Orthophoto Images Research Articles

Analyzing the effects of 2D and 3D urban structures on LST changes using remotely sensed data

2D and 3D urban structures play an important role in characterizing Land Surface Temperature (LST). The effects of 2D urban structures on changes of LST have been studied in many studies, but less attention has been paid to the effects of 3D urban structures. Therefore, in this study, we aimed at employing LiDAR point clouds and a Landsat-8 image to investigate the effects of 2D and 3D structures on LST changes in Tartu, Estonia. To this end, LST retrieval and extraction of 2D structures were carried out using a Landsat-8 image. A 3D land cover of the study area was generated through employing random forest (RF) classifier on a joint set of features extracted from LiDAR point clouds and an orthophoto image, as a basis to identify 3D structures. The results of this study indicate a high overall accuracy (97.33%) of RF in land cover classification. Among the 2D structures, Normalized Difference Built-up Index (NDBI) had a higher correlation than Fractional Vegetation Cover (FVC) with LST. The results also indicate the significant role of height in impervious surfaces and vegetation in increasing and decreasing LST. Finally, the results showed that the hot spots emerged in areas where the mean height of impervious surfaces was higher, while the cold spots were linked to areas of higher mean vegetation heights.

Straddling the Fence: Land Use Patterns in and around Ports as Hidden Designers

Ports are clearly demarcated structures on land and water. They are fenced in, easily recognizable on satellite and orthophoto images, and they have specific functions. This apparent clarity of ports, their function and outline, in relation to nearby urban and rural areas, becomes more complex when explored through the lens of land use, that is the existing and planned future functional dimension or socio-economic purpose of the land. In contrast to urban and rural areas, where land use has been mapped and defined for centuries, the use and function of land and water in port areas has long been multifunctional and not defined on land use maps. This raises questions about the role and understanding of port territory in relation to neighboring spaces, past, and present. This article first defines land use and describes its historical development. Scholars from various disciplines, including geographers, planners, and economists, have addressed the issue of land use in port areas. Land use patterns have emerged over time and are based on earlier demarcations of port areas and distinctions between port and city. As shown by the historical port city borders in Hamburg, Rotterdam, and Koper, these delimitations can change over time, by location and by function. The land use register has only recently been harmonized at the European level. European and national registers distinguish existing and planned land use in port areas differently. Mixed uses prevail in new port interventions, creating a new kind of permeability or porosity; that is, areas where port, urban and rural functions merge. New land use porosity is a particular state of land use (on both sides of the boundaries of port areas) that goes beyond the physical boundaries marked by fences. Land use porosity effectively creates land use continuity, a functional porosity that serves as a hidden blueprint for future planning. Understanding land use porosity can provide a foundation for novel approaches to the development of transition strategies that are needed to address contemporary challenges, including climate change and sea level rise, digitization, and new work and life practices in port city regions. In conclusion, we note that due to the porosity of land use patterns, the separation between the present port and the city is beginning to crumble. However, this process has yet to be made fully visible and used as a basis for design.

Benthic habitat classification using multiscale GEOBIA on orthophoto images of Karimunjawa waters

The scale value is an important part of the segmentation stage which is part of Object-Based Image Analysis (OBIA). Selection of scale value can determine the size of the object which affects the results of classification accuracy. In addition to setting the scale value (multiscale), selection of machine learning algorithm applied to classify shallow water benthic habitat objects can also determine the success of the classification. Combination of setting scale values and classification algorithms are aimed to get optimal results by examining classification accuracies. This study uses orthophoto images processed from Unmanned Aerial Vehicle (UAV) mission intended to capture benthic habitat in Karimunjawa waters. The classification algorithms used are Support Vector Machine (SVM), Bayes, and K-Nearest Neighbors (KNN). The results of the classification of combination are then tested for accuracy based on the sample and Training Test Area (TTA) masks. The result shows that SVM algorithm with scale of 300 produces the best level of accuracy. While the lowest accuracy is achieved by using SVM algorithm with scale of 100. The result shows that the optimal scale settings in segmenting objects sequentially are 300, 200, and 100

APLIKASI DRONE-UAV UNTUK SURVEY DAN PEMETAAN MENGGUNAKAN COPTER PADA PERKEBUNAN KELAPA SAWIT (Studi Kasus : Bahruny Group, PT. Satya Agung, Lhoksemawe, Aceh)

