Search Results (225)

The automatic extraction of large-scale tie points (TPs) for Synthetic Aperture Radar (SAR) images is crucial for generating SAR Digital Orthophoto Maps (DOMs). This task involves matching SAR images under various conditions, such as different resolutions, incidence angles, and orbital directions, which is highly challenging. To address the feature extraction challenges of different SAR images, we propose a Gamma Modulated Phase Congruency (GMPC) model. This improved phase congruency model is defined by a Gamma Modulation Filter (GMF) and an adaptive noise model. Additionally, to reduce layover interference in SAR images, we introduce a GMPC-Harris feature point extraction method with layover perception. We also propose a matching method based on the SAR Modality Independent Neighborhood Fusion (SAR-MINF) descriptor, which fuses feature information from different neighborhoods. Furthermore, we present a graph-based overlap extraction algorithm and establish an automated workflow for large-scale TP extraction. Experiments show that the proposed SAR-MINF matching method increases the Correct Match Rate (CMR) by an average of 31.2% and the matching accuracy by an average of 57.8% compared with other prevalent SAR image matching algorithms. The proposed TP extraction algorithm can extract full-degree TPs with an accuracy of less than 0.5 pixels for more than 98% of 2-degree TPs and over 95% of multidegree TPs, meeting the requirements of DOM production. Full article

Constructing high-precision subsidence basins is of paramount importance for mining subsidence monitoring. Traditional unmanned aerial vehicle (UAV) photogrammetry techniques typically construct subsidence basins by directly differencing digital elevation models (DEMs) from different monitoring periods. However, this method often neglects the influence of horizontal displacement on the accuracy of the subsidence basin. Taking a mining area in Ordos, Inner Mongolia, as an example, this study employed the normalized cross-correlation (NCC) matching algorithm to extract horizontal displacement information between two epochs of a digital orthophoto map (DOM) and subsequently corrected the horizontal position of the second-epoch DEM. This ensured that the planar positions of ground feature points remained consistent in the DEM before and after subsidence. Based on this, the vertical displacement in the subsidence area (the subsidence basin) was obtained via DEM differencing, and the parameters of the post-correction subsidence basin were inverted using the probability integral method (PIM). The experimental results indicate that (1) the horizontal displacement was influenced by the gully topography, causing the displacement within the working face to be segmented on both sides of the gully; (2) the influence of the terrain on the subsidence basin was significantly reduced after correction; (3) the post-correction surface subsidence curve was smoother than the pre-correction curve, with abrupt error effects markedly diminished; (4) the accuracy of the post-correction subsidence basin increased by 43.12% compared with the total station data; and (5) comparing the measured horizontal displacement curve with that derived using the probability integral method revealed that the horizontal displacement on the side of an old goaf adjacent to the newly excavated working face shifted toward the advancing direction of the new working face as mining progressed. This study provides a novel approach and insights for using low-cost UAVs to construct high-precision subsidence basins. Full article

Detection and mapping of Sosnowsky’s hogweed (HS) using remote sensing data have proven effective, yet challenges remain in identifying, localizing, and eliminating HS in urban districts and regions. Reliable data on HS growth areas are essential for monitoring, eradication, and control measures. Satellite data alone are insufficient for mapping the dynamics of HS distribution. Unmanned aerial vehicles (UAVs) with high-resolution spatial data offer a promising solution for HS detection and mapping. This study aimed to develop a method for detecting and mapping HS growth areas using a proposed algorithm for thematic processing of multispectral aerial imagery data. Multispectral data were collected using a DJI Matrice 200 v2 UAV (Dajiang Innovation Technology Co., Shenzhen, China) and a MicaSense Altum multispectral camera (MicaSense Inc., Seattle, WA, USA). Between 2020 and 2022, 146 sites in the Moscow region of the Russian Federation, covering 304,631 hectares, were monitored. Digital maps of all sites were created, including 19 digital maps (orthophoto, 5 spectral maps, and 13 vegetation indices) for four experimental sites. The collected samples included 1080 points categorized into HS, grass cover, and trees. Student’s t-test showed significant differences in vegetation indices between HS, grass, and trees. A method was developed to determine and map HS-growing areas using the selected vegetation indices NDVI > 0.3, MCARI > 0.76, user index BS1 > 0.10, and spectral channel green > 0.14. This algorithm detected HS in an area of 146.664 hectares. This method can be used to monitor and map the dynamics of HS distribution in the central region of the Russian Federation and to plan the required volume of pesticides for its eradication. Full article

