bluuebunny/crossref_metadata_2025 huggingface dataset:
SEMANTIC SEGMENTATION OF FOREST STANDS OF PURE SPECIES AS A GLOBAL OPTIMIZATION PROBLEM
Authors: Dechesne C. , Mallet C. , Le Bris A. , Gouet-Brunet V.
Published: June 2017

Abstract. Forest stand delineation is a fundamental task for forest management purposes, that is still mainly manually performed through visual inspection of geospatial (very) high spatial resolution images. Stand detection has been barely addressed in the literature which has mainly focused, in forested environments, on individual tree extraction and tree species classification. From a methodological point of view, stand detection can be considered as a semantic segmentation problem. It offers two advantages. First, one can retrieve the dominant tree species per segment. Secondly, one can benefit from existing low-level tree species label maps from the literature as a basis for high-level object extraction. Thus, the semantic segmentation issue becomes a regularization issue in a weakly structured environment and can be formulated in an energetical framework. This papers aims at investigating which regularization strategies of the literature are the most adapted to delineate and classify forest stands of pure species. Both airborne lidar point clouds and multispectral very high spatial resolution images are integrated for that purpose. The local methods (such as filtering and probabilistic relaxation) are not adapted for such problem since the increase of the classification accuracy is below 5%. The global methods, based on an energy model, tend to be more efficient with an accuracy gain up to 15%. The segmentation results using such models have an accuracy ranging from 96% to 99%.
EXPOSURE AND VULNERABILITY GEOSPATIAL ANALYSIS USING EARTH OBSERVATION DATA IN THE CITY OF LIEGE, BELGIUM
Authors: Stephenne N. , Beaumont B. , Hallot E. , Lenartz F. , Lefebre F. , Lauwaet D. , Poelmans L. , Wolff E.
Published: June 2017

Abstract. Risk situation can be mitigated by prevention measures, early warning tools and adequate monitoring of past experiences where Earth Observation and geospatial analysis have an adding value. This paper discusses the potential use of Earth Observation data and especially Land Cover / Land Use map in addressing within the three aspects of the risk assessment: danger, exposure and vulnerability. Evidences of the harmful effects of air pollution or heat waves are widely admitted and should increase in the context of global warming. Moreover, urban areas are generally warmer than rural surroundings, the so-called urban heat island. Combined with in-situ measurements, this paper presents models of city or local climate (air pollution and urban heat island), with a resolution of less than one kilometer, developed by integrating several sources of information including Earth Observation data and in particular Land Cover / Land Use. This assessment of the danger is then be related to a map of exposure and vulnerable people. Using dasymetric method to disaggregate statistical information on Land Cover / Land Use data, the SmartPop project analyzes in parallel the map of danger with the maps of people exposure A special focus on some categories at risk such as the elderly has been proposed by Aubrecht and Ozceylan (2013). Perspectives of the project includes the integration of a new Land Cover / Land Use map in the danger, exposure and vulnerability models and proposition of several aspects of risk assessment with the stakeholders of Wallonia.
IMPROVING ACTIVE QUERIES WITH A LOCAL SEGMENTATION STEP AND APPLICATION TO LAND COVER CLASSIFICATION
Authors: Wuttke S. , Middelmann W. , Stilla U.
Published: June 2017

Abstract. Active queries is an active learning method used for classification of remote sensing images. It consists of three steps: hierarchical clustering, dendrogram division, and active label selection. The goal of active learning is to reduce the needed amount of labeled data while preserving classification accuracy. We propose to apply local segmentation as a new step preceding the hierarchical clustering. We are using the SLIC (simple linear iterative clustering) algorithm for dedicated image segmentation. This incorporates spatial knowledge which leads to an increased learning rate and reduces classification error. The proposed method is applied to six different areas of the Vaihingen dataset.
INVESTIGATING THE POTENTIAL OF DEEP NEURAL NETWORKS FOR LARGE-SCALE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE IMAGES
Authors: Postadjian T. , Le Bris A. , Sahbi H. , Mallet C.
Published: June 2017

