ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

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EISSN : 2194-9050
Published by: Copernicus GmbH (10.5194)
Total articles ≅ 2,957
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Latest articles in this journal

A. S. Kumar, P. L. N. Raju, S. R. Reyes
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 7-7;

A. S. Kumar
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 9-16;

This article gives an overview of capacity development (CD) elements for fast-growing geospatial technologies and their applications (GSTA). While the Earth observation data and spatial analytics software tools are getting more open and accessible, lack of skilled workforce and institutional capacities are limiting effective applications. A systematic process is essential in realising required capacity at individual and institutional levels, in maintaining and upgrading technological infrastructures, and in updating geospatial data and strengthening human resources for sustainable supply of workforce. This article discusses on different user sectors and their competencies that are expected to meet their demands. It is also important to take stock of different modes of CD along with a variety of methods to select the appropriate suitable for the different users’ groups for efficient knowledge transfer. In addition, this article describes briefly on two methods of building curriculum design of courses based on two different body of knowledge objectives. The importance of coordinated effort in CD programs is stressed for promoting GSTA for national and international flagship development programs such as the 2030 global sustainable development agenda. It briefly discusses on the issue of gender diversity and points out ways to increase the women participation in GSTA CD programs.
M. S. Gomes de Castro,
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 213-218;

Iconographic image collections are a cultural heritage that could reach a larger audience by proposing their immersive presentation in a 3D web application. Proposing a historical street view application, based on these historical images, raises issues such as the unavailability of historical 3D models of the scene and the heterogeneity and sparsity of these photographs. We propose to use the 3D city and terrain models of the current scene, as well as a 3D point cloud if available, to simultaneously reproject and blend many historical images using an image-based rendering approach. Our contributions raise significantly the number of projective textures blended per rendering pass (typically from 8 to 40) on triangular meshes (of the 3D city and terrain models) and on point clouds. As a first step to tackle diachrony artifacts, we also propose a simple point cloud classification to filter in the shader the points corresponding to building or terrain details from the points corresponding to transient objects.
D. Bulatov, B. Kottler, E. Strauss, G. Häufel, M. May, P. Helmholz, F. Mancini
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 251-258;

Building modeling from remote sensing data is essential for creating accurate 3D and 4D digital twins, especially for temperature modeling. In order to represent buildings as gap-free, visually appealing, and rich in details models, geo-typical prototypes should be represented in the scene. The sensor data and freely available OSM data are supposed to provide guidelines for best-possible matching. In this paper, the default similarity function based on intersection over union is extended by terms reflecting the similarity of elevation values, orientation towards the road, and trees in the vicinity. The goodness of fit has been evaluated by architecture experts as well as thermal simulations with a thermal image as ground truth and error measures based on mean average error, root mean square and mutual information. It could be concluded that while intersection over union measure still seems to be most preferred by architects, slightly better thermal simulation results are yielded by taking into account all similarity functions.
B. Alsadik, N. A. Abdulateef
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 25-32;

Stereo image orientation is one of the major topics in computer vision, photogrammetry, and robotics. The stereo vision problem solution represents the basic element of the multi-view Structure from Motion SfM in computer vision and photogrammetry.A successfully reconstructed stereo image geometry is based on solving the epipolar constraint using the fundamental matrix which is based on the projective geometry in computer vision. However, in photogrammetry, the problem is well known as relative orientation and there is a different solution that is based on the euclidean geometry using collinearity or coplanarity equations.A lot of literature and discussions were found in the last decades to solve the epipolar geometry problem. However, there is still no clear description to compare between solutions introduced using both projective and euclidean solutions and which method of the relative image orientation is mostly preferred.To the best of our knowledge, computing and plotting the epipolar lines using photogrammetric collinearity and coplanarity equations is not shown before in the educational litrature. In this paper, a detailed mathematical solution of the epipolar geometry will be shown using both photogrammetric and computer vision techniques. This is aimed to remove any confusion for new learners in using the current methods in both scientific fields and show that using any technique should lead to comparable results with advantages and disadvantages.
Q. Qin, S. Xu, M. Du,
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 83-90;

Urban Functional Zone (UFZ) identification facilitates the understanding of urban systems, which are complex and huge, and helps promote sustainable urban development. Existing studies on UFZ identification with Points of Interests (POIs) have focused much on more accurately extracting functional semantics, but ignored the fine delineation of UFZs in the spatial domain. The fine delineation of the spatial units of UFZs is also a key issue in UFZ identification. Since the sizes of UFZs can be different in practice, it is difficult to delineate spatially heterogeneous UFZs on a fixed scale. To solve the issue, a novel multi-scale spatial segmentation method was proposed in this study. Through taking the homogeneous socio-economic attributes of UFZs into account, we firstly generated a number of multi-scale spatial units by computing the mixed degree of POIs types, which reflects the mixed functions of each UFZs, using information entropy. Subsequently, we constructed the urban functional corpus of each spatial unit by measuring the spatial distribution pattern of POIs. The Word2Vec model was employed to obtain the semantic embedding vectors of UFZs, following which we adopted cosine distance-based K-means clustering method to group similar UFZs into one cluster. Finally, the enrichment factor was used to help annotate each functional cluster with a specific label. The UFZ identification results were compared with the Baidu e-maps and Baidu street view images for evaluation, and an accuracy of 82.7% was obtained. This study considering the heterogeneous distribution of POIs supports the fine-grained identification of UFZs, providing reference for urban planning.
D. Ilie, O. L. Balotă, D. Iordan, P. S. Nicoară
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 181-188;

