Search

The infrastructure heritage is the symbol of the highest standard of the technical construction and design methodologies of the Chinese Eastern Railway (CER). As an important component of the cultural heritage of the CER, their conservation hasn’t attracted more attention than the architectural heritage part. However, some heritage infrastructure components have been already demolished while others were threatened by urban expansion and natural degradation during the past hundred years. In this study, we have analysed structural features based on the original drawings and fieldwork for the reconstruction of the vanished and invisible parts of CER. HBIM will be used to support the conservation of the surviving elements. In addition, we collected geographical coordinates of the heritages using on-site GNSS records and Google Earth®. We propose a method to facilitate the preservation of this historical infrastructure by building a database that integrates HBIM and GIS, including information about historical data and the status quo. In this way, this database will not only be useful for planning purposes by the government and the conservation agencies, but also it will benefit the entire society and the public through its online display and information collection.

This paper aims at presenting the recording and modelling work developed in the framework of restoration and conservation activities for the Basilica di San Giacomo in Como, Italy, whose construction started in the 11th century. The project started in 2022 and involved the application of the Historical Building Information Model (HBIM) methodology through a Scan-to-BIM approach was assessed. High-detailed 3D survey techniques were used to acquire the specific shape of the church and annexed buildings. Then, through different modelling strategies, the HBIM environment allowed the representation of all the architectural elements. The purpose of this model is to have essential support to plan the restoration activities and to give the different experts involved a single three-dimensional tool for managing all the information during the development of the construction works. The possibility to update the model over time with geometric and non-geometric information will provide a powerful tool also for other future activities, such as the installation of a monitoring system that could reveal the displacements of bearing elements of the church.

Digitization processes, i.e., the practice of converting physical assets into digital assets, are gradually transforming the Architecture, Engineering, Construction and Owner Operators (AECOO) industry, and it concerns new and existing buildings and infrastructures. This study is about the management of disused railways and aims to present a BIM (Building Information Modelling) / GIS (Geographic Information Systems) workflow to identify and model disused railways buildings. The workflow was tested on the ‘Potenza Inferiore Scalo’ – ‘Laurenzara’ disused railway located in the South of Italy. The objective is the creation of a comprehensive digital database to help decision-makers give new utility to these existing infrastructures and buildings. And this is because the railway heritage can represent an essential factor in sustainable development processes and landscape regeneration.

Large-scale infrastructure is typically regarded as a symbol of technological and engineering development during its construction time. This phenomenon is particularly evident in the infrastructure heritage along the Chinese Eastern Railway (CER) Main Line. However, the conservation of this crucial component of the CER, which is an important cross-culture and linear heritage in China, has received little attention, with conservation methods remaining relatively traditional. Due to threats posed by human and natural factors, the remaining infrastructure heritage is at risk of being lost. Additionally, the vanished part, including the infrastructure and technical heritage applied during construction, cannot be revealed. To address this issue, new technologies and management methods, such as BIM/HBIM and GIS, should be introduced to reconstruct the vanished part and record the status quo of what still remains. We propose building a database that integrates HBIM and GIS to facilitate the preservation of this historic infrastructure and analyse the feasibility of this method. Our research aims to establish an accurate, efficient, and collaborative method for integrating historical data and preserving the infrastructure heritage along the Main Line of the CER.

One of the major challenges in precision viticulture in Europe is the detection and mapping of flavescence dorée (FD) grapevine disease to monitor and contain its spread. The lack of effective cures and the need for sustainable preventive measures are nowadays crucial issues. Insecticides and the plants uprooting are commonly employed to withhold disease infection, even if these solutions imply serious economic consequences and a strong environmental impact. The development of a rapid strategy to identify the disease is required to cover large portions of the crop and thus to limit damages in a time-effective way. This paper investigates the use of Unmanned Aerial Vehicles (UAVs), a cost-effective approach to early detection of diseased areas. We address this task with an object detection deep network, Faster R-CNN, instead of a traditional pixel-wise classifier. This work tests Faster R-CNN performance on this specific application through a comparative analysis with a pixel-wise classification algorithm (Random Forest). To take advantage of the full image resolution, the experimental analysis is performed using the original UAV imagery acquired in real conditions (instead of the derived orthomosaic). The first result of this paper is the definition of a new dataset for FD disease identification by UAV original imagery at the canopy scale. Moreover, we demonstrate the feasibility of applying Faster-R-CNN as a quasi-real-time alternative solution to semantic segmentation. The trained Faster-R-CNN achieved an average precision of 82% on the test set.