Abstract. Semantic classification is a core remote sensing task as it provides the fundamental input for land-cover map generation. The very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many classification tasks including the automatic analysis of Very High Spatial Resolution (VHR) geospatial images. Most of the recent initiatives have focused on very high discrimination capacity combined with accurate object boundary retrieval. Therefore, current architectures are perfectly tailored for urban areas over restricted areas but not designed for large-scale purposes. This paper presents an end-to-end automatic processing chain, based on DCNNs, that aims at performing large-scale classification of VHR satellite images (here SPOT 6/7). Since this work assesses, through various experiments, the potential of DCNNs for country-scale VHR land-cover map generation, a simple yet effective architecture is proposed, efficiently discriminating the main classes of interest (namely buildings, roads, water, crops, vegetated areas) by exploiting existing VHR land-cover maps for training.
AN EFFICIENT METHOD TO DETECT MUTUAL OVERLAP OF A LARGE SET OF UNORDERED IMAGES FOR STRUCTURE-FROM-MOTION
Authors: Wang X. , Zhan Z.Q. , Heipke C.
Published: June 2017

Abstract. Recently, low-cost 3D reconstruction based on images has become a popular focus of photogrammetry and computer vision research. Methods which can handle an arbitrary geometric setup of a large number of unordered and convergent images are of particular interest. However, determining the mutual overlap poses a considerable challenge. We propose a new method which was inspired by and improves upon methods employing random k-d forests for this task. Specifically, we first derive features from the images and then a random k-d forest is used to find the nearest neighbours in feature space. Subsequently, the degree of similarity between individual images, the image overlaps and thus images belonging to a common block are calculated as input to a structure-from-motion (sfm) pipeline. In our experiments we show the general applicability of the new method and compare it with other methods by analyzing the time efficiency. Orientations and 3D reconstructions were successfully conducted with our overlap graphs by sfm. The results show a speed-up of a factor of 80 compared to conventional pairwise matching, and of 8 and 2 compared to the VocMatch approach using 1 and 4 CPU, respectively.
EVALUATING THREE INSAR TIME-SERIES METHODS TO ASSESS CREEP MOTION, CASE STUDY: MASOULEH LANDSLIDE IN NORTH IRAN
Authors: Mirzaee S. , Motagh M. , Akbari B. , Wetzel H. U. , Roessner S.
Published: June 2017

Abstract. Masouleh is one of the ancient cities located in a high mountainous area in Gilan province of northern Iran. The region is threatened by a hazardous landslide, which was last activated in 1998, causing 32 dead and 45 injured. Significant temporal decorrelation caused by dense vegetation coverage within the landslide area makes the use of Synthetic Aperture Radar Interferometry (InSAR) for monitoring landslide movement very challenging. In this paper, we investigate the capability of three InSAR time-series techniques for evaluating creep motion on Masouleh landslide. The techniques are Persistent Scatterer Interferometry (PSI), Small BAseline Subset (SBAS) and SqueeSAR. The analysis is done using a dataset of 33 TerraSAR-X images in SpotLight (SL) mode covering a period of 15 months between June 2015 and September 2016. Results show the distinguished capability of SqueeSAR method in comparison to 2 other techniques for assessing landslide movement. The final number of scatterers in the landslide body detected by PSI and SBAS are about 70 and 120 respectively while this increases to about 345 in SqueeSAR. The coherence of interferograms improved by about 37% for SqueeSAR as compared to SBAS. The same rate of displacement was observed in those regions where all the methods were able to detect scatterers. Maximum rates of displacement detected by SqueeSAR technique in the northern edge, older and younger part of the landslide body are about -39, -65 and -22 mm/y, respectively.
VOXEL- AND GRAPH-BASED POINT CLOUD SEGMENTATION OF 3D SCENES USING PERCEPTUAL GROUPING LAWS
Authors: Xu Y. , Hoegner L. , Tuttas S. , Stilla U.
Published: June 2017

Abstract. Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.
UTILIZING THE UNCERTAINTY OF POLYHEDRA FOR THE RECONSTRUCTION OF
BUILDINGS
Authors: Meidow J. , Förstner W.
Published: June 2017

