The potential of small unmanned aircraft systems and structure-from-motion for topographic surveys: a test of emerging integrated approaches at Cwm Idwal, North Wales

This paper was accepted for publication in the journal Geomorphology and the definitive published version is available at http://dx.doi.org/10.1016/j.geomorph.2014.07.021Novel topographic survey methods that integrate both structure-from-motion (SfM) photogrammetry and small unmanned aircraft systems (sUAS) are a rapidly evolving investigative technique. Due to the diverse range of survey configurations available and the infancy of these new methods, further research is required. Here, the accuracy, precision and potential applications of this approach are investigated. A total of 543 images of the Cwm Idwal moraine–mound complex were captured from a light (b5 kg) semi-autonomous multi-rotor unmanned aircraft system using a consumer-grade 18 MP compact digital camera. The imageswere used to produce a DSM(digital surfacemodel) of themoraines. The DSMis in good agreement with 7761 total station survey points providing a total verticalRMSE value of 0.517mand verticalRMSE values as lowas 0.200mfor less densely vegetated areas of the DSM. High-precision topographic data can be acquired rapidly using this technique with the resulting DSMs and orthorectified aerial imagery at sub-decimetre resolutions. Positional errors on the total station dataset, vegetation and steep terrain are identified as the causes of vertical disagreement. Whilst this aerial survey approach is advocated for use in a range of geomorphological settings, care must be taken to ensure that adequate ground control is applied to give a high degree of accuracy

Terrain Mapping From Unmanned Aerial Vehicles

In land surveying, digital terrain model (DTM) and digital surface model (DSM) have long been benefitted in many applications related to terrain mapping. Conventional methods of generating DTM and DSM have limitations in terms of practicality, time consumption and costing. The problems are much more serious for tropical regions where clouds are persistence and tend to affect the accuracy of most of these devices. This study aims to propose a novel way of generating DTM and DSM by utilizing unmanned aerial vehicle (UAV) for different land covers including forest, plantation and developed areas in the tropical region of Malaysia. The aerial images obtained from non-matrix digital compact camera payload on UAV were processed photogrammetrically to produce terrain mapping products including DTM, DSM and orthophoto. A detailed survey is also conducted at these areas to produce contour map as benchmark data in which is less being practiced by UAV mappers. To determine the accuracy, quantitative and qualitative analysis were carried out by means of root mean square error (RMSE) and visual inspection. The results show that the RMSE of DTM for forest, plantation and developed area are ± 1.806m, ± 0.938m and ± 0.549m, respectively while for DSM are ± 3.143m, ± 0.637m and ± 0.276m respectively. This study has determined that, the development area gives the highest accuracy compared to the plantation and forested area in which for developed and plantation area the DSM is better than DTM while vice-versa for forested area. It can be concluded that the complexity if terrain is found to be one of the key factors that influences the accuracy of the generated DSM and DTM

High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders

Automatic Crack Segmentation for UAV-assisted Bridge Inspection

Bridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway.publishedVersio

Self-supervised monocular depth estimation from oblique UAV videos

UAVs have become an essential photogrammetric measurement as they are affordable, easily accessible and versatile. Aerial images captured from UAVs have applications in small and large scale texture mapping, 3D modelling, object detection tasks, DTM and DSM generation etc. Photogrammetric techniques are routinely used for 3D reconstruction from UAV images where multiple images of the same scene are acquired. Developments in computer vision and deep learning techniques have made Single Image Depth Estimation (SIDE) a field of intense research. Using SIDE techniques on UAV images can overcome the need for multiple images for 3D reconstruction. This paper aims to estimate depth from a single UAV aerial image using deep learning. We follow a self-supervised learning approach, Self-Supervised Monocular Depth Estimation (SMDE), which does not need ground truth depth or any extra information other than images for learning to estimate depth. Monocular video frames are used for training the deep learning model which learns depth and pose information jointly through two different networks, one each for depth and pose. The predicted depth and pose are used to reconstruct one image from the viewpoint of another image utilising the temporal information from videos. We propose a novel architecture with two 2D CNN encoders and a 3D CNN decoder for extracting information from consecutive temporal frames. A contrastive loss term is introduced for improving the quality of image generation. Our experiments are carried out on the public UAVid video dataset. The experimental results demonstrate that our model outperforms the state-of-the-art methods in estimating the depths.Comment: Submitted to ISPRS Journal of Photogrammetry and Remote Sensin

The TUM-DLR Multimodal Earth Observation Evaluation Benchmark

We present a new dataset for development, benchmarking, and evaluation of remote sensing and earth observation approaches with special focus on converging perspectives. In order to provide data with different modalities, we observed the same scene using satellites, airplanes, unmanned aerial vehicles (UAV), and smartphones. The dataset is further complemented by ground-truth information and baseline results for different application scenarios. The provided data can be freely used by anybody interested in remote sensing and earth observation and will be continuously augmented and updated