A New Lunar Dome Detection Method Based on Improved YOLOv7.

This paper study the effect of the Digital Elevation Model (DEM) data parameters ( type, source, and resolution) on a slope unit and landslide susceptibility analysis using Geographic Information System (GIS). The Landslide susceptibility analysis and slope unit delineation is done using six DEM data from three different sources (Bige Map, SRTM, and Wietu) at varying resolutions and pixel sizes. The Frequency Ratio Method is then used to analyze Ghana’s susceptibility to seismic landslides whiles; a pseudo-static critical slip method (Factor of safety method) is used to check the accuracy and also indicate the specific slopes to fail at specific seismic loading. The research aimed to achieve the goal since the method allowed for the attainment of an optimum slope unit and landslide map for each resolution of data regardless of the source. The procedure permitted and informed a decision making on the best DEM DATA and resolution for slope unit and Landslide analysis. The result showcases a DEM Resolution of 18 and 30 m and pixel 30m*30m size to be the best DEM to be used for landslide susceptibility mapping. The results further prove Bige Map and STRM DEM to give a more precise effect than weitu.

Flood incidents that often occur in Karawang regency need to be mitigated. These expectations exist on technologies that can predict, anticipate and reduce disaster risks. Flood modeling techniques using Digital Elevation Model (DEM) data can be applied in mitigation activities. High accuracy DEM data used in modeling, will result in better flooding flood models. The result of high accuracy DEM data processing will yield information about surface morphology which can be used to identify indication of flood hazard area. The purpose of this study was to identify and describe flood hazard areas by identifying wetland areas using DEM data and Landsat-8 images. TerraSAR-X high-resolution data is used to detect wetlands from landscapes, while land cover is identified by Landsat image data. The Topography Wetness Index (TWI) method is used to detect and identify wetland areas with basic DEM data, while for land cover analysis using Tasseled Cap Transformation (TCT) method. The result of TWI modeling yields information about potential land of flood. Overlay TWI map with land cover map that produces information that in Karawang regency the most vulnerable areas occur flooding in rice fields. The spatial accuracy of the flood hazard area in this study was 87%.

This paper suggests a method of calculating the elevation and the volume change of the terrain based on the multitemporal digital elevation model (DEM) data sets for the same area. Two methods for calculating the surface change are introduced: One is based on the regular square grids (RSG), another uses the triangulated irregular network (TIN) generalized from the original source data by the 3D Douglas-Peucker algorithm so that not only the accuracy of generalization using 3D Douglas-Peucker is verified, but also the kinds of data formats of DEM for this purpose have been expanded. Finally, the formulae used by these two methods are introduced, and the experimental results calculated from the same original DEM data acquired in 1971 and 2000 respectively form the area of Bayanbulak in Xinjiang are compared. The experiments have shown that the results of the two methods are relatively identical even if under the great generalization degree of DEM for the second method. Therefore, it shows that the second method can greatly heighten the efficiency of the calculation while insuring its accuracy.

Sea turtles exhibit high migratory rates and occupy a broad range of habitats, which in turn makes monitoring these taxa challenging. Applying deep learning (DL) models to vast image datasets collected from citizen science programs can offer promising solutions to overcome the challenge of monitoring the wide habitats of wildlife, particularly sea turtles. Among DL models, object detection models, such as the You Only Look Once (YOLO) series, have been extensively employed for wildlife classification. Despite their successful application in this domain, detecting objects in images with complex backgrounds, including underwater environments, remains a significant challenge. Recently, instance segmentation models have been developed to address this issue by providing more accurate classification of complex images compared to traditional object detection models. This study compared the performance of two state-of-the-art DL methods namely; the object detection model (YOLOv5) and instance segmentation model (YOLOv5-seg), to detect and classify sea turtles. The images were collected from iNaturalist and Google and then divided into 64% for training, 16% for validation, and 20% for test sets. Model performance during and after finishing training was evaluated by loss functions and various indexes, respectively. Based on loss functions, YOLOv5-seg demonstrated a lower error rate in detecting rather than classifying sea turtles than the YOLOv5. According to mean Average Precision (mAP) values, which reflect precision and recall, the YOLOv5-seg model showed superior performance than YOLOv5. The mAP0.5 and mAP0.5:0.95 for the YOLOv5 model were 0.885 and 0.795, respectively, whereas for the YOLOv5-seg, these values were 0.918 and 0.831, respectively. In particular, based on the loss functions and classification results, the YOLOv5-seg showed improved performance for detecting rather than classifying sea turtles compared to the YOLOv5. The results of this study may help improve sea turtle monitoring in the future.