Abstract. The reconstruction of urban areas suffers from the dilemma of modeling urban structures in a generic or specific way, thus being too unspecific or too restrictive. One approach is to model and to instantiate buildings as arbitrarily shaped polyhedra and to recognize comprised man-made structures in a subsequent stage by geometric reasoning. To do so, we assume the existence of boundary representations for buildings with vertical walls and horizontal ground floors. To stay generic and to avoid the use of templates for pre-defined building primitives, no further assumptions for the buildings’ outlines and the planar roof areas are made. Typically, roof areas are derived interactively or in an automatic process based on given point clouds or digital surface models. Due to the mensuration process and the assumption of planar boundaries, these planar faces are uncertain. Thus, a stochastic geometric reasoning process with statistical testing is appropriate to detected man-made structures followed by an adjustment to enforce the deduced geometric constraints. Unfortunately, city models usually do not feature information about the uncertainty of geometric entities. We present an approach to specify the uncertainty of the planes corresponding to the planar patches, i.e., polygons bounding a building, analytically. This paves the way to conduct the reasoning process with just a few assumptions. We explicate and demonstrate the approach with real data.
LANDSAT BIG DATA ANALYSIS FOR DETECTING LONG-TERM WATER QUALITY CHANGES: A CASE STUDY IN THE HAN RIVER, SOUTH KOREA
Authors: Seong J. C. , Hwang C. S. , Gibbs R. , Roh K. , Mehdi M. R. , Oh C. , Jeong J. J.
Published: June 2017

Abstract. Landsat imagery satisfies the characteristics of big data because of its massive data archive since 1972, continuous temporal updates, and various spatial resolutions from different sensors. As a case study of Landsat big data analysis, a total of 776 Landsat scenes were analyzed that cover a part of the Han River in South Korea. A total of eleven sample datasets was taken at the upstream, mid-stream and downstream along the Han River. This research aimed at analyzing locational variance of reflectance, analyzing seasonal difference, finding long-term changes, and modeling algal amount change. There were distinctive reflectance differences among the downstream, mid-stream and upstream areas. Red, green, blue and near-infrared reflectance values decreased significantly toward the upstream. Results also showed that reflectance values are significantly associated with the seasonal factor. In the case of long-term trends, reflectance values have slightly increased in the downstream, while decreased slightly in the mid-stream and upstream. The modeling of chlorophyll-a and Secchi disk depth imply that water clarity has decreased over time while chlorophyll-a amounts have decreased. The decreasing water clarity seems to be attributed to other reasons than chlorophyll-a.
SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARK
Authors: Hackel T. , Savinov N. , Ladicky L. , Wegner J. D. , Schindler K. , Pollefeys M.
Published: June 2017

Abstract. This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.
GEOREFERENCING IN GNSS-CHALLENGED ENVIRONMENT: INTEGRATING UWB AND IMU TECHNOLOGIES
Authors: Toth C. K. , Koppanyi Z. , Navratil V. , Grejner-Brzezinska D.
Published: June 2017

Abstract. Acquiring geospatial data in GNSS compromised environments remains a problem in mapping and positioning in general. Urban canyons, heavily vegetated areas, indoor environments represent different levels of GNSS signal availability from weak to no signal reception. Even outdoors, with multiple GNSS systems, with an ever-increasing number of satellites, there are many situations with limited or no access to GNSS signals. Independent navigation sensors, such as IMU can provide high-data rate information but their initial accuracy degrades quickly, as the measurement data drift over time unless positioning fixes are provided from another source. At The Ohio State University’s Satellite Positioning and Inertial Navigation (SPIN) Laboratory, as one feasible solution, Ultra- Wideband (UWB) radio units are used to aid positioning and navigating in GNSS compromised environments, including indoor and outdoor scenarios. Here we report about experiences obtained with georeferencing a pushcart based sensor system under canopied areas. The positioning system is based on UWB and IMU sensor integration, and provides sensor platform orientation for an electromagnetic inference (EMI) sensor. Performance evaluation results are provided for various test scenarios, confirming acceptable results for applications where high accuracy is not required.
HETEROGENEITY INDEX FOR THE ASSESSMENT OF RELATIONSHIP BETWEEN LAND USE PATTERN AND ROAD TRAFFIC CONGESTION IN APAPA-OWOROSHOKI EXPRESS WAY, LAGOS METROPOLIS
Authors: Alaigba D. B. , Soumah M. , Banjo M. O.
Published: June 2017

