Digital Surface Map Research Articles

Global Localization using OpenStreetMap and Elevation Offsets

Localization is a critical component in autonomous vehicle navigation stacks. While GNSS-only localization cannot be fully reliable and available all the time, localization based on 3D high-definition (HD) maps have to be robust to world changes, which is still a challenging issue. Added to that, in general, HD maps are expensive and difficult to construct and maintain.In this paper, we propose a particle filter-based 2D global pose estimation method that can use the crowdsourced OpenStreetMap (OSM) API, a digital surface map, or both. The main contributions of the proposed approach are: that it is lightweight, does not require the vehicle to map the environment, does not require a GPU (can be used with low-power computing resources), is agnostic to the odometry source, and achieved relatively low position and orientation errors for this localization modality using the KITTI dataset sequences. The proposed method's implementation is open source and is available with the experimental results on our GitHub page.

The use of unmanned aerial vehicle (UAV) data in village development plans: A case study of Aksaray Yaylak Village

With the increasing population, the problem of people's need for shelter has also emerged. In many settlements, structures were built without the necessary planning. Three-dimensional (3D) models of these lands should be created in order to make land management plans. Technological improvements in remote sensing have made easier the terrain modelling. To make a better land management plan of rural areas, we need high resolution maps. Unmanned aerial vehicle (UAV) is one of the methods in map production, which can obtain spatial data with high accuracy. In this study, we created 3D model of Yaylak village located in Aksaray District using UAV Photogrammetry. We created high resolution Digital Surface Map and orthophoto map of the village using the pictures obtained from e-Bee. Using these maps, the administrator of the region will decide healthy decision. This study shows us UAV can be used in preparing the necessary maps for designing the settlement area.

Modelling Ozancık village (Aksaray) in computer environment using UAV photogrammetry

Modelling the terrain with high accuracy is vital to create modern settlement areas, and to find solutions that threaten the people. Traditional land surveying is a time-consuming and expensive method. Unmanned aerial vehicle (UAV) is a powered aerial vehicle without a human operator. UAV related case study publications have been increased since last two decades. Detailed three-dimensional (3D) information on the land surface can be obtained from high spatial resolution point clouds. Modelling the terrain requires up-to date information. UAV can provide high-resolution images with low cost in the order of centimeters. We can reconstruct the terrain in a 3D representation using photogrammetry. In this study, we model a village located in Aksaray city using UAV photogrammetry. We created the orthomosaic and Digital Surface Map (DSM) of the region to characterize the terrain in detail. The obtained model and maps will help us to generate modern living space.

Digital Earth surface maps for radar ground clutter simulation

This paper dwells upon optimizing the azimuth sampling interval of digital surface maps used to model radar ground clutter. The resulting equations can be used to find the digital map sampling interval for the required calculation error and modeled power of the simulated signal, which determines the resulting distribution of backscatter intensity. The paper further showcases how the sampling interval could be increased by preprocessing the map.

Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV

Methods for producing high-resolution digital topographic maps using an unmanned aerial vehicle (UAV), and 3D fluid dynamics simulation to estimate the flooded areas caused by a collapsed reservoir, were proposed in this paper. The UAV flight path for photographing damaged areas was divided into two sections considering the drone flight time and overlapping range of the images in the x- and y-directions. The metadata taken by the drone were transferred into world coordinates by tracking the key features of the photographs of nearby areas using a 3D rotation matrix. The point cloud data with a 3D space were extracted from the registered images, and a digital surface map (DSM) was produced using a point cloud classification geometric mapping technique. To amend the serious elevation errors caused by natural or artificial obstacles, a kriging interpolation method was used to reproduce the DSM. A transient computational simulation that considers both the complex geometric topology and hydrodynamic energy of flowing water was conducted using FLOW-3D software to deal with an renormalization group (RNG) turbulence model. The flooded areas calculated through visual reading using images taken by the UAV were compared with the 3D simulation results for verification. The flooded areas estimated through the simulation were approximately 18.3% larger than those found by visual reading. Turbulent flows were mainly observed in obstacles or curved areas of the stream, and the differences in the water depth could be further increased. However, the villagers confirmed that the flooded areas were much greater than what was seen through the visual reading. Therefore, the combination of UAV surveying and the 3D simulation method based on the RNG turbulence model is recommended to accurately estimate flooded areas, and it will support an administrative policy aimed at minimizing the economic costs of damage caused by future reservoir collapses. Keywords: reservoir failure, flooded area, finite volume method, unmanned aerial vehicle, photogrammetric survey, digital surface model DOI: 10.25165/j.ijabe.20201306.4909 Citation: Jeon J, Choi W. Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV. Int J Agric & Biol Eng, 2020; 13(6): 7–15.