The LiDAR point clouds are usually processed in Universal Transvers Mercator projection. The transformation to a national coordinate system is frequently made with low accuracy with the help of the generic transformation implemented in the actual softwares. An accurate and precise transformation for LiDAR files in the national coordinate systems of Romania (planimetric system Stereographic 1970 and altimetric system Black Sea 1975) is not yet available. The National Center for Cartography (NCC) from Romania developed a software for precise transformation, but it works only for certain patterns of the text files and for a maximum number of points of about 1 million. Because of this, the use of point clouds in precision work was not possible, using only extracts of low-density grid points in text format. In this research we develop an algorithm which use the precise transformation of NCC to realise an accurate transformation of the LiDAR point clouds in the national coordinate systems. The algorithm is then implemented in an innovative software to transform the LiDAR *LAS files, using the common version 1.2. The software is a batch processing application, which it can process big LiDAR data without blocking. Moreover, the application is capable to apply with accuracy and precision the last published national quasigeoid to the LiDAR point files. In the end, the obtained LiDAR point cloud are more suitable to be used in any domain, because of the accurate and precise transformation in the Romanian coordinate systems.
A. K. Adeleke, R. M. Gebashe
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 145-151;

African cities are rapidly experiencing an increase in population, thereby making it difficult to attain self-sustainability. Traffic congestion is a major contributing factor to this issue. Johannesburg's inner-city fits this profile, with an increasing decline in economic and social activities, and quality of life due to traffic congestion. Furthermore, the lack of a road transport infrastructure geodatabase and traffic data in these cities makes it more difficult for stakeholders to make an informed decision on how to effectively manage roads prone to traffic congestion or due for infrastructure upgrade. This paper focuses on developing a geodatabase using factors that cause traffic congestion such as bus stops, traffic lights, speed humps, t-joints, cross joints, street parking, and others. These factors were investigated on some selected roads within the Johannesburg inner-city by enumerating the number of such factors existing on each road with the aid of high-resolution aerial imagery. The developed geodatabase becomes a tool that can support the decision-making process in solving traffic congestion by querying the geodatabase to select roads that are prone to traffic congestions depending on the number of factors occurring along a road.
X. Shi, H. Zhang, W. Yuan, D. Huang, Z. Guo, R. Shibasaki
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 91-98;

Long-term human path forecasting in crowds is critical for autonomous moving platforms (like autonomous driving cars and social robots) to avoid collision and make high-quality planning. It is not easy for prediction systems to successfully take into account social interactions and predict a distribution of future possible path in a highly interactive and dynamic circumstance. In this paper, we develop a data-driven model for long-term trajectory prediction, which naturally takes into account social interactions through a spatio-temporal graph representation and predicts multi-modes of future trajectories. Different from generative adversarial network (GAN) based models which generate samples and then provide distributions of samples, we use mixture density functions to describe human motion and intuitively map the distribution of future path with explicit densities. To prevent the model from collapsing into a single mode and truly capture the intrinsic multi-modality, we further use a Winner-Takes-All (WTA) loss instead of computing loss over all modes. Extensive experiments over several trajectory prediction benchmarks demonstrate that our method is able to capture the multi-modality of human motion and forecast the distributions of plausible futures in complex scenarios.
A. Le Guilcher, A.-M. Olteanu-Raimond, M. B. Balde
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp 99-106;

Importing spatial open data in OpenStreetMap (OSM) project, is a practice that has existed from the beginning of the project. The rapid development and multiplication of collaborative mapping tools and open data have led to the growth of the phenomenon of importing massive data into OSM. The goal of this paper is to study the evolution of the massive imports over time. We propose an approach in three steps: classification of the sources used to edit features in the OSM platform including those massively imported, classification of modifications, and identification of evolution patterns. The approach is mixing global analysis (i.e. sources and modifications are classified) and feature based analysis (i.e. imported features are analyzed with respect to their evolution over time). The approach is applied on three datasets coming from OSM considered for their heterogeneity in terms of complexity, imports, and spatial and temporal characteristics. The results show that there is a sustained activity of edition on imported features, with a ratio between geometry editions and semantic editions depending on the type of the features, with roads being the features concentrating the most activity.
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