Dynamic Time Warping (DTW) has been successfully used for crops mapping due to its capability to achieve good classification results when a reduced number of training samples and irregular satellite image time series is available. Despite its recognized advantages, DTW does not account for the duration and seasonality of crops and local differences when assessing the similarity between two temporal sequences. In this study, we implemented a Weighted Derivative modification of DTW (WDDTW) and compared it with DTW and Time Weighted Dynamic Time Warping (TWDTW) for crops mapping. We show that WDDTW outperformed DTW achieving an overall accuracy of 67 %, whereas DTW obtained an accuracy of 57%. Yet, TWDTW performed better than both methods obtaining an accuracy of 88%.

Crop lodging – the bending of crop stems from the vertical – is a major yield-reducing factor in cereal crops and causes deterioration in grain quality. Accurate assessment of crop lodging is important for improving estimates of crop yield losses, informing insurance loss adjusters and influencing management decisions for subsequent seasons. The role of remote sensing data, particularly synthetic aperture radar (SAR) data has been emphasized in the recent literature for crop lodging assessment. However, the effect of lodging on SAR scattering mechanisms is still unknown. Therefore, this research aims to understand the possible change in scattering mechanisms due to lodging by investigating SAR image pairs before and after lodging. We conducted the study in 26 wheat fields in the Bonifiche Ferraresi farm, located in Jolanda di Savoia, Ferrara, Italy. We measured temporal crop biophysical (e.g. crop angle) parameters and acquired multi-incidence angle RADARSAT-2 (R-2 FQ8-27° and R-2 FQ21-41°) and Sentinel-1 (S-1 40°) images corresponding to the time of field observations. We extracted metrics of SAR scattering mechanisms from RADARSAT-2 and Sentinel-1 image pairs in different zones using the unsupervised H/α decomposition algorithm and Wishart classifier. Contrasting results were obtained at different incidence angles. Bragg surface scattering increased in the case of S-1 (6.8%), R-2 FQ8 (1.8%) while at R-2 FQ21, it decreased (8%) after lodging. The change in double bounce scattering was more prominent at low incidence angle. These observations can guide future use of SAR-based information for operational crop lodging assessment in particular, and sustainable agriculture in general.

Updated information on urban land use allows city planners and decision makers to conduct large scale monitoring of urban areas for sustainable urban growth. Remote sensing data and classification methods offer an efficient and reliable way to update such land use maps. Features extracted from land cover maps are helpful on performing a land use classification task. Such prior information can be embedded in the design of a deep learning based land use classifier by applying a multitask learning setup—simultaneously solving a land use and a land cover classification task. In this study, we explore a fully convolutional multitask network to classify urban land use from very high resolution (VHR) imagery. We experimented with three different setups of the fully convolutional network and compared it against a baseline random forest classifier. The first setup is a standard network only predicting the land use class of each pixel in the image. The second setup is a multitask network that concatenates the land use and land cover class labels in the same output layer of the network while the other setup accept as an input the land cover predictions, predicted by a subpart of the network, concatenated to the original input image patches. The two deep multitask networks outperforms the other two classifiers by at least 30% in average F1-score.

The 3rd edition of the international summer school “Close-range Sensing Techniques in Alpine terrain” took place in Obergurgl, Austria, in June 2019. This article reports on results from the training and seminar activities and the outcome of student questionnaire survey. Comparison between the recent edition and the past edition in 2017 shows no significant differences on the level of satisfaction on organizational and training aspects. Gender balance was present both in candidates and in the outcome of selections. Selection was based on past research activities and on topic relevance. The majority of trainees were therefore doctoral candidates and postdoctoral researchers, but also motivated master students participated. The training took place through keynotes, lectures, seminars, in the field with hands-on surveys followed by data analysis in the lab, and teamwork for preparing a final team presentation over different assignments.

Land cover describes the physical material of the earth’s surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7 % and 77.4 % can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification.