Abstract. The problem of urban mobility is complicated by traffic delay, resulting from poor planning, high population density and poor condition of roads within urban spaces. This study assessed traffic congestion resulting from differential contribution made by various land-uses along Apapa-Oworoshoki expressway in Lagos metropolis. The data for this study was from both primary and secondary sources; GPS point data was collected at selected points for traffic volume count; observation of the nature of vehicular traffic congestion, and land use types along the corridor. Existing data on traffic count along the corridor, connectivity map and land use map sourced from relevant authorities were acquired. Traffic congestion within the area was estimated using volume capacity ratio (V/C). Heterogeneity Index was developed and used to quantify the percentage contribution to traffic volume from various land-use categories. Analytical Hierarchical Processing (AHP) and knowledge-based weighting were used to rank the importance of different heterogeneity indices. Results showed significant relationship between the degree of heterogeneity of the land use pattern and road traffic congestion. Volume Capacity Ratio computed revealed that the route corridor exceeds its designed capacity in the southward direction between the hours of 8am and 12pm on working days. Five major nodes were analyzed along the corridor, and were all above the expected Passenger Car Unit (PCU), these are “Oshodi” 15 %, “Airport junction” 10 %, “Cele bus stop” 21 %, “Mile 2” 14 %, “Berger” 15 % and “Tincan bus stop” 33 % indicating heavy traffic congestion.
SAPC – APPLICATION FOR ADAPTING SCANNED ANALOGUE PHOTOGRAPHS TO USE THEM IN STRUCTURE FROM MOTION TECHNOLOGY
Authors: Salach A.
Published: June 2017

Abstract. The documentary value of analogue scanned photographs is invaluable. A large and rich collection of archival photographs is often the only source of information about past of the selected area. This paper presents a method of adaptation of scanned, analogue photographs to suitable form allowing to use them in Structure from Motion technology. For this purpose, an automatic algorithm, implemented in the application called SAPC (Scanned Aerial Photographs Correction), which transforms scans to a form, which characteristic similar to the images captured by a digital camera, was invented. Images, which are created in the applied program as output data, are characterized by the same principal point position in each photo and the same resolution through cutting out the black photo frame. Additionally, SAPC generates a binary image file, which can mask areas of fiducial marks. In the experimental section, scanned, analogue photographs of Warsaw, which had been captured in 1986, were used in two variants: unprocessed and processed in SAPC application. An insightful analysis was conducted on the influence of transformation in SAPC on quality of spatial orientation of photographs. Block adjustment through aerial triangulation was calculated using two SfM software products: Agisoft PhotoScan and Pix4d and their results were compared with results obtained from professional photogrammetric software – Trimble Inpho. The author concluded that pre-processing in SAPC application had a positive impact on a quality of block orientation of scanned, analogue photographs, using SfM technology.
METRIC EVALUATION PIPELINE FOR 3D MODELING OF URBAN SCENES
Authors: Bosch M. , Leichtman A. , Chilcott D. , Goldberg H. , Brown M.
Published: June 2017

Abstract. Publicly available benchmark data and metric evaluation approaches have been instrumental in enabling research to advance state of the art methods for remote sensing applications in urban 3D modeling. Most publicly available benchmark datasets have consisted of high resolution airborne imagery and lidar suitable for 3D modeling on a relatively modest scale. To enable research in larger scale 3D mapping, we have recently released a public benchmark dataset with multi-view commercial satellite imagery and metrics to compare 3D point clouds with lidar ground truth. We now define a more complete metric evaluation pipeline developed as publicly available open source software to assess semantically labeled 3D models of complex urban scenes derived from multi-view commercial satellite imagery. Evaluation metrics in our pipeline include horizontal and vertical accuracy and completeness, volumetric completeness and correctness, perceptual quality, and model simplicity. Sources of ground truth include airborne lidar and overhead imagery, and we demonstrate a semi-automated process for producing accurate ground truth shape files to characterize building footprints. We validate our current metric evaluation pipeline using 3D models produced using open source multi-view stereo methods. Data and software is made publicly available to enable further research and planned benchmarking activities.
IMPACT ASSESSMENT OF MIKANIA MICRANTHA ON LAND COVER AND MAXENT MODELING TO PREDICT ITS POTENTIAL INVASION SITES
Authors: Baidar T. , Shrestha A. B. , Ranjit R. , Adhikari R. , Ghimire S. , Shrestha N.
Published: June 2017

