then investigated using spatial indicators of intersection and minimum central distance.
Abstract: Abstract. Volunteered citizens have the potential to be used as social and distributed sensors, monitoring their surroundings and producing and sharing massive amounts of geographic data. The degree of spatial similarities in Volunteered Geographic Information (VGI) somehow indicates an index for the accuracy and precision of user-generated spatial data. In other words, the spatial similarities in VGI refer to how close a citizen-generated spatial feature is to the true (or accepted one) or how close the citizen-generated spatial features of the same geographic phenomenon are to each other. The present study aims at developing a Web-based GIS tool to collect VGI and extract the spatial similarity indexes. To this end, a case study involving the identification of optimal areas for restaurants in Babolsar, Mazandaran province was used. The degree of similarity between the areas (polygons) proposed by citizens was then investigated using spatial indicators of intersection and minimum central distance. The results show that with the increase in the frequency of citizen-generated polygons, the geometric dispersion of the polygons decreases, and the similarity of citizens’ polygons to establish a restaurant increases. With the increasing agreement, the amount of standard deviation in the area, perimeter, and minimum central distance of intersection areas reduces from 4645 to 15.4, 134.5 to 21.6, and 42.4 to 4.2, respectively.
TL;DR: The ZEB Revo laser scanner was used for data acquisition, which is much more suitable for such areas than conventional terrestrial scanners, with which the work is lengthy and not nearly as efficient, and not the least of which eliminates the time-consuming post-scan work of merging scans as discussed by the authors .
Abstract: Abstract. The article focuses on modern methods of mining activity documentation and visualization. The development of technologies that allow us to display 3D models are many in recent years, from web portals, augmented reality using smartphones or tablets to virtual reality glasses. These technologies give us a whole new way of looking at and viewing 3D models. In this study, we will use most of the available options to visualize mining activities in the UNESCO-protected Ore Mountains. Each of these technologies needs differently prepared data. Because each of these technologies have different limits, we need to find these limits and modify the 3D models. The ZEB Revo laser scanner was used for data acquisition, which uses SLAM technology and is much more suitable for such areas than conventional terrestrial scanners, with which the work is lengthy and not nearly as efficient, and not the least of which eliminates the time-consuming post-scan work of merging scans. Especially when using scanning for mining, where there is not quite enough to go around, manually splicing scans is a nightmare, and automatic post-production merging is also often problematic.
TL;DR: In this article , the authors compare the history of the theoretical conservation approach to living heritage and explain its core concepts and basic methods, and analyze the application and effectiveness of the community co-creation model in living heritage sites using the case of the ancient city of Pingyao in Shanxi Province, China.
Abstract: Abstract. Since the conservation of living heritage sites such as historic towns and traditional villages often involves the coordination of multiple interests, the modern socio-economic development of heritage sites and the dominant participatory nature of local heritage communities become the main conflicts in practice. Using a combination of literature research and field cases, this paper firstly compares the history of the theoretical conservation approach to living heritage and explains its core concepts and basic methods. Secondly, it analyses the application and effectiveness of the community co-creation model in living heritage sites using the case of the ancient city of Pingyao in Shanxi Province, China. It is hoped that this will provide a more comprehensive understanding of the current development and future direction of heritage conservation methods, and further consider how to reconcile the historical and daily values of heritage in the conservation process.
TL;DR: In this paper , the seasonality and magnitude of Level-3 Chl-a time-series products, and daily Level-2 satellite-derived Chla concentrations were assessed using corresponding/concurrent in-situ observations gathered during 2008 to 2018.
Abstract: Abstract. The Persian Gulf is a peripheral sea that is quite turbid and visually complicated. Because of the complicated oceanic and atmospheric optical features of this maritime area, satellite remotely sensed chlorophyll-a (Chl-a, mg m−3) outputs have been used extensively. In this study, the seasonality and magnitude of Level-3 Chl-a time-series products, and daily Level-2 satellite-derived Chl-a concentrations were assessed using corresponding/concurrent in-situ observations gathered during 2008 to 2018. The results revealed that the field observations overestimated satellite-derived Chl-a by 115% in the deeper areas and up to 161% along the Iranian coastal waters. Comparison of inter-annual Chl-a time-series datasets with corresponding in-situ measurements showed that temporal patterns of the satellite-derived Chl-a values are not consistent with field observations. The monthly average of the satellite Chl-a series shows a different seasonal pattern in every region of the study area, and their magnitude over-estimated from 21% to 83% relative to in-situ observations. The absorption coefficients at 488–510 nm are primarily influenced by non-living particles instead of phytoplankton pigments, and no significant correlation are found between in-situ and Chl-a values from OC3/OC4 algorithms. Absorption coefficients spectra of water constituent’s shows that the contribution of phytoplankton pigments in particulate absorption coefficients at the blue-green bands are 48%–59%, and the particulate absorptions of water bodies are under the strong influence of non-living particles (40%–52%). Our results suggest that the red-green band-ratios algorithm centred at 675 nm and 555 nm presents more accurate results than OC3/OC4 over the study area.
