The use of unmanned aerial vehicles (UAVs) for engineering geology applications

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Implementation of Unmanned aerial vehicles (UAVs) for assessment of transportation infrastructure - Phase II

Technological advances in unmanned aerial vehicle (UAV) technologies continue to enable these tools to become easier to use, more economical, and applicable for transportation-related operations, maintenance, and asset management while also increasing safety and decreasing cost. This Phase 2 project continued to test and evaluate five main UAV platforms with a combination of optical, thermal, and lidar sensors to determine how to implement them into MDOT workflows. Field demonstrations were completed at bridges, a construction site, road corridors, and along highways with data being processed and analyzed using customized algorithms and tools. Additionally, a cost-benefit analysis was conducted, comparing manual and UAV-based inspection methods. The project team also gave a series of technical demonstrations and conference presentations, enabling outreach to interested audiences who gained understanding of the potential implementation of this technology and the advanced research that MDOT is moving to implementation. The outreach efforts and research activities performed under this contract demonstrated how implementing UAV technologies into MDOT workflows can provide many benefits to MDOT and the motoring public; such as advantages in improved cost-effectiveness, operational management, and timely maintenance of Michigan’s transportation infrastructure

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Extracting Physical and Environmental Information of Irish Roads Using Airborne and Mobile Sensors

Airborne sensors including LiDAR and digital cameras are now used extensively for capturing topographical information as these are often more economical and efficient as compared to the traditional photogrammetric and land surveying techniques. Data captured using airborne sensors can be used to extract 3D information important for, inter alia, city modelling, land use classification and urban planning. According to the EU noise directive (2002/49/EC), the National Road Authority (NRA) in Ireland is responsible for generating noise models for all roads which are used by more than 8,000 vehicles per day. Accordingly, the NRA has to cover approximately 4,000 km of road, 500m on each side. These noise models have to be updated every 5 years. Important inputs to noise model are digital terrain model (DTM), 3D building data, road width, road centre line, ground surface type and noise barriers. The objective of this research was to extract these objects and topographical information using nationally available datasets acquired from the Ordnance Survey of Ireland (OSI). The OSI uses ALS50-II LiDAR and ADS40 digital sensors for capturing ground information. Both sensors rely on direct georeferencing, minimizing the need for ground control points. Before exploiting the complementary nature of both datasets for information extraction, their planimetric and vertical accuracies were evaluated using independent ground control points. A new method was also developed for registration in case of any mismatch. DSMs from LiDAR and aerial images were used to find common points to determine the parameters of 2D conformal transformation. The developed method was also evaluated by the EuroSDR in a project which involved a number of partners. These measures were taken to ensure that the inputs to the noise model were of acceptable accuracy as recommended in the report (Assessment of Exposure to Noise, 2006) by the European Working Group. A combination of image classification techniques was used to extract information by the fusion of LiDAR and aerial images. The developed method has two phases, viz. object classification and object reconstruction. Buildings and vegetation were classified based on Normalized Difference Vegetation Index (NDVI) and a normalized digital surface model (nDSM). Holes in building segments were filled by object-oriented multiresolution segmentation. Vegetation that remained amongst buildings was classified using cues obtained from LiDAR. The short comings there in were overcome by developing an additional classification cue using multiple returns. The building extents were extracted and assigned a single height value generated from LiDAR nDSM. The extracted height was verified against the ground truth data acquired using terrestrial survey techniques. Vegetation was further classified into three categories, viz. trees, hedges and tree clusters based on shape parameter (for hedges) and distance from neighbouring trees (for clusters). The ground was classified into three surface types i.e. roads and parking area, exposed surface and grass. This was done using LiDAR intensity, NDVI and nDSM. Mobile Laser Scanning (MLS) data was used to extract walls and purpose built noise barriers, since these objects were not extractable from the available airborne sensor data. Principal Component Analysis (PCA) was used to filter points belonging to such objects. A line was then fitted to these points using robust least square fitting. The developed object extraction method was tested objectively in two independent areas namely the Test Area-1 and the Test Area-2. The results were thoroughly investigated by three different accuracy assessment methods using the OSI vector data. The acceptance of any developed method for commercial applications requires completeness and correctness values of 85% and 70% respectively. Accuracy measures obtained using the developed method of object extraction recommend its applicability for noise modellin