Earthen architecture holds deep historical, cultural, and ecological value, forming an essential component of our global cultural heritage. However, these structures face numerous threats, including climate change, socio-economic shifts, and, in many cases, neglection, which accelerate their deterioration. This study introduces a photogrammetry-based methodology adapted for the digital documentation and preservation of earthen architecture within the context of developing countries. We focus on the Ex-Hotel Oasis Rouge in Timimoun, an iconic earthen building in southwestern Algeria and the current headquarters of CAPTERRE (Algerian Centre for Earthen Built Cultural Heritage). This paper details our approach to using photogrammetry to capture both the interior and exterior of the building, produce detailed orthophotos for archiving the unique earthen bas-reliefs, and conduct a condition assessment. Our findings demonstrate the effectiveness of photogrammetry as a cost-effective tool for heritage documentation, highlighting its potential to assist in the ongoing preservation and informed restoration of earthen architecture. Full article

Coal mining in the Loess Plateau can very easily generate ground cracks, and these cracks can immediately result in ventilation trouble under the mine shaft, runoff disturbance, and vegetation destruction. Advanced UAV (Unmanned Aerial Vehicle) high-resolution mapping and DL (Deep Learning) are introduced as the key methods to quickly delineate coal mining ground surface cracks for disaster prevention. Firstly, the dataset named the Ground Cracks of Coal Mining Area Unmanned Aerial Vehicle (GCCMA-UAV) is built, with a ground resolution of 3 cm, which is suitable to make a 1:500 thematic map of the ground crack. This GCCMA-UAV dataset includes 6280 images of ground cracks, and the size of the imagery is 256 × 256 pixels. Secondly, the DRA-UNet model is built effectively for coal mining ground surface crack delineation. This DRA-UNet model is an improved UNet DL model, which mainly includes the DAM (Dual Dttention Dechanism) module, the RN (residual network) module, and the ASPP (Atrous Spatial Pyramid Pooling) module. The DRA-UNet model shows the highest recall rate of 77.29% when the DRA-UNet was compared with other similar DL models, such as DeepLabV3+, SegNet, PSPNet, and so on. DRA-UNet also has other relatively reliable indicators; the precision rate is 84.92% and the F1 score is 78.87%. Finally, DRA-UNet is applied to delineate cracks on a DOM (Digital Orthophoto Map) of 3 km² in the mining workface area, with a ground resolution of 3 cm. There were 4903 cracks that were delineated from the DOM in the Huojitu Coal Mine Shaft. This DRA-UNet model effectively improves the efficiency of crack delineation. Full article

Impact craters are crucial for our understanding of planetary resources, geological ages, and the history of evolution. We designed a novel pseudo-spectral spatial feature extraction and enhanced fusion (PSEF) method with the YOLO network to address the problems encountered during the detection of the numerous and densely distributed meter-sized impact craters on the lunar surface. The illumination incidence edge features, isotropic edge features, and eigen frequency features are extracted by Sobel filtering, LoG filtering, and frequency domain bandpass filtering, respectively. Then, the PSEF images are created by pseudo-spectral spatial techniques to preserve additional details from the original DOM data. Moreover, we conducted experiments using the DES method to optimize the post-processing parameters of the models, thereby determining the parameter ranges for practical deployment. Compared with the Basal model, the PSEF model exhibited superior performance, as indicated by multiple measurement metrics, including the precision, recall, F₁-score, mAP, and robustness, etc. Additionally, a statistical analysis of the error metrics of the predicted bounding boxes shows that the PSEF model performance is excellent in predicting the size, shape, and location of impact craters. These advancements offer a more accurate and consistent method to detect the meter-sized craters on planetary surfaces, providing crucial support for the exploration and study of celestial bodies in our solar system. Full article

