Validation tests of open-source procedures for digital camera calibration and 3D image-based modelling

Among the many open-source software solutions recently developed for the extraction of point clouds from a set of un-oriented images, the photogrammetric tools Apero and MicMac (IGN, Institut G\ue9ographique National) aim to distinguish themselves by focusing on the accuracy and the metric content of the final result. This paper firstly aims at assessing the accuracy of the simplified and automated calibration procedure offered by the IGN tools. Results obtained with this procedure were compared with those achieved with a test-range calibration approach using a pre-surveyed laboratory test-field. Both direct and a-posteriori validation tests turned out successfully showing the stability and the metric accuracy of the process, even when low textured or reflective surfaces are present in the 3D scene. Afterwards, the possibility of achieving accurate 3D models from the subsequently extracted dense point clouds is also evaluated. Three different types of sculptural elements were chosen as test-objects and "ground-truth" data were acquired with triangulation laser scanners. 3D models derived from point clouds oriented with a simplified relative procedure show a suitable metric accuracy: all comparisons delivered a standard deviation of millimeter-level. The use of Ground Control Points in the orientation phase did not improve significantly the accuracy of the final 3D model, when a small figure-like corbel was used as test-object

Reconstrucción de edificios y análisis urbanístico de centros históricos con fotogrametría aérea

[EN] Historical city centers are complex scenarios to be reconstructed in 3D. Advances in automated 3D reconstruction are useful to apply urban analysis that otherwise will require a lot of human effort. In this paper, urban parameters are automatically derived to quantify the urban analysis in historical city centers. Particularly, an aerial photogrammetric flight is used as input data to reconstruct 3D models of buildings with metric capabilities. The results reveal that geometric information of buildings (heights, areas and volumes) and urban density attributes (building coverage ratio and floor area ratio) plays an essential role in the design, planning and management of historical cities. The approach developed was validated in the historical city center of Trento (Italy) using cadastral data and a mobile mapping system (MMS) as ground-truth.[ES] Los centros urbanos históricos son escenarios complejos para su reconstrucción tridimensional. Los avances en la reconstrucción automática son de gran utilidad para realizar análisis urbanísticos que de otra manera requerirían un elevado esfuerzo humano. En este artículo, se derivarán de forma automática parámetros urbanísticos para el análisis de los centros históricos. En particular, se utiliza un vuelo fotogramétrico como base para la obtención de modelos 3D de edificios con propiedades métricas. Los resultados revelan que la información geométrica de los edificios (alturas, áreas y volúmenes) y los atributos de densidad urbana (intensidad de ocupación del suelo en 2D y 3D) juegan un papel esencial en el diseño, planificación y gestión de los centros históricos. El enfoque propuesto fue validado en el centro histórico de la ciudad de Trento (Italia) utilizando datos catastrales y un sistema de cartografiado móvil como referencia geométrica.S

Valorisation of history and landscape for promoting the memory of WWI

10 p.In recent years many activities were conducted to commemorate the 100th Anniversary of the FirstWorld War (WWI) outbreak. Among these, the valorisation of history and landscape (VAST) project(http://vast.fbk.eu) was part of the initiatives promoted by the Autonomous Province of Trento (Italy)as a tribute to WWI events in the region. The project was primarily aimed to document and promote,through 3D digitization approaches, ICT technologies and communication material, the memory of sites,theatre of the world conflict. The Trento’s area was under the Austro-Hungarian Empire until the end ofWWI and on the border with the Italian Kingdom. The area represented a crucial and bloody war frontbetween the Austrian and Italian territories. It was thus constellated of military fortresses, trenches andtunnels, most of them now ruined and at risk to slowly disappear. 3D surveying and modelling techniqueswere exploited to produce 3D digital models of structures and objects, along with virtual tours, dissem-ination material and a WebGIS of the area. All the products are now used for restoration, valorisation,educational and communication purposesS

Reconstrucción de edificios y análisis urbanístico de centros históricos con fotogrametría aérea

Historical city centers are complex scenarios to be reconstructed in 3D. Advances in automated 3D reconstruction are useful to apply urban analysis that otherwise will require a lot of human effort. In this paper, urban parameters are automatically derived to quantify the urban analysis in historical city centers. Particularly, an aerial photogrammetric flight is used as input data to reconstruct 3D models of buildings with metric capabilities. The results reveal that geometric information of buildings (heights, areas and volumes) and urban density attributes (building coverage ratio and floor area ratio) plays an essential role in the design, planning and management of historical cities. The approach developed was validated in the historical city center of Trento (Italy) using cadastral data and a mobile mapping system (MMS) as ground-truth.Los centros urbanos históricos son escenarios complejos para su reconstrucción tridimensional. Los avances en la reconstrucción automática son de gran utilidad para realizar análisis urbanísticos que de otra manera requerirían un elevado esfuerzo humano. En este artículo, se derivarán de forma automática parámetros urbanísticos para el análisis de los centros históricos. En particular, se utiliza un vuelo fotogramétrico como base para la obtención de modelos 3D de edificios con propiedades métricas. Los resultados revelan que la información geométrica de los edificios (alturas, áreas y volúmenes) y los atributos de densidad urbana (intensidad de ocupación del suelo en 2D y 3D) juegan un papel esencial en el diseño, planificación y gestión de los centros históricos. El enfoque propuesto fue validado en el centro histórico de la ciudad de Trento (Italia) utilizando datos catastrales y un sistema de cartografiado móvil como referencia geométrica

Creating multi-temporal maps of urban environments of improved localization of autonomous vehicles

The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics. In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Improving deep learning based semantic segmentation with multi view outliner correction

