Abstract. Unmanned Aerial Vehicles (UAV) are established platforms for photogrammetric surveys in remote areas. They are lightweight, easy to operate and can allow access to remote sites otherwise difficult (or impossible) to be surveyed with other techniques. Very good accuracy can be obtained also with low-cost UAV platforms as far as a reliable ground control is provided. However, placing ground control points (GCP) in these contexts is time consuming and requires accessibility that, in some cases, can be troublesome. RTK-capable UAV platforms are now available at reasonable costs and can overcome most of these problems, requiring just few (or none at all) GCP and still obtaining accurate results. The paper will present a set of experiments performed in cooperation with ARPA VdA (the Environmental Protection Agency of Valle d’Aosta region, Italy) on a test site in the Italian Alps using a Dji Phantom 4 RTK platform. Its goals are: a) compare accuracies obtainable with different calibration procedures (pre- or on-the-job/self-calibration); b) evaluate the accuracy improvements using different number of GCP when the site allows for it; and c) compare alternative positioning modes for camera projection centres determination, (Network RTK, RTK, Post Processing Kinematic and Single Point Positioning).
Abstract. In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.
Abstract. We study and analyse performance of a system for direct reflectance measurements from a drone. Key instruments of the system are upwards looking irradiance sensor and downwards looking imaging spectrometer. Requirement for both instruments is that they are radiometrically calibrated, the irradiance sensor has to be horizontally stabilized, and the sensors needs to be accurately synchronized. In our system, irradiance measurements are done with FGI Aerial Image Reference System (FGI AIRS), which uses novel optical levelling methodology and can compensate sensor tilting up to 15°. We performed SI-traceable spectral and radiance calibration of FPI hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). After the calibration, the radiance accuracy of different channels was between ±4 % when evaluated with independent test data. Sensors response to radiance proved to be highly linear and was on average 0.9994 for all channels. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI and highlighted the importance of accurate calibration. The drone-based direct reflectance measurement system showed promising results with imagery collected over Jokioinen agricultural grass test site, Finland. AIRS-based image- and band wise image adjustment provided homogenous and seamless image mosaics even under varying illumination conditions and under clouds.
Abstract. The future demands on professional archaeological prospection will be its ability to cover large areas in a time and cost efficient manner with very high spatial resolution and accuracy. The objective of the 2010 in Vienna established Ludwig Boltzmann Institute for Archaeological Prospection and Virtual Archaeology, in collaboration with its nine European partner organisations, is the advancement of the state-of-the-art. This goal will be achieved by focusing on the development of remote sensing, geophysical prospection and virtual reality applications. Main focus will be placed on novel integrated interpretation approaches combining cutting-edge near-surface prospection methods with advanced computer science.
Abstract. The occurrence of urban flooding following strong rainfall events may increase as a result of climate change. Urban expansion, aging infrastructure and an increasing number of impervious surfaces are further exacerbating flooding. To increase resilience and support flood mitigation, bespoke accurate flood modelling and reliable prediction is required. However, flooding in urban areas is most challenging. State-of-the-art flood inundation modelling is still often based on relatively low-resolution 2.5 D bare earth models with 2–5 m GSD. Current systems suffer from a lack of precise input data and numerical instabilities and lack of other important data, such as drainage networks. Especially, the quality and resolution of the topographic input data represents a major source of uncertainty in urban flood modelling. A benchmark study is needed that defines the accuracy requirements for highly detailed urban flood modelling and to improve our understanding of important threshold processes and limitations of current methods and 3D mapping data alike.This paper presents the first steps in establishing a new, innovative multiscale data set suitable to benchmark urban flood modelling. The final data set will consist of high-resolution 3D mapping data acquired from different airborne platforms, focusing on the use of drones (optical and LiDAR). The case study includes residential as well as rural areas in Dudelange/Luxembourg, which have been prone to localized flash flooding following strong rainfall events in recent years. The project also represents a cross disciplinary collaboration between the geospatial and flood modelling community. In this paper, we introduce the first steps to build up a new benchmark data set together with some initial flood modelling results. More detailed investigations will follow in the next phases of this project.
Abstract. This paper identifies the application domain, context of use, processes and goals of low-cost street-level photogrammetry after urban disasters. The proposal seeks a synergy between top-down and bottom-up initiatives carried out by different actors during the humanitarian response phase in data scarce contexts. By focusing on the self-organisation capacities of local people, this paper suggests using collaborative photogrammetry to empower communities hit by disasters and foster their active participation in recovery and reconstruction planning. It shows that this task may prove technically challenging depending on the specifics of the collected imagery and develops a grounded framework to produce user-centred image acquisition guidelines and fit-for-purpose photogrammetric reconstruction workflows, useful in future post-disaster scenarios. To this end, it presents an in-depth analysis of a collaborative photographic mapping initiative undergone by a group of citizen-scientists after the 2016 Central Italy earthquake, followed by the explorative processing of some sample datasets. Specifically, the paper firstly presents a visual ethnographic study of the photographic material uploaded by participants from September 2016 to November 2018 in the two Italian municipalities of Arquata del Tronto and Norcia. Secondly, it illustrates from a technical point of view issues concerning the processing of crowdsourced data (e.g. image filtering, selection, quality, semantic content and 3D model scaling) and discusses the viability of using it to enrich the pool of geo-information available to stakeholders and decision-makers. Final considerations are discussed as part of a grounded framework for future guidelines tailored to multiple goals and data processing scenarios.