For the accurate and automatic mapping of forest stands based on very-high-resolution satellite imagery and digital orthophotos, precise object detection at the individual tree level is necessary. Currently, supervised deep learning models are primarily applied for this task. To train a reliable model, it is crucial to have an accurate tree crown annotation dataset. The current method of generating these training datasets still relies on manual annotation and labeling. Because of the intricate contours of tree crowns, vegetation density in natural forests and the insufficient ground sampling distance of the imagery, manually generated annotations are error-prone. It is unlikely that the manually delineated tree crowns represent the true conditions on the ground. If these error-prone annotations are used as training data for deep learning models, this may lead to inaccurate mapping results for the models. This study critically validates manual tree crown annotations on two study sites: a forest-like plantation on a cemetery and a natural city forest. The validation is based on tree reference data in the form of an official tree register and tree segments extracted from UAV laser scanning (ULS) data for the quality assessment of a training dataset. The validation results reveal that the manual annotations detect only 37% of the tree crowns in the forest-like plantation area and 10% of the tree crowns in the natural forest correctly. Furthermore, it is frequent for multiple trees to be interpreted in the annotation as a single tree at both study sites. Full article

Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired through remote sensing and geomatics techniques, has facilitated the safe exploration of otherwise inaccessible areas. The datasets that can be gathered from these techniques, typically combined with data from fieldwork, can subsequently undergo analyses employing/applying machine learning algorithms and/or numerical modeling, in order to identify/discern the predominant influencing factors affecting cliff top erosion. This study focuses on a specific case situated at the Conero promontory of the Adriatic Sea in the Marche region. The research methodology entails several steps. Initially, the morphological, geological and geomechanical characteristics of the areas were determined through unmanned aerial vehicle (UAV) and conventional geological/geomechanical surveys. Subsequently, cliff top retreat was determined within a GIS environment by comparing orthophotos taken in 1978 and 2022 using the DSAS tool (Digital Shoreline Analysis System), highlighting cliff top retreat up to 50 m in some sectors. Further analysis was conducted via the use of two Machine Learning (ML) algorithms, namely Random Forest (RF) and eXtreme Gradient Boosting (XGB). The Mean Decrease in Impurity (MDI) methodology was employed to assess the significance of each factor. Both algorithms yielded congruent results, emphasising that cliff top erosion rates are primarily influenced by slope height. Finally, a validation of the ML algorithm results was conducted using 2D Limit Equilibrium Method (LEM) codes. Ten sections extracted from the sector experiencing the most substantial cliff top retreat, as identified by DSAS, were utilised for 2D LEM analysis. Factor of Safety (FS) values were identified and compared with the cliff height of each section. The results from the 2D LEM analyses corroborated the outputs of the ML algorithms, showing a strong correlation between the slope instability and slope height (R² of 0.84), with FS decreasing with slope height. Full article

Historic Building Information Modelling (HBIM) plays a pivotal role in heritage conservation endeavours, offering a robust framework for digitally documenting existing structures and supporting conservation practices. However, HBIM’s efficacy hinges upon the implementation of case-specific approaches to address the requirements and resources of each individual asset and context. This paper defines a flexible and generalisable workflow that encompasses various aspects (i.e., documentation, surveying, vulnerability assessment) to support risk-informed decision making in heritage management tailored to the peculiar conservation needs of the structure. This methodology includes an initial investigation covering historical data collection, metric and condition surveys and non-destructive testing. The second stage includes Finite Element Method (FEM) modelling and structural analysis. All data generated and processed are managed in a multi-purpose HBIM model. The methodology is tested on a relevant case study, namely, the church of Santa Ana in Seville, chosen for its historical significance, intricacy and susceptibility to seismic action. The defined level of detail of the HBIM model is sufficient to inform the structural analysis, being balanced by a more accurate representation of the alterations, through linked orthophotos and a comprehensive list of alphanumerical parameters. This ensures an adequate level of information, optimising the trade-off between model complexity, investigation time requirements, computational burden and reliability in the decision-making process. Field testing and FEM analysis provide valuable insight into the main sources of vulnerability in the building, including the connection between the tower and nave and the slenderness of the columns. Full article