Safety is the eternal theme of power systems. In view of problems such as time-consuming and poor real-time performance in the correct use of seat belt hooks by manual supervision operators in the process of power operation, this paper proposes an improved YOLOv7 seat belt hook suspension state recognition algorithm. Firstly, the feature extraction part of the YOLOv7 backbone network is improved, and the M-Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (M-SPPCSPC) feature extraction module is constructed to replace the Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (SPPCSPC) module of the backbone network, which reduces the amount of computation and improves the detection speed of the backbone network while keeping the sensory field of the backbone network unchanged. Second, a decoupled head, which realizes the confidence and regression frames separately, is introduced to alleviate the negative impact of the conflict between the classification and regression tasks, consequently improving the network detection accuracy and accelerating the network convergence. Ultimately, a dynamic non-monotonic focusing mechanism is introduced in the output layer, and the Wise Intersection over Union (WioU) loss function is used to reduce the competitiveness of high-quality anchor frames while reducing the harmful gradient generated by low-quality anchor frames, which ultimately improves the overall performance of the detection network. The experimental results show that the mean Average Precision (mAP@0.5) value of the improved network reaches 81.2%, which is 7.4% higher than that of the original YOLOv7, therefore achieving better detection results for multiple-state recognition of hooks.

The availability of watershed delineation that has been generated from DEM data is difficult to obtain and the accessibility of DEM data which are unrestricted and precise are hard to obtain in Malaysia. The aim of this study is to examine the accuracy of watershed delineation between Digital Elevation Model (DEM) from ASTER and SRTM in Johor State. In this study, free online data sources from USGS website are used to delineate watershed from ASTER and SRTM satellite imageries. The hydrological modelling tool namely ArcSWAT is utilized to delineate watersheds for both DEM datasets. Both DEM data that had been mosaiced using ERDAS imagine and their DEM is generated using ArcGIS. Watershed boundary for the whole Johor State is then being delineated by using ArcSWAT. ASTER and SRTM accuracies were verified using correlation analysis and mean center distance with data from Department of Irrigation and Drainage (DID). Study indicated that DID watershed area is correlated to ASTER and SRTM at 67.60% and 67.85%. In addition, total mean center distance for ASTER and SRTM are 148.485 and 200.200 where it shows the total mean center distance of ASTER is almost close with DID. The results from this study have successfully indicated that both ASTER and SRTM DEMs are suitable for watershed delineation for Johor State at free and reliable source.

Since hurricane Katrina, developer and planners are looking at a tools and sustainable ways to minimize vulnerabilities in events of disasters especially along the coast. In this setting, remote sensing and Geographic information systems (GIS) have the capacity to provide valuable and timely information about coastal resources, and form an important basis for sustainable planning for land management and decision making. This paper focuses on the design of appropriate coastal databases in six counties in Southern Mississippi using remote sensing and GIS technology. The intent is to enable planners and policy-makers contribute to improved land administration and coastal resources management in order to enhance their competence in decision-making. In order to achieve these objectives, satellite image and digital elevation models (DEMs) data were downloaded from the United States Geological Survey (USGS) seamless data warehouse National Elevation Dataset (NED). From there, the DEM data was co-registered to the satellite image to facilitate draping of the image over the DEM to create terrain models. Result reveals greater part of the three counties along the coast lies less than 10 meters above mean sea level with exposure to coastal disaster vulnerability. In the context of the objectives of the research anchored on the applications of GIS and remote sensing towards efficient land administration and coastal resource management in six coastal counties in an ecologically fragile area already dubbed the epicenter of coastal disasters. Considering the changes that took place in the six counties after Hurricane Katrina debacle, the findings in this study not only stand out, but they remain highly beneficial to decision makers made up of planners and policy makers in several ways. Just as the study injected elements of novelty by identifying digital elevation model information for the six counties in low lying areas, revealing the steeper areas in the north portion of the study stands out as a major finding and study contribution in a way beneficial to decision makers in the region. With that they are now better informed in sharing and cautioning and pinpointing to the public the hidden critical pathways to coastal vulnerability that were previously unknown to ordinary people.