Evaluating the attributes of remote sensing image pixels for fast k-meansclustering

Clustering process is an important stage for many data mining applications. In this process, data elements are grouped according to their similarities. One of the most known clustering algorithms is the k-means algorithm. The algorithm initially requires the number of clusters as a parameter and runs iteratively. Many remote sensing image processing applications usually need the clustering stage like many image processing applications. Remote sensing images provide more information about the environments with the development of the multispectral sensor and laser technologies. In the dataset used in this paper, the infrared (IR) and the digital surface maps (DSM) are also supplied besides the red (R), the green (G), and the blue (B) color values of the pixels. However, remote sensing images come with very large sizes (6000 $\times$ 6000 pixels for each image in the dataset used). Clustering these large-size images using their multiattributes consumes too much time if it is used directly. In the literature, some studies are available to accelerate the k-means algorithm. One of them is the normalized distance value (NDV)-based fast k-means algorithm that benefits from the speed of the histogram-based approach and uses the multiattributes of the pixels. In this paper, we evaluated the effects of these attributes on the correctness of the clustering process with different color space transformations and distance measurements. We give the success results as peak signal-to-noise ratio and structural similarity index values using two different types of reference data (the source images and the ground-truth images) separately. Finally, we give the results based on accuracy measurement for evaluating both the success of the clustering outputs and the reliability of the NDV-based measurement methods presented in this paper.

Shoreline Movement Monitoring and Geomorphologic Changes of Beaches Using Lidar and UAVs Images on the Coast of the East Sea, Korea

Lee, Y.K.; Eom, J.; Do, J.D.; Kim, B.J., and Ryu, J.H., 2019. Shoreline movement monitoring and geomorphologic changes of beaches using lidar UAVs images on the coast of the East Sea, Korea. In: Jung, H.S.; Lee, S.; Ryu, J.H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 409-414. Coconut Creek (Florida), ISSN 0749-0208.Sandy beaches are important habitats for coastal organisms and act as buffer zones during coastal disasters. Most of the coastal zone along the eastern coast of Korea consists of sandy beaches. However, beach erosion has been accelerating in recent years. In this study, shoreline movement and topographical changes were analyzed in Uljin-gun on the East Sea coast of Korea using remotely sensed data from airborne Lidar and unmanned aerial vehicles (UAVs). The shoreline changes extracted were statistically quantified using net shoreline movement (NSM) and linear regression rate (LRR) in the digital shoreline analysis system (DSAS). Morphological changes were quantified based on a comparison of the digital surface maps (DSM) generated by Lidar and UAVs. Shoreline movement and morphological changes were analyzed over the short-term (February 2016 to February 2019) and long-term (June 2008 to June 2018). Seaward migration was dominant for 7.02 m, 1.64 m, and 9.22 m along three defined subareas over the long-term. Over the short-term, LRR results showed landward migration as 0.7 m and 1.10 m in two of the subareas. Morphological change showed erosion and accretion occurring at approximately 0.08 m3 and 0.42 m3, respectively. This reverse trend indicates that a detailed DSM can detect volumetric change due to relocation of the sediment around artificial construction.

Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV

Monitoring the development of vegetation height through time provides a key indicator of crop health and overall condition. Traditional manual approaches for monitoring crop height are generally time consuming, labor intensive and impractical for large-scale operations. Dynamic crop heights collected through the season allow for the identification of within-field problems at critical stages of the growth cycle, providing a mechanism for remedial action to be taken against end of season yield losses. With advances in unmanned aerial vehicle (UAV) technologies, routine monitoring of height is now feasible at any time throughout the growth cycle. To demonstrate this capability, five digital surface maps (DSM) were reconstructed from high-resolution RGB imagery collected over a field of maize during the course of a single growing season. The UAV retrievals were compared against LiDAR scans for the purpose of evaluating the derived point clouds capacity to capture ground surface variability and spatially variable crop height. A strong correlation was observed between structure-from-motion (SfM) derived heights and pixel-to-pixel comparison against LiDAR scan data for the intra-season bare-ground surface (R2 = 0.77 − 0.99, rRMSE = 0.44% − 0.85%), while there was reasonable agreement between canopy comparisons (R2 = 0.57 − 0.65, rRMSE = 37% − 50%). To examine the effect of resolution on retrieval accuracy and processing time, an evaluation of several ground sampling distances (GSD) was also performed. Our results indicate that a 10 cm resolution retrieval delivers a reliable product that provides a compromise between computational cost and spatial fidelity. Overall, UAV retrievals were able to accurately reproduce the observed spatial variability of crop heights within the maize field through the growing season and provide a valuable source of information with which to inform precision agricultural management in an operational context.

Growing fresh fruits and vegetables in an urban landscape: A geospatial assessment of ground level and rooftop urban agriculture potential in Boston, USA

Urban parcels can potentially be leveraged for developing a local urban food system by growing high yield food crops. Here, a remote sensing and GIS-based modeling framework was developed to locate and quantify available area for urban farming, including both rooftop and ground level areas in the city of Boston, MA, USA. Geoprocessing and spatial analysis tools were used to process geographic data layers for zoning, ownership, slope, soil quality, and adequate light availability. Surface slope (roof pitch) was determined for all buildings in the city through the creation of a digital surface map from remotely sensed LiDAR data. Potential parcels from ground level public and private vacant lots and underutilized residential and commercial areas were mapped using publicly available datasets. Approximately 922ha of rooftop and 1,250ha of ground level parcels have been identified, representing 7.4% and 10% of the total land area in Boston, respectively. Finally, food yield values for common urban agricultural crops were used to estimate the city’s food production potential from the identified parcels. Despite Boston’s density, the mapped areas have potential to produce enough fresh fruits and vegetables for Boston’s population, while providing both environmental and economic co-benefits. The study outcome was compared with mapping and inventory results from other North American cities.

DIRECT PHOTOGRAMMETRY USING UAV: TESTS AND FIRST RESULTS

Abstract. In recent years, the quality of high resolution acquisition platforms for geomatic applications has decidedly increased, especially in the performance of the sensors devoted to image acquisition. The small size of these new sensors combined with the increase in resolution allows them to be easily mounted onto Unmanned Aerial Vehicles (UAVs); in particular, calibrated, very-high-resolution digital cameras for photogrammetric purposes such as digital terrain model (DTM) and digital surface model (DSM) extraction, orthophotos, and map realization have been used. In this case, the UAV performance allows a high quality product to be obtained, considering the pixel size and the accuracy of the DTM/DSM which could be obtained with automatic procedures. Several navigation sensors (GPS/GNSS and IMU-MEMS) are embedded into UAVs in order to realize autonomous flight. The quality of these sensors, in terms of accuracy, depends on the model of UAV and its purpose. The navigation solution (position and attitude) is estimated by the internal UAV sensor and can be employed to directly georeference the images, in order to produce an quick and easy description and analysis of the overlooked area. This is a good condition for semi-automatic procedures using a bundle-block photogrammetric approach. But is it possible to realize a direct photogrammetry? And what are the limits? Several tests were carried out over different areas and in different conditions using three different UAVs belonging to the Geomatics group of the Politecnico di Torino. In this paper, an investigation of the limits of some commercial UAVs is reported, defining a dedicated procedure to valuate their performance, especially considering the use of UAVs for direct photogrammetry.

