Gullies naturally exhibit complex morphologies and hence are difficult to measure. Photogrammetric methods are promising tools to overcome such problems due to 3D reconstruction from overlapping ...images without disturbing the surface. Airborne (e.g. unmanned aerial vehicles, UAV) and terrestrial image acquisition platforms are possible data sources for comprehensive digital gully modelling. In this study, an approach for the synergetic utilisation of UAV and terrestrial images for high resolution 3D model generation is introduced. While proper UAV mission planning is necessary, terrestrial images can be acquired spontaneously and flexibly depending on the encountered terrain conditions to supplement UAV data. Without confining accuracy demands, terrestrial images are processed solely with additional information from already existent 3D models and orthophotos from UAV image processing. During this field campaign UAVs are suitable for flexible and frequent gully monitoring. However, with the case specific flight configuration, 3D modelling is restricted at steep gully walls – above slope gradients of 50–60° – as well as overhanging gully walls, due to the limiting birds-eye view. Additional terrestrial images are used to overcome these limitations and fill up data gaps. In this study the final merged digital gully model reveals a resolution about 0.5cm and an accuracy of 1cm. A gully headcut in Andalusia (Spain) is analysed to confirm the suitability of the synergetic data utilisation. A study period of three months during winter season is investigated to measure multi-temporal 3D volume changes. The positive volume balance amounts to 5m3 for a gully area of 170m2 and is caused mainly by anthropogenic interactions. Concluding, the combination of UAV and terrestrial data allow for comprehensive gully models with high spatial resolution at frequent intervals.
•Methodological case study to assess gully headcut changes in Andalusia•Integration of terrestrial images can improve 3D gully model of UAV images.•Fused high resolution gully model enables analysis of 3D volume changes.
Photogrammetry and geosciences have been closely linked since the late 19th century due to the acquisition of high-quality 3-D data sets of the environment, but it has so far been restricted to a ...limited range of remote sensing specialists because of the considerable cost of metric systems for the acquisition and treatment of airborne imagery. Today, a wide range of commercial and open-source software tools enable the generation of 3-D and 4-D models of complex geomorphological features by geoscientists and other non-experts users. In addition, very recent rapid developments in unmanned aerial vehicle (UAV) technology allow for the flexible generation of high-quality aerial surveying and ortho-photography at a relatively low cost.The increasing computing capabilities during the last decade, together with the development of high-performance digital sensors and the important software innovations developed by computer-based vision and visual perception research fields, have extended the rigorous processing of stereoscopic image data to a 3-D point cloud generation from a series of non-calibrated images. Structure-from-motion (SfM) workflows are based upon algorithms for efficient and automatic orientation of large image sets without further data acquisition information, examples including robust feature detectors like the scale-invariant feature transform for 2-D imagery. Nevertheless, the importance of carrying out well-established fieldwork strategies, using proper camera settings, ground control points and ground truth for understanding the different sources of errors, still needs to be adapted in the common scientific practice.This review intends not only to summarise the current state of the art on using SfM workflows in geomorphometry but also to give an overview of terms and fields of application. Furthermore, this article aims to quantify already achieved accuracies and used scales, using different strategies in order to evaluate possible stagnations of current developments and to identify key future challenges. It is our belief that some lessons learned from former articles, scientific reports and book chapters concerning the identification of common errors or "bad practices" and some other valuable information may help in guiding the future use of SfM photogrammetry in geosciences.
Photogrammetric models have become a standard tool for the study of surfaces, structures and natural elements. As an alternative to Light Detection and Ranging (LiDAR), photogrammetry allows 3D point ...clouds to be obtained at a much lower cost. This paper presents an enhanced workflow for image-based 3D reconstruction of high-resolution models designed to work with fixed time-lapse camera systems, based on multi-epoch multi-images (MEMI) to exploit redundancy. This workflow is part of a fully automatic working setup that includes all steps: from capturing the images to obtaining clusters from change detection. The workflow is capable of obtaining photogrammetric models with a higher quality than the classic Structure from Motion (SfM) time-lapse photogrammetry workflow. The MEMI workflow reduced the error up to a factor of 2 when compared to the previous approach, allowing for M3C2 standard deviation of 1.5 cm. In terms of absolute accuracy, using LiDAR data as a reference, our proposed method is 20% more accurate than models obtained with the classic workflow. The automation of the method as well as the improvement of the quality of the 3D reconstructed models enables accurate 4D photogrammetric analysis in near-real time.
To obtain reliable water segmentations from image data for real-time monitoring of river water levels, a comparison of 32 convolutional neural networks was performed. They were trained on a new river ...water segmentation dataset consisting of 1128 images. To prevent overfitting, two methods using offline and online augmentation were developed to improve the variance. It was found that offline augmentation is superior on fewer data, while online augmentation is advantageous for a larger dataset (such as Cityscapes).
The network comparison showed that U-Net performs best on the water segmentation dataset when using an ResNeXt 50 encoding network pre-trained on ImageNet. It achieves an intersection over union (IoU) of 0.91 without augmentation, 0.98 with offline augmentation and 0.93 with the online augmentation method. The authors have applied the algorithms for online and offline augmentation to Cityscapes to verify the applicability of the strategies to other datasets. The mean IoU is 0.86 without augmentation, 0.86 with offline augmentation and 0.87 with online augmentation. Only online augmentation could prevent overfitting on Cityscapes.