Abstract. Mikania micrantha is one of the major invasive alien plant species in tropical moist forest regions of Asia including Nepal. Recently, this weed is spreading at an alarming rate in Chitwan National Park (CNP) and threatening biodiversity. This paper aims to assess the impacts of Mikania micrantha on different land cover and to predict potential invasion sites in CNP using Maxent model. Primary data for this were presence point coordinates and perceived Mikania micrantha cover collected through systematic random sampling technique. Rapideye image, Shuttle Radar Topographic Mission data and bioclimatic variables were acquired as secondary data. Mikania micrantha distribution maps were prepared by overlaying the presence points on image classified by object based image analysis. The overall accuracy of classification was 90 % with Kappa coefficient 0.848. A table depicting the number of sample points in each land cover with respective Mikania micrantha coverage was extracted from the distribution maps to show the impact. The riverine forest was found to be the most affected land cover with 85.98 % presence points and sal forest was found to be very less affected with only 17.02 % presence points. Maxent modeling predicted the areas near the river valley as the potential invasion sites with statistically significant Area Under the Receiver Operating Curve (AUC) value of 0.969. Maximum temperature of warmest month and annual precipitation were identified as the predictor variables that contribute the most to Mikania micrantha's potential distribution.
STUDY ON HIGH RESOLUTION MEMBRANE-BASED DIFFRACTIVE OPTICAL IMAGING ON GEOSTATIONARY ORBIT
Authors: Jiao J. , Wang B. , Wang C. , Zhang Y. , Jin J. , Liu Z. , Su Y. , Ruan N.
Published: June 2017

Abstract. Diffractive optical imaging technology provides a new way to realize high resolution earth observation on geostationary orbit. There are a lot of benefits to use the membrane-based diffractive optical element in ultra-large aperture optical imaging system, including loose tolerance, light weight, easy folding and unfolding, which make it easy to realize high resolution earth observation on geostationary orbit. The implementation of this technology also faces some challenges, including the configuration of the diffractive primary lens, the development of high diffraction efficiency membrane-based diffractive optical elements, and the correction of the chromatic aberration of the diffractive optical elements. Aiming at the configuration of the diffractive primary lens, the “6+1” petal-type unfold scheme is proposed, which consider the compression ratio, the blocking rate and the development complexity. For high diffraction efficiency membrane-based diffractive optical element, a self-collimating method is proposed. The diffraction efficiency is more than 90 % of the theoretical value. For the chromatic aberration correction problem, an optimization method based on schupmann is proposed to make the imaging spectral bandwidth in visible light band reach 100 nm. The above conclusions have reference significance for the development of ultra-large aperture diffractive optical imaging system.
CHANGE DETECTION VIA SELECTIVE GUIDED CONTRASTING FILTERS
Authors: Vizilter Y. V. , Rubis A. Y. , Zheltov S. Y.
Published: June 2017

Abstract. Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented filters provide the robustness relative to weak geometrical discrepancy of compared images. Selective guided contrasting based on morphological opening/closing and thresholded morphological correlation demonstrates the best change detection result.
RELATIVE CAMERA POSE ESTIMATION METHOD USING OPTIMIZATION ON THE MANIFOLD
Authors: Cheng C. , Hao X. , Li J.
Published: June 2017

Abstract. To solve the problem of relative camera pose estimation, a method using optimization with respect to the manifold is proposed. Firstly from maximum-a-posteriori (MAP) model to nonlinear least squares (NLS) model, the general state estimation model using optimization is derived. Then the camera pose estimation model is applied to the general state estimation model, while the parameterization of rigid body transformation is represented by Lie group/algebra. The jacobian of point-pose model with respect to Lie group/algebra is derived in detail and thus the optimization model of rigid body transformation is established. Experimental results show that compared with the original algorithms, the approaches with optimization can obtain higher accuracy both in rotation and translation, while avoiding the singularity of Euler angle parameterization of rotation. Thus the proposed method can estimate relative camera pose with high accuracy and robustness.
OBLIQUE PHOTOGRAMMETRY SUPPORTING 3D URBAN RECONSTRUCTION OF COMPLEX SCENARIOS
Authors: Toschi I. , Ramos M. M. , Nocerino E. , Menna F. , Remondino F. , Moe K. , Poli D. , Legat K. , Fassi F.
Published: June 2017

