scholarly journals CHANGING THE PRODUCTION PIPELINE – USE OF OBLIQUE AERIAL CAMERAS FOR MAPPING PURPOSES

2016 ◽  
Vol XLI-B4 ◽  
pp. 631-637 ◽  
Author(s):  
K. Moe ◽  
I. Toschi ◽  
D. Poli ◽  
F. Lago ◽  
C. Schreiner ◽  
...  
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Reference Data ◽  
Ground Truth ◽  
Building Extraction ◽  
Camera System ◽  
Ground Truth Data ◽  
Production Pipeline ◽  
Dense Image ◽  
Oblique Images

This paper discusses the potential of current photogrammetric multi-head oblique cameras, such as UltraCam Osprey, to improve the efficiency of standard photogrammetric methods for surveying applications like inventory surveys and topographic mapping for public administrations or private customers. <br><br> In 2015, Terra Messflug (TM), a subsidiary of Vermessung AVT ZT GmbH (Imst, Austria), has flown a number of urban areas in Austria, Czech Republic and Hungary with an UltraCam Osprey Prime multi-head camera system from Vexcel Imaging. In collaboration with FBK Trento (Italy), the data acquired at Imst (a small town in Tyrol, Austria) were analysed and processed to extract precise 3D topographic information. The Imst block comprises 780 images and covers an area of approx. 4.5 km by 1.5 km. Ground truth data is provided in the form of 6 GCPs and several check points surveyed with RTK GNSS. Besides, 3D building data obtained by photogrammetric stereo plotting from a 5 cm nadir flight and a LiDAR point cloud with 10 to 20 measurements per m² are available as reference data or for comparison. The photogrammetric workflow, from flight planning to Dense Image Matching (DIM) and 3D building extraction, is described together with the achieved accuracy. For each step, the differences and innovation with respect to standard photogrammetric procedures based on nadir images are shown, including high overlaps, improved vertical accuracy, and visibility of areas masked in the standard vertical views. Finally the advantages of using oblique images for inventory surveys are demonstrated.

scholarly journals CHANGING THE PRODUCTION PIPELINE – USE OF OBLIQUE AERIAL CAMERAS FOR MAPPING PURPOSES

2016 ◽  
Vol XLI-B4 ◽  
pp. 631-637
Author(s):  
K. Moe ◽  
I. Toschi ◽  
D. Poli ◽  
F. Lago ◽  
C. Schreiner ◽  
...  
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Reference Data ◽  
Ground Truth ◽  
Building Extraction ◽  
Camera System ◽  
Ground Truth Data ◽  
Production Pipeline ◽  
Dense Image ◽  
Oblique Images

This paper discusses the potential of current photogrammetric multi-head oblique cameras, such as UltraCam Osprey, to improve the efficiency of standard photogrammetric methods for surveying applications like inventory surveys and topographic mapping for public administrations or private customers. <br><br> In 2015, Terra Messflug (TM), a subsidiary of Vermessung AVT ZT GmbH (Imst, Austria), has flown a number of urban areas in Austria, Czech Republic and Hungary with an UltraCam Osprey Prime multi-head camera system from Vexcel Imaging. In collaboration with FBK Trento (Italy), the data acquired at Imst (a small town in Tyrol, Austria) were analysed and processed to extract precise 3D topographic information. The Imst block comprises 780 images and covers an area of approx. 4.5 km by 1.5 km. Ground truth data is provided in the form of 6 GCPs and several check points surveyed with RTK GNSS. Besides, 3D building data obtained by photogrammetric stereo plotting from a 5 cm nadir flight and a LiDAR point cloud with 10 to 20 measurements per m² are available as reference data or for comparison. The photogrammetric workflow, from flight planning to Dense Image Matching (DIM) and 3D building extraction, is described together with the achieved accuracy. For each step, the differences and innovation with respect to standard photogrammetric procedures based on nadir images are shown, including high overlaps, improved vertical accuracy, and visibility of areas masked in the standard vertical views. Finally the advantages of using oblique images for inventory surveys are demonstrated.

scholarly journals COMBINING AIRBORNE OBLIQUE CAMERA AND LIDAR SENSORS: INVESTIGATION AND NEW PERSPECTIVES

2018 ◽  
Vol XLII-1 ◽  
pp. 437-444 ◽  
Author(s):  
I. Toschi ◽  
F. Remondino ◽  
R. Rothe ◽  
K. Klimek
Keyword(s):  
Ground Truth ◽  
Point Clouds ◽  
Image Sensors ◽  
Lidar Data ◽  
Imaging Data ◽  
Ground Truth Data ◽  
Dense Image ◽  
Integrated Processing ◽  
Urban Mapping ◽  
Oblique Images