3차원 거리정보와 DSM의 정사윤곽선 영상 정합을 이용한 무인이동로봇의 위치인식

본 논문에서는 야지 환경에서 동작하는 무인이동로봇에서 획득한 3차원 LIDAR (Light Detection and Ranging) 센서 정보와 로봇이 이동하는 지형의 3차원 DSM (Digital Surface Map)에서 정사윤곽선(Ortho-edge) 특징영상을 생성하고 정합하여 로봇의 현재 위치를 추정하는 기술을 제안한다. 최근의 무인이동로봇의 위치 인식에 대한연구는 GPS (Global Positioning System), IMU (Inertial Measurement Unit), LIDAR 등의 위치인식 센서를 융합하는 경우가 많아지고 있다. 특히 LIDAR에서 획득한 거리정보를 ICP(Iterative Closest Point) 기반의 기하정합으로 로봇의 위치를 추정하는 기술이 개발되고 있다. 그러나 이동로봇에서 획득한 센서 정보는 DSM의 센싱 방향과 큰 차이차이가 있어 기존의 기하정합 기술을 사용하는데 어려움이 있다. 본 논문에서는 서로 다른 센싱 방향에서 획득한 3차원 LIDAR 거리정보와 DSM에서 정사윤곽선이라는 특징 영상을 생성하고 이들을 정합하여 로봇의 위치를 추정하는 새로운 기술을 제안한다. DSM으로부터 현재 시점의 정사윤곽선 영상을 생성하는 방법, 전방향 LIDAR 거리센서에서 정사윤곽선 영상을 생성하는 방법, 그리고 정사윤곽선 영상의 정합 기술을 설명하였다. 실험에서는 다양한 주행 경로에 대한 위치 추정의 오차를 분석하고 제안 기술의 성능의 우수성을 보였다. This paper presents a new localization technique of an UGV(Unmanned Ground Vehicle) by matching ortho-edge images generated from a DSM (Digital Surface Map) which represents the 3D geometric information of an outdoor navigation environment and 3D range data which is obtained from a LIDAR (Light Detection and Ranging) sensor mounted at the UGV. Recent UGV localization techniques mostly try to combine positioning sensors such as GPS (Global Positioning System), IMU (Inertial Measurement Unit), and LIDAR. Especially, ICP (Iterative Closest Point)-based geometric registration techniques have been developed for UGV localization. However, the ICP-based geometric registration techniques are subject to fail to register 3D range data between LIDAR and DSM because the sensing directions of the two data are too different. In this paper, we introduce and match ortho-edge images between two different sensor data, 3D LIDAR and DSM, for the localization of the UGV. Details of new techniques to generating and matching ortho-edge images between LIDAR and DSM are presented which are followed by experimental results from four different navigation paths. The performance of the proposed technique is compared to a conventional ICP-based technique.

Acute Detection of ST-Elevation Myocardial Infarction Missed on Standard 12-Lead ECG With a Novel 80-Lead Real-Time Digital Body Surface Map: Primary Results From the Multicenter OCCULT MI Trial

Although 80-lead ECG body surface mapping is more sensitive for ST-elevation myocardial infarction (STEMI) than the 12-lead ECG, its clinical utility in chest pain in the emergency department (ED) has not been studied. We sought to determine the prevalence, clinical care patterns, and clinical outcomes of patients with STEMI identified on 80-lead but not on 12-lead (80-lead-only STEMI). The Optimal Cardiovascular Diagnostic Evaluation Enabling Faster Treatment of Myocardial Infarction trial was a multicenter prospective observational study of moderate- to high-risk chest pain patients presenting to the ED. Patients received simultaneous 12-lead and 80-lead ECGs as part of their initial evaluation and were treated according to the standard of care, with clinicians blinded to the 80-lead results. The primary outcome of the trial was door-to-sheath time in patients with 80-lead-only STEMI versus patients with STEMI identified by 12-lead alone (12-lead STEMI). Secondary outcomes included angiographic and clinical outcomes at 30 days. One thousand eight hundred thirty patients were evaluated, 91 had a discharge diagnosis of 12-lead STEMI, and 25 patients met criteria for 80-lead-only STEMI. Eighty-four of the 91 12-lead STEMI patients underwent cardiac catheterization, with a median door-to-sheath time of 54 minutes, versus 14 of the 25 80-lead-only STEMI patients, with a door-to-sheath time of 1,002 minutes (estimated treatment difference in median=881; 95% confidence interval 181 to 1,079 minutes). Clinical outcomes and revascularization rates, however, were similar between 80-lead-only STEMI and 12-lead STEMI patients. The 80-lead ECG provides an incremental 27.5% increase in STEMI detection versus the 12-lead. Patients with 80-lead-only STEMI have adverse outcomes similar to those of 12-lead STEMI patients but are treated with delayed or conservative invasive strategies.