Abstract. Accurate 3D city models represent an important source of geospatial information to support various “smart city” applications, such as space management, energy assessment, 3D cartography, noise and pollution mapping as well as disaster management. Even though remarkable progress has been made in recent years, there are still many open issues, especially when it comes to the 3D modelling of complex urban scenarios like historical and densely-built city centres featuring narrow streets and non-conventional building shapes. Most approaches introduce strong building priors/constraints on symmetry and roof typology that penalize urban environments having high variations of roof shapes. Furthermore, although oblique photogrammetry is rapidly maturing, the use of slanted views for façade reconstruction is not completely included in the reconstruction pipeline of state-of-the-art software. This paper aims to investigate state-of-the-art methods for 3D building modelling in complex urban scenarios with the support of oblique airborne images. A reconstruction approach based on roof primitives fitting is tested. Oblique imagery is then exploited to support the manual editing of the generated building models. At the same time, mobile mapping data are collected at cm resolution and then integrated with the aerial ones. All approaches are tested on the historical city centre of Bergamo (Italy).
GEOSPATIAL DATA PROCESSING FOR 3D CITY MODEL GENERATION, MANAGEMENT AND VISUALIZATION
Authors: Toschi I. , Nocerino E. , Remondino F. , Revolti A. , Soria G. , Piffer S.
Published: June 2017

Abstract. Recent developments of 3D technologies and tools have increased availability and relevance of 3D data (from 3D points to complete city models) in the geospatial and geo-information domains. Nevertheless, the potential of 3D data is still underexploited and mainly confined to visualization purposes. Therefore, the major challenge today is to create automatic procedures that make best use of available technologies and data for the benefits and needs of public administrations (PA) and national mapping agencies (NMA) involved in “smart city” applications. The paper aims to demonstrate a step forward in this process by presenting the results of the SENECA project (Smart and SustaiNablE City from Above – http://seneca.fbk.eu). State-of-the-art processing solutions are investigated in order to (i) efficiently exploit the photogrammetric workflow (aerial triangulation and dense image matching), (ii) derive topologically and geometrically accurate 3D geo-objects (i.e. building models) at various levels of detail and (iii) link geometries with non-spatial information within a 3D geo-database management system accessible via web-based client. The developed methodology is tested on two case studies, i.e. the cities of Trento (Italy) and Graz (Austria). Both spatial (i.e. nadir and oblique imagery) and non-spatial (i.e. cadastral information and building energy consumptions) data are collected and used as input for the project workflow, starting from 3D geometry capture and modelling in urban scenarios to geometry enrichment and management within a dedicated webGIS platform.
MULTI-TEMPORAL LAND COVER CLASSIFICATION WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS
Authors: Rußwurm M. , Körner M.
Published: June 2017

Abstract. Land cover classification (LCC) is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN), with a classical non-temporal convolutional neural network (CNN) model and an additional support vector machine (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.
SHUTTER-LESS TEMPERATURE-DEPENDENT CORRECTION FOR UNCOOLED THERMAL CAMERA UNDER FAST CHANGING FPA TEMPERATURE
Authors: Lin D. , Westfeld P. , Maas H.-G.
Published: June 2017

Abstract. Conventional temperature-dependant correction methods for uncooled cameras are not so valid for images under the condition of fast changing FPA temperature as usual, therefore, a shutter-less temperature-dependant correction method is proposed here to compensate for these errors and stabilize camera's response only related to the object surface temperature. Firstly, sequential images are divided into the following three categories according to the changing speed of FPA temperature: stable (0°C/min), relatively stable (<0.5°C/min), unstable (>0.5°C/min). Then all of the images are projected into the same level using a second order polynomial relation between FPA temperatures and gray values from stable images. Next, a third order polynomial relation between temporal differences of FPA temperatures and the above corrected images is implemented to eliminate the deviation caused by fast changing FPA temperature. Finally, radiometric calibration is applied to convert image gray values into object temperature values. Experiment results show that our method is more effective for fast changing FPA temperature data than FLIR GEV.
STUDY OF THE INTEGRATION OF LIDAR AND PHOTOGRAMMETRIC DATASETS BY IN SITU CAMERA CALIBRATION AND INTEGRATED SENSOR ORIENTATION
Authors: Mitishita E. , Costa F. , Martins M.
Published: June 2017

