Rapid and uncontrolled expansion of cities has led to unplanned aerial development. In this way, modeling and predicting the urban growth of a city helps decision-makers. In this study, the aspect of sustainable urban development has been studied for the city of Mashhad. In general, the prediction of urban aerial development is one of the most important topics of modern town management. In this research, using the Cellular Automaton (CA) model developed for geo data of Geographic Information Systems (GIS) and presenting a simple and powerful model, a simulation of complex urban processes has been done.

The aim of the article. This article highlights the significance of forest cover as an important indicator of the state of the environment. It discusses the findings of the Food and Agriculture Organization of the United Nations (FAO) Forest Resources Assessment (FRA) 2020 report, which states that the world's forest area has decreased by 178 million hectares since 1990. The case study of Volyn region shows how cloud processing and vegetation classification can help quantify forest dynamics from 2000 to 2020, allowing local authorities and decision makers to monitor and analyze trends in near real time. Overall, this work provides insights into the importance of monitoring forest dynamics and the potential for remote sensing technology to facilitate this process. Data & Methods. Remote sensing is an effective tool for monitoring forest ecology and management, and Google Earth Engine (GEE) is an online platform that combines data from various agencies to analyze environmental data. The article presents a case study of the Volyn region and how cloud processing and vegetation classification were used to assess forest dynamics from 2000 to 2020. The study used data from Landsat 7 Collection 1 Tier 1 composites and the CART algorithm for binary decision tree building. The study was based on information provided by the Main Department of Statistics in the Volyn region on the area of forests and areas where logging was carried out during the specified period. Research results. It is interesting to note that despite the decrease in logging activities, there is an increase in forest cover loss within forest ranges. This could be due to various reasons, such as illegal logging or natural disturbances like fires or disease outbreaks. The use of machine learning methods like CART classification can help to identify and monitor these changes, which can then be used to inform policy decisions and management practices to reduce forest cover loss. In general, in the Volyn region, there is a gradual decrease in the areas where various kinds of logging are carried out from 524 km2 in 2003 to 239 km2 in 2020. In contrast, forest cover loss within forest ranges increased rapidly from 37.85 km2 in 2015 to 84.01 km2 in 2017 and beyond from 5.53 km2 to 10.80 km2 in 2015 and 2017 respectively. In this study, the accuracy assessment was performed using 30% of the control points obtained initially, based on data on the reliability of the land cover. The manufacturer's accuracy and user accuracy were calculated to evaluate error omissions and possibilities of a pixel being categorized in a certain category. The spatial resolution of Landsat 7 data used in this study was 30 m, with a minimum calculation area of 0.337 hectares. The overall accuracy and the coefficient κ are the most representative measures of accuracy, with an average accuracy of classification of OAav=98.82% and κav=0.9764.

Photogrammetric point clouds offer immense potential for various applications, especially for the AEC industry and ”as-built” BIM. However, despite many advantages such as time and cost efficiency, image based point clouds of indoor environments mostly suffer from inhomogeneous and strongly fluctuating point-wise uncertainties. This lack of area-filling geometric reliability represents a strong barrier for innovations and further development of image based applications for as-built BIM, regarding both software and hardware. Therefore, this paper presents a method for the geometric verification of indoor BIMs by images and uncertainty management in order to unleash the potential of photogrammetry in context of professional building documentation heading towards ”digital twinning”. Individual 3D point accuracies, object’s surface characteristics and BIM related uncertainties according to the Level of Accuracy (LOA) specification are assessed and taken into account. The final decision of whether or not a photogrammetric point cloud confirms a given model within its associated level of accuracy results from a combined reasoning pipeline based on Dempster–Shafer evidence theory. The novel Pho-to-BIM verification method is demonstrated on three real indoor construction sites, each 3D mapped with different image sensors. Based on the experiments it is shown how to set up belief functions for evidence based reasoning individually, depending on the measurement and site characteristics.

Forest classification is needed to solve a wide range of environmental issues related to of forest classes and succession processes, the extent of afforestation and deforestation and global environmental change. These applications require a very accurate mapping and monitoring of forest types. This article investigates the combination of modern open geographic information systems and remote sensing data in forest management tasks for a specific part of the Ukrainian state area. Based on the existing afforestation plans, the results of the unsupervised classification of Sentinel-2 images and the selection of forest species fragments with closed crowns as training data for supervised classification, classifiers of forest species of the study object were developed with and without taking into account age groups. A supervised classification of research objects is realized and the accuracy of the obtained results is evaluated. It is established that the accuracy of determining forest species on the basis of the proposed method is 90.3 and 91.4%, taking into account age groups and without taking them into account, respectively. Thus, it is found that the modeling of the age groups does not improve the classification result for the test area.

Automatic construction progress documentation and metric evaluation of execution work in confined building interiors requires particularly reliable geometric evaluation and interpretation of statistically uncertain as-built point clouds. This paper presents a method for high-resolution change detection based on dense 3D point clouds from terrestrial laser scanning (TLS) and the discretization of space by voxels. In order to evaluate the metric accuracy of a BIM according to the Level of Accuracy (LOA) specification, the effects of laser range measurements on the occupancy of space are modeled with belief functions and evaluated using Dempster and Shafer's theory of evidence. The application is demonstrated on the point cloud data of multi temporal scanning campaigns of real indoor reconstructions. The results show that TLS point clouds are suitable to verify a given BIM up to LOA 40 if special attention is paid to the scanning geometry during the acquisition. The proposed method can be used to document construction progress, verify and even update the LOA status of a given BIM, confirming valid and BIM-compliant as-built models for further planning.