DSM과 다시점 거리영상의 3차원 등록을 이용한 무인이동차량의 위치 추정: 가상환경에서의 적용

A computer vision technique of estimating the location of an unmanned ground vehicle is proposed. Identifying the location of the unmaned vehicle is very important task for automatic navigation of the vehicle. Conventional positioning sensors may fail to work properly in some real situations due to internal and external interferences. Given a DSM(Digital Surface Map), location of the vehicle can be estimated by the registration of the DSM and multiview range images obtained at the vehicle. Registration of the DSM and range images yields the 3D transformation from the coordinates of the range sensor to the reference coordinates of the DSM. To estimate the vehicle position, we first register a range image to the DSM coarsely and then refine the result. For coarse registration, we employ a fast random sample matching method. After the initial position is estimated and refined, all subsequent range images are registered by applying a pair-wise registration technique between range images. To reduce the accumulation error of pair-wise registration, we periodically refine the registration between range images and the DSM. Virtual environment is established to perform several experiments using a virtual vehicle. Range images are created based on the DSM by modeling a real 3D sensor. The vehicle moves along three different path while acquiring range images. Experimental results show that registration error is about under 1.3m in average.

Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets

A study was conducted to determine the feasibility of obtaining estimates of vegetation canopy height from digital elevation data collected during the 2000 Shuttle Radar Topography Mission (SRTM). The SRTM sensor mapped 80% of the Earth's land mass with a C-band Interferometric Synthetic Aperture Radar (InSAR) instrument, producing the most complete digital surface map of Earth. Due to the relatively short wavelength (5.6 cm) of the SRTM instrument, the majority of incoming electromagnetic energy is reflected by scatterers located within the vegetation canopy at heights well above the “bald-Earth” surface. Interferometric SAR theory provides a basis for properly identifying and accounting for the dependence of this scattering phase center height on both instrument and target characteristics, including relative and absolute vertical error and vegetation structural attributes. An investigation to quantify the magnitude of the vertical error component was conducted using SRTM data from two vegetation-free areas in Iowa and North Dakota, revealing absolute errors of −4.0 and −1.1 m, respectively. It was also shown that the relative vertical error due to phase noise can be reduced significantly through sample averaging. The relative error range for the Iowa site was reduced from 13 to 4 m and for the North Dakota site from 7 to 3 m after averaging of 50 samples. Following error reduction, it was demonstrated that the SRTM elevation data can be successfully correlated via linear regression models with ground-measured canopy heights acquired during the general mission timeframe from test sites located in Georgia and California. Prior to outlier removal and phase noise reduction, initial adjusted r 2 values for the Georgia and California sites were 0.15 and 0.20, respectively. Following outlier analysis and averaging of at least 20 SRTM pixels per observation, adjusted r 2 values for the Georgia and California sites improved to 0.79 (rmse=1.1 m) and 0.75 (rmse=4.5 m), respectively. An independent validation of a novel bin-based modeling strategy designed for reducing phase noise in sample plot data confirmed both the robustness of the California model (adjusted r 2=0.74) as well as the capacity of the binning strategy to produce stable models suitable for inversion (validated rmse=4.1 m). The results suggest that a minimum mapping unit of approximately 1.8 ha is appropriate for SRTM-based vegetation canopy height mapping.