Semi-automated geomorphological mapping applied to landslide hazard analysis

Computer-assisted three-dimensional (3D) mapping using stereo and multi-image (“softcopy”) photogrammetry is shown to enhance the visual interpretation of geomorphology in steep terrain with the direct benefit of greater locational accuracy than traditional manual mapping. This would benefit multi-parameter correlations between terrain attributes and landslide distribution in both direct and indirect forms of landslide hazard assessment. Case studies involve synthetic models of a landslide, and field studies of a rock slope and steep undeveloped hillsides with both recently formed and partly degraded, old landslide scars. Diagnostic 3D morphology was generated semi-automatically both using a terrain-following cursor under stereo-viewing and from high resolution digital elevation models created using area-based image correlation, further processed with curvature algorithms. Laboratory-based studies quantify limitations of area-based image correlation for measurement of 3D points on planar surfaces with varying camera orientations. The accuracy of point measurement is shown to be non-linear with limiting conditions created by both narrow and wide camera angles and moderate obliquity of the target plane. Analysis of the results with the planar surface highlighted problems with the controlling parameters of the area-based image correlation process when used for generating DEMs from images obtained with a low-cost digital camera. Although the specific cause of the phase-wrapped image artefacts identified was not found, the procedure would form a suitable method for testing image correlation software, as these artefacts may not be obvious in DEMs of non-planar surfaces.Modelling of synthetic landslides shows that Fast Fourier Transforms are an efficient method for removing noise, as produced by errors in measurement of individual DEM points, enabling diagnostic morphological terrain elements to be extracted. Component landforms within landslides are complex entities and conversion of the automatically-defined morphology into geomorphology was only achieved with manual interpretation; however, this interpretation was facilitated by softcopy-driven stereo viewing of the morphological entities across the hillsides.In the final case study of a large landslide within a man-made slope, landslide displacements were measured using a photogrammetric model consisting of 79 images captured with a helicopter-borne, hand-held, small format digital camera. Displacement vectors and a thematic geomorphological map were superimposed over an animated, 3D photo-textured model to aid non-stereo visualisation and communication of results

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs

UAV monitoring horských rašelinišť

This thesis deals with UAV imagery analysis as used to monitor environmental settings, in this case a particularly sensitive ecosystem of a peatbog. The non-destructive aspect of UAV monitoring based on remote access to the studied area is crucial in this scenario. Introduction to the topic, examples of the employment of UAV technologies and the possibilities of their application in monitoring peatbogs are followed by examples of visual data analysis with the help of various software on the multispectral data acquired at peatbog Rokytka in the Šumava National Park. Key words: UAV, monitoring, peatbogs, remote sensing, multispectral imaging, classificationTato práce se zabývá analýzou UAV snímků využívaných k monitorování prostředí, v tomto případě zvláště citlivého ekosystému rašeliniště. Za těchto okolností je zásadní nedestruktivní aspekt monitorování UAV plynoucí z dálkového průzkumu studované oblasti. Na úvod do problematiky, příklady využití UAV technologií a možnosti jejich aplikace při monitorování rašelinišť navazují příklady vizuální analýzy dat pomocí různých softwarů na multispektrálních datech získaných na rašeliništi na Rokytce v Národním parku Šumava. Klíčová slova: UAV, monitoring, rašeliniště, DPZ, multispektrální snímkování, klasifikaceKatedra fyzické geografie a geoekologieDepartment of Physical Geography and GeoecologyFaculty of SciencePřírodovědecká fakult

Geometric Accuracy Testing, Evaluation and Applicability of Space Imagery to the Small Scale Topographic Mapping of the Sudan

The geometric accuracy, interpretabilty and the applicability of using space imagery for the production of small-scale topographic maps of the Sudan have been assessed. Two test areas have been selected. The first test area was selected in the central Sudan including the area between the Blue Nile and the White Nile and extending to Atbara in the Nile Province. The second test area was selected in the Red Sea Hills area which has modern 1:100,000 scale topographic map coverage and has been covered by six types of images, Landsat MSS TM and RBV; MOMS; Metric Camera (MC); and Large format Camera (LFC). Geometric accuracy testing has been carried out using a test field of well-defined control points whose terrain coordinates have been obtained from the existing maps. The same points were measured on each of the images in a Zeiss Jena Stereocomparator (Stecometer C II) and transformed into the terrain coordinate system using polynomial transformations in the case of the scanner and RBV images; and space resection/intersection, relative/absolute orientation and bundle adjustment in the case of the MC and LFC photographs. The two sets of coordinates were then compared. The planimetric accuracies (root mean square errors) obtained for the scanner and RBV images were: Landsat MSS +/-80 m; TM +/-45 m; REV +/-40 m; and MOMS +/-28 m. The accuracies of the 3-dimensional coordinates obtained from the photographs were: MC:-X=+/-16 m, Y=+/-16 m, Z=+/-30 m; and LFC:- X=+/-14 m, Y=+/-14 m, and Z=+/-20 m. The planimetric accuracy figures are compatible with the specifications for topographic maps at scales of 1:250,000 in the case of MSS; 1:125,000 scale in the case of TM and RBV; and 1:100,000 scale in the case of MOMS. The planimetric accuracies (vector =+/-20 m) achieved with the two space cameras are compatible with topographic mapping at 1:60,000 to 1:70,000 scale. However, the spot height accuracies of +/-20 to +/-30 m - equivalent to a contour interval of 50 to 60 m - fall short of the required heighting accuracies for 1:60,000 to 1:100,000 scale mapping. The interpretation tests carried out on the MSS, TM, and RBV images showed that, while the main terrain features (hills, ridges, wadis, etc.) can be mapped reasonably well, there was an almost complete failure to pick up the cultural features - towns, villages, roads, railways, etc. - present in the test areas. The high resolution MOMS images and the space photographs were much more satisfactory in this respect though still the cultural features are difficult to pick up due to the buildings and roads being built out of local material and exhibiting little contrast on the images