TL;DR: In this paper , the authors evaluated the RGB indices to monitor the growth of the rice crop using a DJI PM4 multispectral UAV and the results showed that RGB indices can be used as the vegetation index in the case of unavailable multi-spectral images.
Abstract: Abstract. The unmanned aerial vehicles (UAVs) are widely used for agricultural monitoring due to reduce the cost and time of crop monitoring via the acquisition of images with high spatial-temporal resolution. The normalized difference vegetation index (NDVI) is the most widely studied and used for mapping crop growth. A relatively expensive multispectral sensor is required to produce an NDVI map. The visible vegetation indices (VIs) derived from UAV images showed potential capabilities for predicting crop growth. The purpose of this paper is to evaluate the RGB indices to monitor the growth of the rice crop. The images were obtained from the study area by DJI PM4 multispectral UAV. The multispectral images were used to calculate NDVI as a reference vegetation index and different RGB indices were implemented and compared with the reference index. The results showed that RGB indices can be used as the vegetation index in the case of unavailable multispectral images.
TL;DR: Wu et al. as discussed by the authors explored the "trinity" innovation and entrepreneurship talent training model of "promoting learning through competition, education through competition and innovation through competition" to enhance the stickiness among colleges, educators, students, and social enterprises, and enable students to participate in extracurricular academic activities that are highly consistent with the needs of the industrial market.
Abstract: Abstract. Innovation leads the development of technology and society, so the position of innovation and entrepreneurship training to nurture innovative talents is very important. The paper explores the ‘trinity’ innovation and entrepreneurship talent training model of ‘promoting learning through competition, education through competition, and innovation through competition’, focus on high level innovation and entrepreneurship competitions, enhance the stickiness among colleges, educators, students, and social enterprises, and enable students to participate in extracurricular academic activities that are highly consistent with the needs of the industrial market. To achieve this goal, School of Remote Sensing and Information Engineering of Wuhan University puts forward a ‘remote sensing +’ training model. Through the two-year practice of this model, 136 students from different schools and majors participated in the training of the ‘Remote Sensing +’ Innovation and Entrepreneurship Center in Wuhan University, and won three gold awards in the China's largest innovation and entrepreneurship competition. Due to the characteristics of cross-integration of remote sensing science and technology itself, it has played a supporting role in innovation education, integrating other multi-specialized knowledge, and providing more entrepreneurial and employment opportunities for students of related majors.
TL;DR: Wang et al. as discussed by the authors compared the Italian Stradivari violin with Guqin from the Tang Dynasty and found that the wood, paint, and inner cavity design used by the two masters during their respective heydays are strikingly similar, revealing their shared experience in creating ancient musical instruments in their countries.
Abstract: Abstract. Guqin is the Chinese oldest musical instrument and intangible cultural heritage. The Tang Dynasty was the most prosperous in Chinese history, with a flourishing culture. One of the Guqin manufactured by Master Lei Wei, a notable Guqin maker in the Tang Dynasty, was recognized as a reference standard instrument and is kept in the Palace Museum in Beijing. Stradivari, a well-known Italian violin maker, has created and improved violins that no one else can surpass. Triangulation laser and CT/CBCT Scanner-Based 3D modelling data-driven analysis of the ancient musical instrument Guqin from the Tang Dynasty has been compared with the Italy Stradivari violin in a historical review perspective in this paper. After delving into the wood, spatial structure, and other aspects of the Stradivari violin, it has been discovered that the wood, paint, and inner cavity design used by the two masters during their respective heydays are strikingly similar, revealing their shared experience in creating ancient musical instruments in their countries. The bass bar of a Stradivari violin is meant to be transplanted into a Chinese Guqin prototype and get the conclusion that the timbre of the Guqin can be improved according to MATLAB spectrum analysis. This is the first time the two masters from Eastern and Western comparison analysis after the millennium.
TL;DR: In this paper , using the Google Earth engine system and various sources of remote sensing data, the flooded areas of 2019 in Khuzestan province of Iran were extracted and the area of damaged agricultural lands was estimated.
Abstract: Abstract. Floods are one of the most dangerous crises that cause a lot of damage in various fields, including economic and human lives. Therefore, preparation for prevention and damage assessment in order to manage this crisis is essential. In the meantime, providing methods with high speed and accuracy together can be helpful. In this study, using the Google Earth engine system and various sources of remote sensing data, the flooded areas of 2019 in Khuzestan province of Iran were extracted and the area of damaged agricultural lands was estimated. The general method was to first use the Sentinel 1 images, which are independent of the cloud, and the JRC global surface water mapping data to obtain flooded areas. After that, with the help of Sentinel 2 images and extracting various features from its bands and implementing an automated method, a map of damaged agricultural lands was also prepared. In order to approximate the affected population, WorldPop Global Project Population data has been used to take advantage of the maximum capacity of various remote sensing sources. The resulting flood map was evaluated by a ground truth map to prove the efficiency of the method. The overall accuracy of the map was 96.30 and its kappa coefficient was 80.03, which is quantitatively appropriate. The proposed method and the system used, due to their simplicity, can be generalized at high speed to other areas.