Abstract. Photogrammetric and Lidar datasets should be in the same mapping or geodetic frame to be used simultaneously in an engineering project. Nowadays direct sensor orientation is a common procedure used in simultaneous photogrammetric and Lidar surveys. Although the direct sensor orientation technologies provide a high degree of automation process due to the GNSS/INS technologies, the accuracies of the results obtained from the photogrammetric and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to verify the importance of the in situ camera calibration and Integrated Sensor Orientation without control points to increase the accuracies of the photogrammetric and LIDAR datasets integration. The horizontal and vertical accuracies of photogrammetric and Lidar datasets integration by photogrammetric procedure improved significantly when the Integrated Sensor Orientation (ISO) approach was performed using Interior Orientation Parameter (IOP) values estimated from the in situ camera calibration. The horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE) of the 3D discrepancies from the Lidar check points, increased around of 37% and 198% respectively.
AN AUTOMATED ROAD ROUGHNESS DETECTION FROM MOBILE LASER SCANNING DATA
Authors: Kumar P. , Angelats E.
Published: June 2017

Abstract. Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.
Brief Communication: Drought Likelihood for East Africa
Authors: Yang Hui , Huntingford Chris
Published: June 2017

Abstract. The on-going effects of severe drought in East Africa are causing high levels of malnutrition, hunger, illness and death. Close to 16 million people across Somalia, Ethiopia and Kenya need food, water and medical assistance (DEC, 2017). Many factors influence drought stress and ability to respond. However, inevitably it is asked: are elevated atmospheric greenhouse gas (GHG) concentrations altering the likelihood of extreme rainfall deficits? We find small increases in probability of this for East African, based on merging the observation-based reanalysis dataset by the European Centre for Medium-Range Weather Forecasts (ECMWF) (Dee et al., 2011) with Global Climate Models (GCMs) in the CMIP5 database (Taylor et al., 2012).
Improving gridded snow water equivalent products in British
Columbia, Canada: multi-source data fusion by neural network
models
Authors: Snauffer Andrew , Hsieh William , Cannon Alex , Schnorbus Markus
Published: June 2017

Abstract. Estimates of surface snow water equivalent (SWE) in alpine regions with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC: ERA-Interim/Land, GLDAS-2, MERRA, MERRA-Land, GlobSnow and ERA-Interim. Relevant spatiotemporal covariates including survey date, year, latitude, longitude, elevation and grid cell elevation differences were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and correlations for April surveys were found using cross validation. The ANN using the three best performing SWE products (ANN3) had the lowest mean station MAE across the entire province, improving on the performance of individual products by an average of 53 %. Mean station MAEs and April survey correlations were also found for each of BC’s five physiographic regions. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all regions except for the BC Plains, which has relatively few stations and much lower accumulations than other regions. Subsequent comparisons of the ANN results with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to be superior over the entire VIC domain and within most physiographic regions. The superior performance of the ANN over individual products, product means, MLR and VIC was found to be statistically significant across the province.
Atmospheric turbulence affects wind turbine nacelle transfer functions
Authors: St. Martin Clara M. , Lundquist Julie K. , Clifton Andrew , Poulos Gregory S. , Schreck Scott J.
Published: June 2017

Abstract. Despite their potential as a valuable source of individual turbine power performance and turbine array energy production optimization information, nacelle-mounted anemometers have often been neglected because complex flows around the blades and nacelle interfere with their measurements. This work quantitatively explores the accuracy of and potential corrections to nacelle anemometer measurements to determine the degree to which they may be useful when corrected for these complex flows, particularly for calculating annual energy production (AEP) in the absence of other meteorological data. Using upwind meteorological tower measurements along with nacelle-based measurements from a General Electric (GE) 1.5sle model, we calculate empirical nacelle transfer functions (NTFs) and explore how they are impacted by different atmospheric and turbulence parameters. This work provides guidelines for the use of NTFs for deriving useful wind measurements from nacelle-mounted anemometers. Corrections to the nacelle anemometer wind speed measurements can be made with NTFs and used to calculate an AEP that comes within 1 % of an AEP calculated with upwind measurements. We also calculate unique NTFs for different atmospheric conditions defined by temperature stratification as well as turbulence intensity, turbulence kinetic energy, and wind shear. During periods of low stability as defined by the Bulk Richardson number (RB), the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of high stability at some wind speed bins below rated speed, leading to a steeper NTF during periods of low stability. Similarly, during periods of high turbulence, the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of low turbulence at most wind bins between cut-in and rated wind speed. Based on these results, we suggest different NTFs be calculated for different regimes of atmospheric stability and turbulence for power performance validation purposes.
Visual Outcomes following Descemet Stripping Automated Endothelial Keratoplasty for Corneal Endothelial Dysfunction
Authors: Guechi Oualid , Lhuillier Louis , Houmad Naila , Goetz Christophe , Ouamara Nadia , Perone Jean Marc
Published: June 2017

Objective

To assess objective and subjective visual outcomes achieved by patients with corneal endothelial dysfunction who have undergone surgical treatment with Descemet stripping automated endothelial keratoplasty (DSAEK).