<p><strong>Abstract.</strong> Hybrid sensor solutions, that feature active laser and passive image sensors on the same platform, are rapidly entering the airborne market of topographic and urban mapping, offering new opportunities for an improved quality of geo-spatial products. In this perspective, a concurrent acquisition of LiDAR data and oblique imagery, seems to have all the potential to lead the airborne (urban) mapping sector a step forward. This contribution focuses on the first commercial example of such an integrated, all-in-one mapping solution, namely the Leica CityMapper hybrid sensor. By analysing two CityMapper datasets acquired over the city of Heilbronn (Germany) and Bordeaux (France), the paper investigates potential and challenges, w.r.t. (i) number and distribution of tie points between nadir and oblique images, (ii) strategy for image aerial triangulation (AT) and accuracy achievable w.r.t ground truth data, (iii) local noise level and completeness of dense image matching (DIM) point clouds w.r.t LiDAR data. Solutions for an integrated processing of the concurrently acquired ranging and imaging data are proposed, that open new opportunities for exploiting the real potential of both data sources.</p>

A Digital Terrain Modeling Method in Urban Areas by the ICESat-2 (Generating precise terrain surface profiles from photon-counting technology)

2021 ◽  
Vol 87 (4) ◽  
pp. 237-248
Author(s):  
Nahed Osama ◽  
Bisheng Yang ◽  
Yue Ma ◽  
Mohamed Freeshah
Keyword(s):  
Urban Areas ◽  
Spatial Clustering ◽  
Photon Counting ◽  
Ground Surface ◽  
Ground Truth ◽  
Surface Model ◽  
Laser Altimeter ◽  
Ground Truth Data ◽  
Urban Scenes ◽  
Terrain Surface

The ICE, Cloud and land Elevation Satellite-2 (ICES at-2) can provide new measurements of the Earth's elevations through photon-counting technology. Most research has focused on extracting the ground and the canopy photons in vegetated areas. Yet the extraction of the ground photons from urban areas, where the vegetation is mixed with artificial constructions, has not been fully investigated. This article proposes a new method to estimate the ground surface elevations in urban areas. The ICES at-2 signal photons were detected by the improved Density-Based Spatial Clustering of Applications with Noise algorithm and the Advanced Topographic Laser Altimeter System algorithm. The Advanced Land Observing Satellite-1 PALSAR –derived digital surface model has been utilized to separate the terrain surface from the ICES at-2 data. A set of ground-truth data was used to evaluate the accuracy of these two methods, and the achieved accuracy was up to 2.7 cm, which makes our method effective and accurate in determining the ground elevation in urban scenes.

scholarly journals Automated Annotation of Cell Identities in Dense Cellular Images

2020 ◽  
Author(s):  
Shivesh Chaudhary ◽  
Sol Ah Lee ◽  
Yueyi Li ◽  
Dhaval S. Patel ◽  
Hang Lu
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Gene Expression Pattern ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Automated Annotation ◽  
Expression Pattern Analysis ◽  
Dense Image ◽  
Active Data ◽  
Imaging Conditions

AbstractAssigning cell identities in dense image stacks is critical for many applications, for comparing data across animals and experiment conditions, and investigating properties of specific cells. Conventional methods are laborious, require experience, and could introduce bias. We present a generalizable framework based on Conditional Random Fields models for automatic cell identification. This approach searches for optimal arrangements of labels that maximally preserves prior knowledge such as geometrical relationships. The algorithm shows better accuracy and more robust handling of perturbations, e.g. missing cells and position variability, with both synthetic and experimental ground-truth data. The framework is generalizable across strains, imaging conditions, and easily builds and utilizes active data-driven atlases, which further improves accuracy. We demonstrate the utility in gene-expression pattern analysis, multi-cellular calcium imaging, and whole-brain imaging experiments. Thus, our framework is highly valuable to a wide variety of annotation scenarios including in zebrafish, Drosophila, hydra, and mouse brains.

scholarly journals A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1207-1211
Author(s):  
N. Soyama ◽  
K. Muramatsu ◽  
M. Daigo ◽  
F. Ochiai ◽  
N. Fujiwara
Keyword(s):  
Land Cover ◽  
Reference Data ◽  
Ground Truth ◽  
Plant Functional Type ◽  
Data Sets ◽  
Functional Type ◽  
Reference Dataset ◽  
Ground Truth Data ◽  
Global Land Cover ◽  
Global Land

Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers.

scholarly journals QUALITY FEATURES FOR THE INTEGRATION OF TERRESTRIAL AND UAV IMAGES

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 339-346 ◽  
Author(s):  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Point Cloud ◽  
Ground Truth ◽  
Internal Quality ◽  
3D Reconstructions ◽  
Ground Truth Data ◽  
Dense Point ◽  
Modena Cathedral ◽  
Uav Images ◽  
Filtering Procedure ◽  
Quality Features