Drone is a UAV vehicle which is currently widely used in various activities, one of which is for aerial photography (photogrammetry). The concept of efficiency is the main goal of using this drone, which is to produce detailed and up-to-date aerial photo images with adjustable area coverage, relatively short time, affordable costs and minimal personnel required. The output is an orthophoto image that already has coordinates and can be used as primary data for convenience in the tree counting process so that tree populations in blocks based on design and area statements can be known in detail and accountably. The aerial mapping process using a copter unmanned vehicle with a height of 80-meters above ground level at an image resolution of 2.23 cm/pixel produces 3,795 photos with side overlap and front overlap photos of 70% and 80% with an area covering 2.72 km2 or 272 ha. The results of the calculation of oil palm trees as many as 3,147 pkk with a statement area of 29.08 ha in Block M06 and obtained an SpH of 108 pkk which is smaller than the ideal SpH ranging from 135-143 pkk/ha so that there is a need for compaction activities or plant fulfillment in the block.

Method for Tunnel Displacements Calculation Based on Mobile Tunnel Monitoring System.

Efficient, high-precision, and automatic measurement of tunnel structural changes is the key to ensuring the safe operation of subways. Conventional manual, static, and discrete measurements cannot meet the requirements of rapid and full-section detection in subway construction and operation. Mobile laser scanning technology is the primary method for tunnel detection. Herein, we propose a method to calculate shield tunnel displacements of a full cross-section tunnel. The point cloud data, obtained via a mobile tunnel deformation detection system, were fitted, projected, and interpolated to generate an orthophoto image. Combined with the cumulative characteristics of the tunnel gray gradient, the longitudinal ring seam of the tunnel was identified, while the Canny algorithm and Hough line detection algorithm identified the transverse seam. The symmetrical vertical foot method and cross-section superposition analysis were used to calculate the circumferential and radial displacements, respectively. The proposed displacement calculation method achieves automatic recognition of a ring seam, reduces human–computer interaction, and is fast, intelligent, and accurate. Furthermore, the description of the tunnel deformation location and deformation amount is more quantitative and specific. These results confirm the significance of shield tunnel displacement monitoring based on mobile monitoring systems in tunnel disease monitoring.

Nature’s contributions to people from church forests in a fragmented tropical landscape in southern Ethiopia

Despite expanding interest in nature's contributions to people (NCP) studies, understanding of sacred natural sites’ contributions to human society and the restoration of fragmented landscapes remains relatively limited. This study examines the diversity and extent of NCP by church forests in a fragmented tropical landscape in southern Ethiopia. We identify 339 church forests in the Gurage Zone and examine them using a combination of historical (1967) aerial photographs and recent (2017) orthophoto images, supplemented by vegetation sampling and in-depth interviews with key informants from 42 selected church communities. Church forests can be found in all agro-ecological zones and across the entire vegetation types in the Gurage socio-ecological landscape. In the last five decades, the extent of church forests has been remarkably persistent, and 67% of the forests have seen an increase in size even while surrounding state- and community-controlled forestland has been degraded over time. Interview findings suggest the church forests' persistence is in large part due to the church compound being seen as a sacred space and hence respected and protected by the community. This powerful social norm has allowed for multiple uses of the church forest to continue over time through sustained forest management. More than 15 distinct contributions of church forests to local communities were identified including material, nonmaterial, and regulating NCP categories, suggesting church forests deliver a wide range of NCP in addition to their well-established ecological and conservation value. Findings underscore the current contributions of church forests to local people in southern Ethiopia, as well as the potential for church forests to support the restoration of degraded landscapes through integration into regional landscape planning and management policies.

Forest Road Detection Using Deep Learning Models

ABSTRACT Forest roads are the primary infrastructure facilities of forestry activities. Identifying the forest roads that can be used after a disaster is very important for management of disasters such as fire, flood, and landslide as well as for strengthening of the forest road infrastructure. In this context, forest road extraction from orthophoto images has become a hot research topic in the field of remote sensing image analysis. Deep learning methods stand out in many fields that require remote sensing and these methods lead to very successful results compared with traditional methods. Recently, deep learning methods are applied frequently in road extraction. The aim of the present study was forest road network extraction from high resolution orthophoto images based on deep learning. Four different deep learning models have been used in the study which are AlexNet, ResNet-50, InceptionResNet-V2 and U-Net. First, the images in the dataset were subject to re-processing after which the deep learning models were trained separately. Secondly, values of overall accuracy, precision, recall, Dice coefficient, intersection over union, and test time were calculated for these trained network models over the validity dataset. Finally, the acquired results were compared and forest road segmentation inferences were visualized thus putting forth the accuracy at which the deep learning models used can extraction the forest roads. The results show that Resnet-50 and InceptionResNet-V2 semantic segmentation models can be used accurately and efficiently for forest road extraction.