A detailed inspection of roads requires highly detailed spatial data with sufficient precision to deliver an accurate geometry and to describe road defects visually. This paper presents a novel method for the detection of road defects. The input data for road defect detection included point clouds and orthomosaics gathered by mobile mapping technology. The defects were categorized in three major groups with the following geometric primitives: points, lines and polygons. The method suggests the detection of point objects from matched point clouds, panoramic images and ortho photos. Defects were mapped as point, line or polygon geometries, directly derived from orthomosaics and panoramic images. Besides the geometric position of road defects, all objects were assigned to a variety of attributes: defect type, surface material, center-of-gravity, area, length, corresponding image of the defect and degree of damage. A spatial dataset comprising defect values with a matching data type was created to perform the attribute analysis quickly and correctly. The final product is a spatial vector data set, consisting of points, lines and polygons, which contains attributes with further information and geometry. This paper demonstrates that mobile mapping suits a large-scale feature extraction of road infrastructure defects. By its simplicity and flexibility, the presented methodology allows it to be easily adapted to extract further feature types with their attributes. This makes the proposed approach a vital tool for data extraction settings with multiple mobile mapping data analysts, e.g., offline crowdsourcing.

Besides the common approach of an exclusively file based management of 3D point clouds, meanwhile it is possible to store and process this special type of massive geodata within spatial database systems. Users benefit from the general advantages of database solutions and especially from the potentials of a combined analysis of original 3D point clouds, 2D rasters, 3D voxel stacks and 2D and 3D vector data in order to gain valuable geo-information. This paper describes the integration of 3D point clouds into an open source PostgreSQL/PostGIS database using the Pointcloud extension and functions of the Point Data Abstraction Library (PDAL). The focus is on performing three-dimensional spatial queries and the evaluation of different tiling methods for the organization of 3D point clouds into table rows, regarding memory space, performance of spatial queries and effects on interactions between point clouds and other GIS features within the database. A new approach for an optimized point cloud tiling, considering the individual geometric characteristic of a 3D point cloud, is presented. The results show that an individually selected storage structure for a point cloud is crucial for low memory consumption and high-performance 3D queries in PostGIS applications, taking account of its three-dimensional spatial extent and point density.

In this article, a novel, media undisruptive method for the measurement of photogrammetric test fields using a laser tracker is presented. The new approach is precise and versatile in its application. It relies on image processing on the quasi continuous measurements of a hand-held laser scanner and laser tracker combination. The field of useful applications is large. In this article, we show the benefit in the field of camera calibration. Essential for highly accurate photogrammetric measurements is a careful calibration, since all cameras have optical distortions due to manufacturing processes of the lens. The calibration can be done by e.g.  using a test field. In some cases, 3D coordinates of the control points are necessary. These coordinates are often determined by photogrammetry itself and tacheometric angle measurements in advance. A scale, e.g.  a subtense bar, usually needs to be included which increases the measuring efforts. The method bases on the measured 3D point cloud of a test field. With this technique, not only the centers of all control points are accessible. Other geometric features can be chosen too. Since the point cloud consist of many single point measurements, every control point determination has already a high statistical redundancy. The 3D coordinates of every single control point are extracted from the point cloud, making an additional scale obsolete. Presently, the position accuracy is $$\le 50\,{\upmu }{\text {m}}$$ (MPE), which is mainly limited by the laser scanner used in this article. The here-presented technique can be applied to all kinds of shapes, dimensions, materials, numbers and arrangements of control points. Furthermore, it is a lot faster and easier to handle than the angle measurements of the tacheometer.

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We present an automated approach for the dodging of images, with which we edit digital images as it is usually done with analogue images in dark-rooms. Millions of aerial images of all battle fields were taken during the Second World War. They were intensively used, e.g. for the observation of military movements, the documentation of success and failure of military operations and further planning. Today, the information of these images supports the removal of explosives of the Second World War and the identification of dangerous waste in the soil. In North Rhine-Westphalia, approximately 300.000 aerial images are scanned to handle the huge amount of available data efficiently. The scanning is done with a gray value depth of 12 bits and a pixel size of 21 μm to gain both, a high radiometric and a high geometric resolution of the images. Due to the photographic process used in the 1930s and 1940s and several reproductions, the digitized images are exposed locally very differently. Therefore, the images shall be improved by automated dodging. Global approaches mostly returned unsatisfying results. Therefore, we present a new approach, which is based on local histogram equalization. Other methods as spreading the histogram or linear transformations of the histogram manipulate the images either too much or not enough. For the implementation of our approach, we focus not only on the quality of the resulting images, but also on robustness and performance of the algorithm. Thus, the technique can also be used for other applications concerning image improvements.

In this paper we describe a new concept for the reconstruction of buildings. In contrast to most of the published approaches, we link the reconstruction process with the building interpretation. With this linkage we want to enhance the reconstruction result and to yield semantic information about the buildings. We introduce building models based on their topology. We also may use data from different sensor types. The analysis is done locally using statistical building information for the interpretation in a Markov-Random-Field and using e. g. geometric or radiometric “appearance” models for the reconstruction. A real data example from laserscanner observations demonstrates the approach.

This paper integrates Statistical reasoning and Grassmann-Cayley algebra for making 2D and 3D geometric reasoning practical. The multi-linearity of the forms allows rigorous error propagation and Statistical testing of geometric relations. This is achieved by representing all objects in homogeneous coordinates and expressing all relations using Standard matrix calculus.1