<p><strong>Abstract.</strong> The paper presents an innovative approach for improving the orientation results when terrestrial and UAV images are jointly processed. With the existing approaches, the processing of images coming from different platforms and sensors leads often to noisy and inaccurate 3D reconstructions, due to the different nature and properties of the acquired images. In this work, a photogrammetric pipeline is proposed to filter and remove bad computed tie points, according to some quality feature indicators. A completely automatic procedure has been developed to filter the sparse point cloud, in order to improve the orientation results before computing the dense point cloud. We report some tests and results on a dataset of about 140 images (Modena cathedral, Italy). The effectiveness of the filtering procedure was verified using some internal quality indicators, external checks (ground truth data) and qualitative visual analyses.</p>

scholarly journals POI Mining for Land Use Classification: A Case Study

2020 ◽  
Vol 9 (9) ◽  
pp. 493 ◽  
Author(s):  
Renato Andrade ◽  
Ana Alves ◽  
Carlos Bento
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Smart Cities ◽  
Ground Truth ◽  
Land Use Classification ◽  
Data Types ◽  
Ground Truth Data ◽  
Functional Roles ◽  
Artificial Neural Network Ann

The modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on land use and the functional roles of the places that integrate urban areas. In the last few years, driven by the increased availability of geo-referenced data from social media, embedded sensors, and remote sensing images, various techniques have become popular for land use analysis. In this paper, we first highlight and discuss the different data types and methods usually adopted in this context, as well as their purposes. Then, based on a systematic state-of-the-art study, we focused on exploring the potential of points of interest (POIs) for land use classification, as one of the most common categories of crowdsourced data. We developed an application to automatically collect POIs for the study area, creating a dataset that was used to generate a large number of features. We used a ranking technique to select, among them, the most suitable features for classifying land use. As ground truth data, we used CORINE Land Cover (CLC), which is a solid and reliable dataset available for the whole European territory. It was used an artificial neural network (ANN) in different scenarios and our results reveal values of more than 90% for the accuracy and F-score in one experiment performed. Our analysis suggests that POI data have promising potential to characterize geographic spaces. The work described here aims to provide an alternative to the current methodologies for land use and land cover (LULC) classification, which are usually time-consuming and depend on expensive data types.

scholarly journals Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

2021 ◽  
Vol 13 (9) ◽  
pp. 1697
Author(s):  
Alexander Jenal ◽  
Hubert Hüging ◽  
Hella Ellen Ahrends ◽  
Andreas Bolten ◽  
Jens Bongartz ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Near Infrared ◽  
Imaging System ◽  
N Uptake ◽  
Ground Truth ◽  
Short Wave ◽  
Camera System ◽  
Ground Truth Data ◽  
Single Observation ◽  
Wheat Field

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.

scholarly journals An Urban Flooding Index for Unsupervised Inundated Urban Area Detection Using Sentinel-1 Polarimetric SAR Images

2021 ◽  
Vol 13 (22) ◽  
pp. 4511
Author(s):  
Hui Zhang ◽  
Zhixin Qi ◽  
Xia Li ◽  
Yimin Chen ◽  
Xianwei Wang ◽  
...  
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
Ground Truth ◽  
Detection Accuracy ◽  
Urban Flooding ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Ground Truth Data ◽  
Optical Images ◽  
Coefficient Increase

Urban flooding causes a variation in radar return from urban areas. However, such variation has not been thoroughly examined for different polarizations because of the lack of polarimetric SAR (PolSAR) images and ground truth data simultaneously collected over flooded urban areas. This condition hinders not only the understanding of the effect mechanism of urban flooding under different polarizations but also the development of advanced methods that could improve the accuracy of inundated urban area detection. Using Sentinel-1 PolSAR and Jilin-1 high-resolution optical images acquired on the same day over flooded urban areas in Golestan, Iran, this study investigated the characteristics and mechanisms of the radar return changes induced by urban flooding under different polarizations and proposed a new method for unsupervised inundated urban area detection. This study found that urban flooding caused a backscattering coefficient increase (BCI) and interferometric coherence decrease (ICD) in VV and VH polarizations. Furthermore, VV polarization was more sensitive to the BCI and ICD than VH polarization. In light of these findings, the ratio between the BCI and ICD was defined as an urban flooding index (UFI), and the UFI in VV polarization was used for the unsupervised detection of flooded urban areas. The overall accuracy, detection accuracy, and false alarm rate attained by the UFI-based method were 96.93%, 91.09%, and 0.95%, respectively. Compared with the conventional unsupervised method based on the ICD and that based on the fusion of backscattering coefficients and interferometric coherences (FBI), the UFI-based method achieved higher overall accuracy. The performance of VV was evaluated and compared to that of VH in the flooded urban area detection using the UFI-, ICD-, and FBI-based methods, respectively. VV polarization produced higher overall accuracy than VH polarization in all the methods, especially in the UFI-based method. By using VV instead of VH polarization, the UFI-based method improved the detection accuracy by 38.16%. These results indicated that the UFI-based method improved flooded urban area detection by synergizing the BCI and ICD in VV polarization.

scholarly journals ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 489-492
Author(s):  
S. A. R. Hosseini ◽  
H. Gholami ◽  
Y. Esmaeilpoor
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Ground Truth ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Sensing Technology ◽  
Time Period

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.

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