Oblique photography is a regional digital surface model generation technique that can be widely used for building 3D model construction. However, due to the lack of geometric and semantic information about the building, these models make it difficult to differentiate more detailed components in the building, such as roofs and balconies. This paper proposes a deep learning-based method (U-NET) for constructing 3D models of low-rise buildings that address the issues. The method ensures complete geometric and semantic information and conforms to the LOD2 level. First, digital orthophotos are used to perform building extraction based on U-NET, and then a contour optimization method based on the main direction of the building and the center of gravity of the contour is used to obtain the regular building contour. Second, the pure building point cloud model representing a single building is extracted from the whole point cloud scene based on the acquired building contour. Finally, the multi-decision RANSAC algorithm is used to segment the building detail point cloud and construct a triangular mesh of building components, followed by a triangular mesh fusion and splicing method to achieve monolithic building components. The paper presents experimental evidence that the building contour extraction algorithm can achieve a 90.3% success rate and that the resulting single building 3D model contains LOD2 building components, which contain detailed geometric and semantic information. Full article

Following the processing of the Chang’e-4 satellite images, Chang’e-4 landing camera images, and Yutu-2 panoramic camera images, data were obtained in a variety of resolutions, including digital elevation model (DEM) and digital orthophoto map (DOM). By determining the morphological parameters, including the depths and diameters of impact craters in the study area, as well as their degradation classes based on surface texture features, we conducted a comprehensive analysis of the morphological parameters and population degradation patterns of impact craters from multiple platforms. The data from three platforms were employed to identify 12,089 impact craters with diameters ranging from 0.1 m to 800.0 m, which were then classified into five degradation classes based on their morphology in the images. This study indicates that as the size of impact craters increases, the population within them experiences a greater degree of degradation. However, the severe degradation of impact craters with diameters of less than 1 m or even 2 m is influenced by the rapid rate of degradation of the crater and the low solidity of the crater lips. The results of the equilibrium state of impact craters indicate that for sub-metre-sized impact craters (with diameters below 2.0 m), it is challenging to reach equilibrium. Furthermore, the smaller the impact crater, the more difficult it is to achieve equilibrium, which is probably the result of simpler generation conditions and the faster degradation of small impact craters. Full article

The spread of natural fires is a complex issue, as its mathematical modeling needs to consider many parameters. Therefore, the results of such modeling always need to be validated by comparison with experimental measurements under real-world conditions. Remote sensing with the support of satellite or aerial sensors has long been used for this purpose. In this article, we focused on data collection with an unmanned aerial vehicle (UAV), which was used both for creating a digital surface model and for dynamic monitoring of the spread of controlled grassland fires in the visible spectrum. We subsequently tested the impact of various processing settings on the accuracy of the digital elevation model (DEM) and orthophotos, which are commonly used as a basis for analyzing fire spread. For the DEM generated from images taken during the final flight after the fire, deviations did not exceed 0.1 m compared to the reference model from LiDAR. Scale errors in the model with only approximal WGS84 exterior orientation parameters did not exceed a relative accuracy of 1:500, and possible deformations of the DEM up to 0.5 m in height had a minimal impact on determining the rate of fire spread, even with oblique images taken at an angle of 45°. The results of the experiments highlight the advantages of using low-cost SfM photogrammetry and provide an overview of potential issues encountered in measuring and performing photogrammetric processing of fire spread. Full article