Subgrid variability of solar downward radiation at the surface can be important in estimating subgrid variability of other radiation‐driven variables, such as snowmelt and soil temperature. However, this information is ignored in current hydrological and weather prediction models as only the mean downward solar radiation of model grid is used. In this study, a parameterization for estimating subgrid variability of downward solar radiation from the model grid mean using high‐resolution digital elevation model (DEM) data is proposed. This scheme considers aspect and slope effects on the subgrid variability. The advantage of this scheme is that computations are performed at the same resolution as the considered hydrological or weather prediction model, and subgrid topographic properties derived from high‐resolution DEM data are used as static inputs. This proposed scheme has been verified in mountainous and flat areas, respectively. It is found that the scheme can well estimate the subgrid variability of downward solar radiation. Also, effects of the DEM resolution on the calculated subgrid variability and the spatial correlation of downward solar radiation are studied. Results show that modeled subgrid variability highly depends on the resolution of the DEM, while the spatial correlation is negligibly time dependent. The proposed scheme can be used in any hydrological and weather prediction model to estimate subgrid variability of downward solar radiation. For example, it is planned to be tested in future NOAA regional and global weather models to account for the effects of the subgrid variability of downward solar radiation on the snow model of the land‐surface component.

Using low resolution digital elevation model (DEM) data, reconstruction of cartographic contours is focused on. With a small number of control points, we aim to draw a smooth curve as well as to compute the area and perimeter of a closed contour accurately. The input data forms an oriented contour, each control point of which consists of three values: x, y, and tangent angle. An interpolation method based on arc spline is developed. The method can reproduce G1 continuous curves,in which the exact area and perimeter can be calculated. In a numerical experiment for analytically defined curves, accurate computation of the area and perimeter was achieved with a smaller number of control points. In another experiment using a DEM data, the reconstructed contours were smoother than those by a conventional method.

The drainage pattern of a watershed is an important parameter in nonpoint-source water quality modeling. Manual preparation of this pattern from topographic maps is time–consuming and sometimes subjective. Digital Elevation Model (DEM) data are fundamental cartographic data stored in a uniform grid system which can be easily processed by the computer. Using DEM data and a set of pre-defined rules, the drainage pattern can be automatically determined. Six grid-based methods have been developed, and three of them have been tested for a case study, an area enclosing a subwatershed of Chin-Mei River, Taipei County, Taiwan, R. O. C. The results are comparable to the manually prepared drainage pattern, although several complexities exist for areas such as depression and highly irregular areas. Several refined rules based on the drainage patterns in adjacent areas are utilized to modify the drainage patterns in these areas. Compared with the conventional manual method, the proposed automated methods are much more efficient for regional water quality studies.

Employing K-means clustering to reconstruct missing water surface elevations in LiDAR digital elevation models for hydrodynamic simulation.

Landsat 8, Sentinel 2, Aster, RapidEye and PlanetScope data and Sentinel 1- and Advanced Land Observing Satellite (ALOS)-Phased Array type L-band Synthetic Aperture Radar (PALSAR)-radar images have been evaluated for a karst feature inventory in the Black Hills area in Wyoming/South Dakota, USA. The GeoInformation System (GIS) integrated evaluation of the different satellite data included as well World Imagery files of ESRI and Bing Maps high resolution satellite data of Microsoft. The satellite data revealed several types of circular features related to karst such as enclosed depressions and collapsed dolines as well as traces of tectonic/structural features (visualized by lineament analysis) cutting through youngest sediments, influencing karstification processes. The origin of the circular features is complex and partly unknown, needing further investigations. Digital Elevation Model (DEM) data, such as Aster- and Shuttle Radar Topography Mission (SRTM) DEM data with 30 m and ALOS PASAR DEM with 12.5 m spatial resolution contributed to the detection of depressions, partly related to karst phenomena (sinkholes). Time series of satellite data reveal seasonal changes of the landscape and provide a data base for the documentation of the impact of climate change.

Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data

Edge-protected IDW-based DEM detail enhancement and 3D terrain visualization

Artefact detection in global digital elevation models (DEMs): The Maximum Slope Approach and its application for complete screening of the SRTM v4.1 and MERIT DEMs