Derivation of MSL Riverbank Line from UAV DSM Data based on Tidal Slope Analysis

Deriving a riverbank line at a tidal river is complicated, since dynamic factors such as tide and slope topography influence its location. Hence, this study utilises the Unmanned Aerial Vehicle (UAV), geodetic, and tidal data to identify the exact riverbank line based on Mean Sea Level (MSL) value from UAV products. We carried out two epochs of UAV data collection during low and high tide conditions. Orthometric height (H) has been applied in UAV data processing to produce a high accuracy of orthophoto and Digital Surface Model (DSM). UAV-MSL derived riverbank lines have been identified from the UAV orthophoto image using supervised classification and feature extraction techniques. DSM model provides the topographical height in three-dimensional (3D) view and enables identifying low and high tide, MSL, and lowest astronomical tides (LAT) at the riverbank. The study found that UAV-MSL derived riverbank line was identified in between the MSL value (at 2.302 m) and chart datum (at 1.043 m). This study also identified the correlation model, R2 value at 0.9288 between UAV-MSL derived riverbank line with MSL-tide gauge data. This study provides an alternative for researchers to determine riverbank lines based on UAV derived MSL instead of using only tide gauge data.

Accuracy Assessment of Automatic Road Features Extraction from Unmanned Autonomous Vehicle (UAV) Imagery

Nowadays, the road network documentation is required for many applications, it is essential for the development of economy and its growth brings benefits in people’s life. Traditionally, the road network extraction is done manually, however, it is costly, and time consuming to update and utilize the spatial information. Thus, in order to utilize this issue, this study aims to evaluate the capabilities of automatic road extraction from orthophoto UAV images using Trainable Weka Segmentation (TWS), Level Set (LS) and Seeded Region Growing (SRG) methods. The study area was carried out at UiTM Perlis Branch area. In this study, The UAV image was processed by using Agisoft PhotoScan software to produce orthophoto image, then the road network in the orthophoto was segmented and extracted by using ImageJ Fiji. Several ground controls were also established at the surrounding of study area. For validation purposes, the automatic extracted road network was compared against manual extracted road network. Based on the findings, it was found that SRG method is slightly better in extracting road features compared to LS method in term of completeness, correctness, and quality for automated extraction. It is hope, this study can be used to help reducing the cost and time consumed in extracting features, especially road network, by using automatic extraction instead of manual extraction.

Assessing Nature’s Contributions to People by Jefoure Roads for Sustainable Management in the Gurage Socio-Ecological Production Landscape in Ethiopia

“Jefoure” refers to a traditional grass-covered road with households settled on both sides in the Gurage socio-ecological production landscape in Ethiopia. We assess Nature’s Contributions to People (NCP) on Jefoure roads for sustainable management. Data were collected using survey tools and orthophoto images, and they were systematically analyzed using qualitative and quantitative methods. The Jefoure roads are beyond transport networks and enhance the people’s quality of life. We identified 12 contributions of Jefoure roads from the reporting categories of material, nonmaterial, and regulating NCP. Over time, the contributions of the roads at different localities decreased due to socio-economic, cultural, and religious changes. Recent trends in infrastructural provisions cause the degradation of Jefoure roads and lead to a decrease in their value. Therefore, sustainable landscape planning and management are essential to preserve and enhance the positive NCP. This study indicates that roads do not solely generate negative ecological impacts, and planners need to work on designing versatile roads, particularly in landscapes where traffic flows are low. Indigenous knowledge has a significant role in bridging the past and future and needs to be integrated into landscape planning and management.