Mapping the terrain and the Earth’s surface can be performed through non-contact methoYes, that is correct.ds such as laser scanning. This is one of the most dynamic and effective data collection methods. This case study aims to analyze the usability of spatial data from available sources and to choose the appropriate solutions and procedures for processing the point cloud of the area of interest obtained from available web applications. The processing of the point cloud obtained by airborne laser scanning results in digital terrain models created in selected software. The study also included modeling of different types of residential development, and the results were evaluated. Different data sources may have compatibility issues, which means that the position of the same object from different spatial data databases may not be identical. To address this, deviations of the corresponding points were determined from various data sources such as Real Estate Cadaster, ZBGIS Buildings, LiDAR point cloud, orthophoto mosaic, and geodetic measurements. These deviations were analyzed according to their size and orientation, with the average deviations ranging from 0.22 to 0.34 m and standard deviations from 0.11 to 0.20 m. The Real Estate Cadaster was used as the correct basis for comparison. The area of the building was also compared, with the slightest difference being present between the Real Estate Cadaster and geodetic measurement. The difference was zero after rounding the area to whole numbers. The maximum area difference was +5 m² for ZBGIS Buildings. Full article

High-resolution and high-accuracy mapping products of the Lunar South Pole (LSP) will play a vital role in future lunar exploration missions. Existing lunar global mapping products cannot meet the needs of engineering tasks, such as landing site selection and rover trajectory planning, at the LSP. The Lunar Reconnaissance Orbiter (LRO)’s narrow-angle camera (NAC) can acquire submeter images and has returned a large amount of data covering the LSP. In this study, we combine stereo-photogrammetry and photoclinometry to generate high-resolution digital orthophoto maps (DOMs) and digital elevation models (DEMs) using LRO NAC images for a candidate landing site at the LSP. The special illumination and landscape characteristics of the LSP make the derivation of high-accuracy mapping products from orbiter images extremely difficult. We proposed an easy-to-implement shadow recognition and contrast stretching method based on the histograms of the LRO NAC images, which is beneficial for photogrammetric and photoclinometry processing. In order to automatically generate tie points, we designed an image matching method considering LRO NAC images’ features of long strips and large data volumes. The terrain and smoothness constraints were introduced into the cost function of photoclinometry adjustment, excluding pixels in shadow areas. We used 61 LRO NAC images to generate mapping products covering an area of 400 km². The spatial resolution of the generated DOMs was 1 m/pixel, and the grid spacing of the derived DEMs was 1 m (close to the spatial resolution of the original images). The generated DOMs achieved a relative accuracy of better than 1 pixel. The geometric accuracy of the DEM derived from photoclinometry was consistent with the lunar orbiter laser altimeter (LOLA) DEM with a root mean square error of 0.97 m and an average error of 0.17 m. Full article

Chang’e-6 (CE-6) is the first sample-return mission from the lunar farside and will be launched in May of 2024. The landing area is in the south of the Apollo basin inside the South Pole Aitken basin. Statistics and analyses of impact craters in the landing area are essential to support safe landing and geologic studies. In particular, the crater size–frequency distribution information of the landing area is critical to understanding the provenance of the CE-6 lunar samples to be returned and can be used to verify and refine the lunar chronology model by combining with the radioisotope ages of the relevant samples. In this research, a digital orthophoto map (DOM) mosaic with resolution of 3 m/pixel of the CE-6 landing area was generated from the 743 Narrow Angle Camera of the Lunar Reconnaissance Orbiter Camera. Based on the DOM, craters were extracted by an automated method and checked manually. A total of 770,731 craters were extracted in the whole area of 246 km × 135 km, 511,484 craters of which were within the mare area. Systematic analyses of the crater distribution, completeness, spatial density, and depth-to-diameter ratio were conducted. Geologic model age estimation was carried out in the mare area that was divided into three geologic units according to the TiO₂ abundance. The result showed that the east part of the mare had the oldest model age of $\mu {3.27}_{-0.045}^{+0.036}$ Ga, and the middle part of the mare had the youngest model age of $\mu {2.49}_{-0.073}^{+0.072}$ Ga. The crater catalogue and the surface model age analysis results were used to support topographic and geologic analyses of the pre-selected landing area of the CE-6 mission before the launch and will contribute to further scientific researches after the lunar samples are returned to Earth. Full article