In this paper we investigate the potential of automatic supervised classification for urban hydrological applications. In particular, we contribute to runoff simulations using hydrodynamic urban drainage models. In order to assess whether the capacity of the sewers is sufficient to avoid surcharge within certain return periods, precipitation is transformed into runoff. The transformation of precipitation into runoff requires knowledge about the proportion of drainage-effective areas and their spatial distribution in the catchment area. Common simulation methods use the coefficient of imperviousness as an important parameter to estimate the overland flow, which subsequently contributes to the pipe flow. The coefficient of imperviousness is the percentage of area covered by impervious surfaces such as roofs or road surfaces. It is still common practice to assign the coefficient of imperviousness for each particular land parcel manually by visual interpretation of aerial images. Based on classification results of these imagery we contribute to an objective automatic determination of the coefficient of imperviousness. In this context we compare two classification techniques: Random Forests (RF) and Conditional Random Fields (CRF). Experimental results performed on an urban test area show good results and confirm that the automated derivation of the coefficient of imperviousness, apart from being more objective and, thus, reproducible, delivers more accurate results than the interactive estimation. We achieve an overall accuracy of about 85 % for both classifiers. The root mean square error of the differences of the coefficient of imperviousness compared to the reference is 4.4 % for the CRF-based classification, and 3.8 % for the RF-based classification.

Lunar rilles are any of the long, narrow depressions on the surface of the Moon. These structures have different characteristics, which are related to their origin. Here, a classification of the lunar rilles in different groups with similar morphometric features and likely the same formation mechanisms is proposed. The detection, classification and mapping of lunar rilles has been achieved in two quadrants of the northern hemisphere, which are located between -90°E to 90°E longitude and 0°N to 60°N latitude. For the detection and mapping of rilles, we used images from the Lunar Reconnaissance Orbiter Camera (LROC) [1] which have a resolution of 100 m/pixel and the DEM from Lunar Orbiter Laser Altimeter (LOLA) [2] with a resolution of 6 m/pixel. Lunar rilles include generic sinuous rilles (SR), cracks on lava deposits (CL), surface lava flows (LF), subsurface lava tubes or catena (crater chains, CC) and tectonic structures (TS). For each type of the classified structures, morphologic parameters, i.e. length, width, perimeter, area, depth, longitude and latitude have been measured in order to allow a better understanding of how these features formed [3]. For those sinuous rilles classified as remnants of shallow lava channels or subsurface collapsed lava tubes, morphometric analysis included the extraction of the sinuosity index and of the regional slope and azimuth. This work is born inside an international cooperation between Italy and China called "Moon Mapping project" for the cooperation between Italian and Chinese college students [4].

Topopographic mapping issues concern the area coverage at different scales and their age. The age of the map is determined by the system of updating. The United Nations (UNGGIM) have attempted to track the global map coverage at various scale ranges, which has greatly improved in recent decades. However the poor state of updating of base maps is still a global problem. In Saudi Arabia large scale mapping is carried out for all urban, suburban and rural areas by aerial surveys. Updating is carried out by remapping every 5 to 10 years. Due to the rapid urban development this is not satisfactory, but faster update methods are forseen by use of high resolution satellite imagery and the improvement of object oriented geodatabase structures, which will permit to utilize various survey technologies to update the photogrammetry established geodatabases. The longterm goal is to create an geodata infrastructure, which exists in Great Britain or Germany.

The aftermath of wartime attacks is often felt long after the war ended, as numerous unexploded bombs may still exist in the ground. Typically, such areas are documented in so-called impact maps which are based on the detection of bomb craters. This paper proposes a method for the automatic detection of bomb craters in aerial wartime images that were taken during the Second World War. The object model for the bomb craters is represented by ellipses. A probabilistic approach based on marked point processes determines the most likely configuration of objects within the scene. Adding and removing new objects to and from the current configuration, respectively, changing their positions and modifying the ellipse parameters randomly creates new object configurations. Each configuration is evaluated using an energy function. High gradient magnitudes along the border of the ellipse are favored and overlapping ellipses are penalized. Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing provides the global energy optimum, which describes the conformance with a predefined model. For generating the impact map a probability map is defined which is created from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively. Our results show the general potential of the method for the automatic detection of bomb craters and its automated generation of an impact map in a heterogeneous image stock.

Due to the road safety problem is becoming more and more serious recent years, existing road safety assessment by using automatic method is necessary. Meanwhile, since the pole-like objects have large effect on road safety and are in high demand as facilities to be managed, the automatic pole-like objects extraction is becoming a hot issue. As a result, a robust, quick and automatic pole-like object detection algorithm in MLS data is proposed in this paper. Two datasets are tested to show performance of the proposed algorithm, it demonstrates that it is feasible to detect tree with an overall accuracy of over 92% and other pole-like object of 72% in dataset A and 82% of tree points and 75% of other pole points in dataset B.