•Automatic river water segmentation.•A new dataset for river water segmentation using CNNs.•Improvement of CNN generalizability (segmentation) using augmentation.•Comparison of 32 segmentation CNNs.•A new, easy to use software for CNN training (segmentation).
Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the ...comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as
Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.
Knowledge about the interior and exterior camera orientation parameters is required to establish the relationship between 2D image content and 3D object data. Camera calibration is used to determine ...the interior orientation parameters, which are valid as long as the camera remains stable. However, information about the temporal stability of low-cost cameras due to the physical impact of temperature changes, such as those in smartphones, is still missing. This study investigates on the one hand the influence of heat dissipating smartphone components at the geometric integrity of implemented cameras and on the other hand the impact of ambient temperature changes at the geometry of uncoupled low-cost cameras considering a Raspberry Pi camera module that is exposed to controlled thermal radiation changes. If these impacts are neglected, transferring image measurements into object space will lead to wrong measurements due to high correlations between temperature and camera's geometric stability. Monte-Carlo simulation is used to simulate temperature-related variations of the interior orientation parameters to assess the extent of potential errors in the 3D data ranging from a few millimetres up to five centimetres on a target in X- and Y- direction. The target is positioned at a distance of 10 m to the camera and the Z-axis is aligned with camera's depth direction.
•UAV-SfM soil loss measurements were validated by direct sediment collection.•Photogrammetric precision estimates defined spatially variable levels of detection.•DEMs of difference illustrated the ...diffuse erosion patterns through time.•Accurate 3-D soil surface models were constructed from UAV-SfM data.
Sheet erosion is common on agricultural lands, and understanding the dynamics of the erosive process as well as the quantification of soil loss is important for both soil scientists and managers. However, measuring rates of soil loss from sheet erosion has proved difficult due to requiring the detection of relatively small surface changes over extended areas. Consequently, such measurements have relied on the use of erosion plots, which have limited spatial coverage and have high operating costs. For measuring the larger erosion rates characteristic of rill and gully erosion, structure-from-motion (SfM) photogrammetry has been demonstrated to be a valuable tool. Here, we demonstrate the first direct validation of UAV-SfM measurements of sheet erosion using sediment collection data collected from erosion plots.
Three erosion plots (12 m × 4 m) located at Lavras, Brazil, with bare soil exposed to natural rainfall from which event sediment and runoff was monitored, were mapped during two hydrological years (2016 and 2017), using a UAV equipped with a RGB camera. DEMs of difference (DoD) were calculated to detect spatial changes in the soil surface topography over time and to quantify the volumes of sediments lost or gained. Precision maps were generated to enable precision estimates for both DEMs to be propagated into the DoD as spatially variable vertical uncertainties.
The point clouds generated from SfM gave mean errors of ~2.4 mm horizontally (xy) and ~1.9 mm vertically (z) on control and independent check points, and the level of detection (LoD) along the plots ranged from 1.4 mm to 7.4 mm. The soil loss values obtained by SfM were significantly (p < 0.001) correlated (r2 = 95.55%) with those derived from the sediment collection. These results open up the possibility to use SfM for erosion studies where channelized erosion is not the principal mechanism, offering a cost-effective method for gaining new insights into sheet, and interrill, erosion processes.
Recently developed cameras in the low-cost sector exhibit lens distortion patterns that cannot be handled well with established models of radial lens distortion. This study presents an approach that ...divides the image sensor and distortion modeling into two concentric zones for the application of an extended radial lens distortion model. The mathematical model is explained in detail and it was validated on image data from a DJI Mavic Pro UAV camera. First, the special distortion pattern of the camera was examined by decomposing and analyzing the residuals. Then, a novel bi-radial model was introduced to describe the pattern. Eventually, the new model was integrated in a bundle adjustment software package. Practical tests revealed that the residuals of the bundle adjustment could be reduced by 63% with respect to the standard Brown model. On the basis of external reference measurements, an overall reduction in the residual errors of 40% was shown.
As key-components of the urban-drainage system, storm-drains and manholes are essential to the hydrological modeling of urban basins. Accurately mapping of these objects can help to improve the ...storm-drain systems for the prevention and mitigation of urban floods. Novel Deep Learning (DL) methods have been proposed to aid the mapping of these urban features. The main aim of this paper is to evaluate the state-of-the-art object detection method RetinaNet to identify storm-drain and manhole in urban areas in street-level RGB images. The experimental assessment was performed using 297 mobile mapping images captured in 2019 in the streets in six regions in Campo Grande city, located in Mato Grosso do Sul state, Brazil. Two configurations of training, validation, and test images were considered. ResNet-50 and ResNet-101 were adopted in the experimental assessment as the two distinct feature extractor networks (i.e., backbones) for the RetinaNet method. The results were compared with the Faster R-CNN method. The results showed a higher detection accuracy when using RetinaNet with ResNet-50. In conclusion, the assessed DL method is adequate to detect storm-drain and manhole from mobile mapping RGB images, outperforming the Faster R-CNN method. The labeled dataset used in this study is available for future research.
PDF