Similarity Index Based Approach for Identifying Similar Grotto Statues to Support Virtual Restoration

Grottoes, with caves and statues, are an important part of immovable heritage. Statues in a particular grotto setting are often similar in geometric form and artistic style, and identifying the similarity between these statues can help provide important references for value recognition, condition assessment, repair, and the virtual restoration of statues. Traditionally, such reference information mainly depended on expert empirical judgment, which is highly subjective, lacks quantitative analysis, and cannot provide effective scientific support for the virtual restoration of grotto statues. This paper presents a similarity index based approach for identifying similarities between grotto statues by studying 11 small Buddhist statues carved on the 18th cave in the Yungang Grottoes, located in Datong, China. The similarity index is determined according to the hash values calculated based on the pHash method using the orthophoto images of Buddhist statues to identify similar statues. Similar feature points between the identified statues are then matched using the Scale Invariant Feature Transform (SIFT) operator to support the repair and reconstruction of damaged statues. The experimental results show that the variation of similarity index values confirms the visual inspection of the statues’ appearance in the orthophotos. The additional analysis of three-dimensional (3D) point clouds also confirms that the similarity index based approach is accurate in the initial screening of similar grotto statues.

Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images

Road networks are one of the main urban features. Therefore, road parts extraction from high-resolution remotely sensed imagery and updated road database are beneficial for many GIS applications. However, owing to the presence of various types of obstacles in the images, such as shadows, cars, and trees, with similar transparency and spectral values as road class, achieving accurate road extraction using different classification and segmentation methods is still difficult. This paper proposes an integrated method combining segmentation and classification methods with connected components analysis to extract road class from orthophoto images. The proposed technique is threefold. First, multiresolution segmentation method was applied to segment images. Then, the main classification methods, namely, decision trees (DT), k-nearest neighbors (KNN), and support vector machines (SVM), were implemented based on spectral, geometric, and textural information to classify the obtained results into two classes: road and non-road. Three main accuracy evaluation measures, such as recall, precision, and F1-score, were evaluated to determine the performance of the proposed method, with respective average values of 87.62%, 89.71%, and 88.61%, respectively, for DT; 86.61%, 88.17%, and 87.30%, respectively, for KNN; and 89.83%, 89.52%, and 89.67%, respectively, for SVM. Finally, connected components labelling was used to extract road component parts, and morphological operation was employed to delete non-road parts and noises and improve the performance. These results were also compared with other prior works, which confirmed that the integrated method is an effective road extraction technique.

A Multi-Feature Fusion Using Deep Transfer Learning for Earthquake Building Damage Detection

With the recent tremendous improvements in the spatial, spectral, and temporal resolutions of remote sensing imaging systems, there has been a dramatic increase in their applications. Amongst different applications of very high-resolution remote sensing images, damage detection for rapid emergency response is one of the most challenging ones. Recently, deep learning frameworks have enhanced the performance of earthquake damage detection by automatic extraction of strong deep features. However, most of the existing studies in this area focus on using nadir satellite images or orthophotos which limits the available data sources. The objective of this study is to present a multi-modal integrated structure to combine orthophoto and off-nadir images for earthquake building damage detection. In this context, a multi-feature fusion method based on deep transfer learning is presented, which contains four different steps, namely pre-processing, deep feature extraction, deep feature fusion, and transfer learning. To validate the presented framework, two comparative experiments are conducted on the 2010 Haiti earthquake, using pre- and post-event off-nadir satellite images, which were collected by WorldView-2 (WV-2) satellite platform as well as a post-event airborne orthophoto. The results demonstrate considerable advantages in identifying damaged and non-damaged buildings with over 83% for the overall accuracy.

Mapping and characterizing the Jefoure roads that have cultural heritage values in the Gurage socio-ecological production landscape of Ethiopia

Information about cultural landscape settings and characteristics is essential for understanding and finding solutions for their sustainable management. In the Gurage socio-ecological production landscape of Ethiopia, “Jefoure” refers to a traditional grass-covered road with households on either side. This study aims to map and characterize Jefoure roads to help manage them sustainably. Data were compiled using survey tools and recent orthophoto images and then were qualitatively and quantitatively analyzed. The results underscore Jefoure as a network of roads of variable lengths that can be as extensive as 87 m. Notably, the physical geography of the landscape has an impact on the length, shape, and directions of these roads. Jefoure roads are multifunctional roads that have been designed and managed by local people for centuries. The Gurage settlement pattern is centered on these roads, which influences their home garden functional spaces. Residents, local groups, land-use planners, and decision-makers need to manage the cultural roads sustainably. This study generates valuable empirical information regarding a Gurage landscape feature that is not common knowledge, and it can support decision-makers and other conservation initiatives aimed at the sustainable management. It may also inform and encourage other researchers in their studies on similar cultural roads. This road has a cultural value and needs to work on its heritagization, in conjunction with the community's various cultural assets.