While lightweight stereo vision sensors provide detailed and high-resolution information that allows robust and accurate localization, the computation demands required for such process is doubled compared to monocular sensors. In this paper, an alternative model for pose estimation of stereo sensors is introduced which provides an efficient and precise framework for investigating system configurations and maximize pose accuracies. Using the proposed formulation, we examine the parameters that affect accurate pose estimation and their magnitudes and show that for standard operational altitudes of ∼50 m, a five-fold improvement in localization is reached, from ∼0.4–0.5 m with a single sensor to less than 0.1 m by taking advantage of the extended field of view from both cameras. Furthermore, such improvement is reached using cameras with reduced sensor size which are more affordable. Hence, a dual-camera setup improves not only the pose estimation but also enables to use smaller sensors and reduce the overall system cost. Our analysis shows that even a slight modification in camera directions improves the positional accuracy further and yield attitude angle as accurate as ±6’ (compared to ±20’). The proposed pose estimation method relieves computational demands of traditional bundle adjustment processes and is easily integrated with other inertial sensors.
Abstract. With the rapid development of new indoor sensors and acquisition techniques, the amount of indoor three dimensional (3D) point cloud models was significantly increased. However, these massive “blind” point clouds are difficult to satisfy the demand of many location-based indoor applications and GIS analysis. The robust semantic segmentation of 3D point clouds remains a challenge. In this paper, a segmentation with layout estimation network (SLENet)-based 2D–3D semantic transfer method is proposed for robust segmentation of image-based indoor 3D point clouds. Firstly, a SLENet is devised to simultaneously achieve the semantic labels and indoor spatial layout estimation from 2D images. A pixel labeling pool is then constructed to incorporate the visual graphical model to realize the efficient 2D–3D semantic transfer for 3D point clouds, which avoids the time-consuming pixel-wise label transfer and the reprojection error. Finally, a 3D-contextual refinement, which explores the extra-image consistency with 3D constraints is developed to suppress the labeling contradiction caused by multi-superpixel aggregation. The experiments were conducted on an open dataset (NYUDv2 indoor dataset) and a local dataset. In comparison with the state-of-the-art methods in terms of 2D semantic segmentation, SLENet can both learn discriminative enough features for inter-class segmentation while preserving clear boundaries for intra-class segmentation. Based on the excellence of SLENet, the final 3D semantic segmentation tested on the point cloud created from the local image dataset can reach a total accuracy of 89.97%, with the object semantics and indoor structural information both expressed.
Abstract. A 2010 study examining ASTER GDEM v1 data revealed accuracies of 12-25m and strong negative discrepancy biases compared to precise GPS observations, in several test sites in China. Rather than further investigating these, with the advent of ASTER GDEM v2 a new series of tests, also using precise GPS observations but also other DEMs, was performed. In these tests better than the expected 17m accuracies were found (RMSE values of 3.9m to 15.3m) and no strong biases.
Abstract. Soil subsurface moisture content, especially in the root zone, is important for evaluation the influence of soil moisture to agricultural crops. Conservative monitoring by point-measurement methods is time-consuming and expensive. In this paper we represent an active remote-sensing tool for subsurface spatial imaging and analysis of electromagnetic physical properties, mostly water content, by ground-penetrating radar (GPR) reflection. Combined with laboratory methods, this technique enables real-time and highly accurate evaluations of soils' physical qualities in the field. To calculate subsurface moisture content, a model based on the soil texture, porosity, saturation, organic matter and effective electrical conductivity is required. We developed an innovative method that make it possible measures spatial subsurface moisture content up to a depth of 1.5 m in agricultural soils and applied it to two different unsaturated soil types from agricultural fields in Israel: loess soil type (Calcic haploxeralf), common in rural areas of southern Israel with about 30% clay, 30% silt and 40% sand, and hamra soil type (Typic rhodoxeralf), common in rural areas of central Israel with about 10% clay, 5% silt and 85% sand. Combined field and laboratory measurements and model development gave efficient determinations of spatial moisture content in these fields. The environmentally friendly GPR system enabled non-destructive testing. The developed method for measuring moisture content in the laboratory enabled highly accurate interpretation and physical computing. Spatial soil moisture content to 1.5 m depth was determined with 1–5% accuracy, making our method useful for the design of irrigation plans for different interfaces.