The goal of this paper is to use transfer learning for semi supervised semantic segmentation in 2D images: given a pretrained deep convolutional network (DCNN), our aim is to adapt it to a new camera-sensor system by enforcing predictions to be consistent for the same object in space. This is enabled by projecting 3D object points into multi-view 2D images. Since every 3D object point is usually mapped to a number of 2D images, each of which undergoes a pixelwise classification using the pretrained DCNN, we obtain a number of predictions (labels) for the same object point. This makes it possible to detect and correct outlier predictions. Ultimately, we retrain the DCNN on the corrected dataset in order to adapt the network to the new input data. We demonstrate the effectiveness of our approach on a mobile mapping dataset containing over 10'000 images and more than 1 billion 3D points. Moreover, we manually annotated a subset of the mobile mapping images and show that we were able to rise the mean intersection over union (mIoU) by approximately 10% with Deeplabv3+, using our approach. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Improving disparity estimation based on residual cost volume and reconstruction error volume

Recently, great progress has been made in formulating dense disparity estimation as a pixel-wise learning task to be solved by deep convolutional neural networks. However, most resulting pixel-wise disparity maps only show little detail for small structures. In this paper, we propose a two-stage architecture: we first learn initial disparities using an initial network, and then employ a disparity refinement network, guided by the initial results, which directly learns disparity corrections. Based on the initial disparities, we construct a residual cost volume between shared left and right feature maps in a potential disparity residual interval, which can capture more detailed context information. Then, the right feature map is warped with the initial disparity and a reconstruction error volume is constructed between the warped right feature map and the original left feature map, which provides a measure of correctness of the initial disparities. The main contribution of this paper is to combine the residual cost volume and the reconstruction error volume to guide training of the refinement network. We use a shallow encoder-decoder module in the refinement network and do learning from coarse to fine, which simplifies the learning problem. We evaluate our method on several challenging stereo datasets. Experimental results demonstrate that our refinement network can significantly improve the overall accuracy by reducing the estimation error by 30% compared with our initial network. Moreover, our network also achieves competitive performance compared with other CNN-based methods. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Improving the classification of Land use Objects using Dense Connectitvity of Convolutional Neural Networks

Land use is an important variable in remote sensing which describes the functions carried out on a piece of land in order to obtain benefits and is especially useful to the personnel working in the fields of urban management and planning. The land use information is maintained by national mapping agencies in geo-spatial databases. Commonly, land use data is stored in the form of polygon objects; the label of the object indicates land use. The main goal of classification of land use objects is to update an existing database in an automatic process. Recently, Convolutional Neural Networks (CNN) have been widely used to tackle this task utilizing high resolution aerial images (and derived data such as digital surface model). One big challenge classifying polygons is to deal with the large variation in their geometrical extent. For this challenge, we adopt the method of Yang et al. (2019) to decompose polygons into regular patches of fixed size. The decomposition leads to two sets of polygons: small and large, where the former suffers from a lower identification rate. In this paper, we propose CNN methods which incorporate dense connectivity and integrate it with intermediate information via global average pooling to improve land use classification, mainly focusing on small polygons. We present different network variants by incorporating intermediate information via global average pooling from different stages of the network. We test our methods on two sites; our experiments show that the dense connectivity and integration of intermediate information has a positive effect not only on the classification accuracy on the whole but also on the identification of small polygons. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Phenol-Rich Food Acceptability: The Influence of Variations in Sweetness Optima and Sensory-Liking Patterns

12openInternationalItalian coauthor/editorThe consumption of phenol-rich foods is limited by their prominent bitterness and astringency. This issue has been addressed by adding sweet tastes, which suppress bitterness, but this is not a complete solution since individuals also differ in their preference for sweetness. In this study, we aimed at identifying groups of consumers differing in sweetness optima and sensory-liking patterns. To this end, increasing concentrations of sucrose were added to a chocolate pudding base. This allowed us to (1) investigate if individual differences in sensory responses are associated with different sweet liking optima in a product context, (2) define the psychological and oro-sensory profile of sweet liker phenotypes derived using a product context, and (3) assess if individuals differing in sweet liking optima differ also in consumption and liking of phenol-rich foods and beverages as a function of their sensory properties (e.g., sweeter vs. more bitter and astringent products). Individuals (1208; 58.4% women, 18–69 years) were characterised for demographics, responsiveness to 6-n-propylthiouracil (PROP), personality traits and attitudes toward foods. Three clusters were identified based on correlations between sensory responses (sweetness, bitterness and astringency) and liking of the samples: liking was positively related to sweetness and negatively to bitterness and astringency in High and Moderate Sweet Likers, and the opposite in Inverted U-Shaped. Differences between clusters were found in age, gender and personality. Furthermore, the Inverted-U Shaped cluster was found to have overall healthier food behaviours and preferences, with higher liking and consumption of phenol-rich vegetables and beverages without added sugar. These findings point out the importance of identifying the individual sensory-liking patterns in order to develop more effective strategies to promote the acceptability of healthy phenol-rich foods.openSpinelli, Sara; Prescott, John; Pierguidi, Lapo; Dinnella, Caterina; Arena, Elena; Braghieri, Ada; Di Monaco, Rossella; Gallina Toschi, Tullia; Endrizzi, Isabella; Proserpio, Cristina; Torri, Luisa; Monteleone, ErminioSpinelli, S.; Prescott, J.; Pierguidi, L.; Dinnella, C.; Arena, E.; Braghieri, A.; Di Monaco, R.; Gallina Toschi, T.; Endrizzi, I.; Proserpio, C.; Torri, L.; Monteleone, E