Unmanned aerial vehicle (uav) application for supporting climate change readiness program in depok municipality, indonesia

In 2019, Depok Municipality in Indonesia has participated for the second times in PROKLIM (Program Kampung Iklim-bahasa) which is focussed in RW 10 Baktijaya Village. In previous year, community activities in biopori holes, family herbal planting (TOGA) and rainwater infiltration well (RIW) was difficult to be updated. Therefore, in this study unmanned aerial vehicle (UAV) is applied to acquire location of PROKLIM in Baktijaya village with very high resolution image. Herein, DJI Phantom 4 Pro was flown at 100 m height to capture images of participated houses. The image from UAV then processed with Agisoft Photoscan to generate orthophoto image. With the guiandce from local community leader, biopori hole, TOGA and RIW locations were tagged using Avenza Maps and input into ArcGIS. Layers from UAV images and field survey then integrated to produce maps of biopori hole, TOGA and RIW activities. The map of PROKLIM activities was successfully generated from UAV image and Avenza Maps application. Resulted maps are very useful for updating submission to PROKLIM registration website in Indonesia’s Ministry of Environment and Forestry.

Three-Dimensional Model of the Moon with Semantic Information of Craters Based on Chang'e Data.

China’s Chang’e lunar exploration project obtains digital orthophoto image (DOM) and digital elevation model (DEM) data covering the whole Moon, which are critical to lunar research. The DOM data have three resolutions (i.e., 7, 20 and 50 m), while the DEM has two resolutions (i.e., 20 and 50 m). Analysis and research on these image data effectively help humans to understand the Moon. In addition, impact craters are considered the most basic feature of the Moon’s surface. Statistics regarding the size and distribution of impact craters are essential for lunar geology. In existing works, however, the lunar surface has been reconstructed less accurately, and there is insufficient semantic information regarding the craters. In order to build a three-dimensional (3D) model of the Moon with crater information using Chang‘e data in the Chang‘e reference frame, we propose a four-step framework. First, software is implemented to annotate the lunar impact craters from Chang’e data by complying with our existing study on an auxiliary annotation method and open-source software LabelMe. Second, auxiliary annotation software is adopted to annotate six segments in the Chang’e data for an overall 25,250 impact crater targets. The existing but inaccurate craters are combined with our labeled data to generate a larger dataset of craters. This data set is analyzed and compared with the common detection data. Third, deep learning detection methods are employed to detect impact craters. To address the problem attributed to the resolution of Chang’e data being too high, a quadtree decomposition is conducted. Lastly, a geographic information system is used to map the DEM data to 3D space and annotate the semantic information of the impact craters. In brief, a 3D model of the Moon with crater information is implemented based on Chang’e data in the Chang‘e reference frame, which is of high significance.

Full Convolutional Network Algorithm Improves the Accuracy of the Boundary of Orthographic Composite Images

In the past several “five-year plans”, China has basically established the national, provincial and regional streets of basic geographic information database, and on this basis has carried on a certain degree of improvement. Based on deep learning, this paper aims at improving the boundary accuracy of the orthographic composite image based on the convolutional neural network algorithm. It mainly compares the effects of different parameters on the performance of the network model, and describes the training and testing performance of the model through the corresponding performance evaluation method. In the research and test, the detection accuracy statistics of internal industry encryption points and field control points with different scale accuracy were adopted. The results showed that the error in the DOM detection of new aerial images and satellite images was less than 3m and 9m, which met the technical requirements and improved the boundary accuracy compared with the plane accuracy of existing digital orthophoto images. Thus, it can be seen that the new production technical scheme of aerial image/satellite shadow DOM generated by enhancing the precision of the orthographic composite image boundary based on the full convolutional network algorithm under deep learning is efficient and feasible. The study not only improves the relevant theories, but also has some practical inspirations for future research.