Methods

A total of 40 eyes of 36 patients with corneal endothelial dysfunction underwent DSAEK. All were followed up for 1 year. Visual acuity (VA; logMAR), mean endothelial cell density (MCD; via noncontact specular microscopy), and topography assessment were performed at baseline (preoperatively). Visual acuity and topography measurement were repeated at postoperative year 1. Subjective assessment of visual quality was evaluated using the National Eye Institute Visual Function Questionnaire (NEI-VFQ).

Results

Preoperative best-corrected VA (BCVA) was 1.29 ± 0.53 logMAR with mean optical correction of -0.33 ± 1.74 D. Postoperative BCVA, assessed after a mean of 10.8 ± 2.1 months, showed mean line gain of 7.11 ± 4.8 logMAR, with optical correction of +1.17 ± 1.54 D. Mean 6-month postoperative pachymetry was 598.2 ± 72.3 µm. Three patients experienced premature graft detachment, requiring repositioning via injection of a sterile air bubble. No other adverse events were observed. Significant improvement (p<.05 for all) in general, near, and far vision, ocular pain, difficulty in carrying out daily tasks, dependency, social life, and mental health were reflected by NEI-VFQ scores at 10.4 ± 2.1 months postoperatively versus preoperatively. No significant correlation was noted between postoperative VA line gain and NEI-VFQ-25 questionnaire score (p>0.05).

Conclusions

Descemet stripping automated endothelial keratoplasty treatment may restore corneal clarity, improve VA, and increase vision-related quality of life in patients with advanced endothelial dysfunction. Further large-scale study is needed to corroborate these findings.
Unveiling Idiopathic Macular Telangiectasia: Clinical Applications of Optical Coherence Tomography Angiography
Authors: Gonzalez Marco A. , Shechtman Diana , Haynie Jay M. , Semes Leo
Published: June 2017

Purpose

Idiopathic macular telangiectasia type 2 (IMT2) is a bilateral acquired maculopathy, with a spectrum of clinical presentations associated with inner retinal telangiectatic vascular anomalies. Cases often are underdiagnosed or misdiagnosed. Current diagnostic modalities such as spectral-domain optical coherence tomography (SD-OCT) and fluorescein angiography (FA) are valuable to the understanding of the clinicopathology. More recently, optical coherence tomography angiography (OCTA), as an emerging noninvasive technology, has been shown to be particularly useful in the assessment and management of IMT2.

Methods

Three clinical cases of IMT2 are discussed. Clinical presentation, fundus photography, FA, SD-OCT, and OCTA are presented. Each case illustrates variable presentation, staging, and associated findings related to IMT2.

Results

Optical coherence tomography angiography provides additional value when paired with traditional multimodal imaging in the assessment and management of IMT2.

Conclusions

These cases present an opportunity to demonstrate the features of the OCTA in the evaluation of vascular diseases such as IMT2. Additionally, these examples emphasize the critical importance of OCTA in the clinical diagnosis and management of IMT2.
Total Retinal Detachment Caused by a KIF11 Mutation
Authors: Riedl Jana , Voβmerbäumer Urs , Stoffelns Bernhard , Elflein Heike
Published: June 2017

Purpose

This is a case report of bilateral retinal detachment associated with KIF11 mutation.

Methods

In our university hospital, an 8-week-old patient presented with a potential bilateral congenital cataract, iris atrophy, and iridocorneal contact in the left eye. An examination revealed microcephaly and edema of the dorsa of the feet. The eye examination showed a clear lens in both eyes with a dislodged anterior chamber in the left eye with vessels drawn from the iris to the lens. A retrolental white bilateral mass with vessels was also observed. The MRI and the ultrasound revealed a potential peritoneal hyperplastic glass body. Bilateral retinal detachment was diagnosed during surgery.

Results

Due to the external appearance of the eyes (microcephaly and edema) and the bilateral retinal detachments, a test for KIF11 mutations was requested, and the results were positive.

Conclusions

There is a known association between KIF11 mutation and chorioretinopathy. The bilateral retinal detachment in the present case study has not been previously reported in the literature.