Potential Dam Breach Analysis and Flood Wave Risk Assessment Using HEC-RAS and Remote Sensing Data: A Multicriteria Approach

Dam breach has disastrous consequences for the economy and human lives. Floods are one of the most damaging natural phenomena, and some of the most catastrophic flash floods are related to dam collapses. The goal of the present study is to analyse the impact of a possible failure–collapse on a potentially affected area downstream of the existing Bramianos dam on southern Crete Island. HEC-RAS hydraulic analysis software was used to study the dam breach, the flood wave propagation, and estimate the extent of floods. The analysis was performed using two different relief datasets of the same area: a digital elevation model (DEM) taken from very high-resolution orthophoto images (OPH) of the National Cadastre and Mapping Agency SA and a detailed digital surface model (DSM) extracted from aerial images taken by an unmanned aerial vehicle (UAV). Remote sensing data of the Sentinel-2 satellite and OPH were utilised to create the geographic information system (GIS) layers of a thorough land use/cover classification (LULC) for the potentially flooded area, which was used to assess the impact of the flood wave. Different dam breach and flood scenarios, where the water flows over man-made structures, settlements, and olive tree cultivations, were also examined. The study area is dominated mainly by three geological formations with different hydrogeological characteristics that dictated the positioning and structure of the dam and determine the processes that shape the geomorphology and surface roughness of the floodplain, affecting flow conditions. The results show that the impact of a potential dam break at Bramianos dam is serious, and appropriate management measures should be taken to reduce the risk. The water flow downstream of the collapsed dam depends on the water volume stored in the reservoir. Moreover, the comparison of DSM and DEM cases shows that the detailed DSM may indicate more accurately the surface relief and existing natural obstacles such as vegetation, buildings, and greenhouses, enabling more realistic hydraulic simulation results. Dam breach flood simulations and innovative remote sensing data can provide valuable outcomes for engineers and stakeholders for decision-making and planning in order to confront the consequences of similar incidents worldwide.

Metodyka wyznaczania godła mapy w układzie International Map of the World (IMW).

The systematic division into section sheets adopted in the International World Map is the standard in many countries. This division is used for most types of small-scale maps. Due to the scope of application, it is also often the basis for indexing orthophoto images and data from laser scanning. Data accummulated in central State resources cover entire countries. Their coverage includes more than information obtained and processed for the users’ needs in the source format. Increasingly often, data is transformed into formats that facilitate their application (e.g. setting up a GRID for a digital terrain model). The need for quick spatial identification is a determinant of the availability of resources stored in these databases. The dynamic development of Open Source software in the fields of GIS is another increasingly broad area of study, and the research focus of scientists from around the world. They see it not only as a ready-made tool for conducting spatial analyses, but also in terms of searching for algorithmic solutions to meet the needs resulting from the requirement to process ever larger amounts of this type of data. The present paper discusses the method for designating a map sheet identification number (index) in a selected scale, based on the longitude and latitude of the given point. An unquestionable advantage of the presented solution is the possibility of dividing map sheets into scales, which were missing in their basic description. It follows from the fact that this spatial indexing method is indispensable for large amounts of data.

THE USING OF TERRESTRIAL LASER (POINT CLOUD) SCANNING TECHNOLOGIES ON URBAN SCALE: EXAMPLE OF THE HISTORICAL URBAN TEXTURE OF LAPSEKİ

Aim: This study aims to comparatively evaluate the use potential of orthophoto images obtained by terrestrial laser scanning technologies on an urban scale through the "Old Lapseki Finds Life Project" prepared using terrestrial laser scanning technologies and the "Enez Historical City Square Project" prepared using traditional methods. Method: In the study, street improvement projects of 29.210 m2 Lapseki and 29.214 m2 Enez city designed on an urban scale were evaluated and compared with descriptive statistics based on different parameters. Results: In the study, it has been determined that terrestrial laser (point cloud) technologies are 99,9% accurate when compared to traditional methods, save time by 83,08% and reduce workforce by 80%. In addition, it has been determined that terrestrial laser scanning technologies accelerate project processes compared to traditional methods. Conclusion: In this study, the use of laser scanning technologies, which are basically reverse engineering applications, in architectural restoration projects, determination of the current situation and damage, architectural documentation of structures and preparation of three-dimensional models, in terms of efficiency in survey studies are evaluated. It has been observed that orthophoto images obtained by terrestrial laser scanning technologies in architectural relief-restoration-restitution projects have potentials' worth using in different stages of the project.