Hyperspectral Images are worthwhile data for many processing algorithms (e.g. Dimensionality Reduction, Target Detection, Change Detection, Classification and Unmixing). Classification is a key issue in processing hyperspectral images. Spectral-identification-based algorithms are sensitive to spectral variability and noise in acquisition. There are many algorithms for classification. This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Quebec City hyperspectral dataset, demonstrate that the proposed approach achieves approximately 9% and 5% better overall accuracy than the MLP and the original MHS algorithms respectively.
The invention of Ground Penetrating Radar (GPR) technology has facilitated the possibility of detecting buried utilities and has been used primarily in civil engineering for detecting structural defects, such as voids and cavities in road pavements, slabs and bridge decks, but has not been used to assess the condition of buried pipes. Pipe deterioration can be defined as pipes where, for example, cracking, differential deflection, missing bricks, collapses, holes, fractures and corrosion exists. Assessing the deterioration of underground pipes is important for service efficiency and asset management. This paper describes a research project that focused on the use of GPR for assessing the condition of buried pipes. The research involved the construction of a suitable GPR test facility in the laboratory to conduct controlled testing in a dry sand. Plastic pipes were chosen for the experiments. A series of laboratory experiments were conducted to determine the validity and effectiveness of standard commercially available GPR technology in assessing the condition of buried utilities with common types of damage. Several types of damage to the plastic pipe were investigated with respect to different GPR antenna frequencies. The GPR surveys were carried out in order to obtain signal signatures from damaged and undamaged pipes buried at 0.5m depth. These surveys were organised on a grid pattern across the surface of the sand in the test facility. The results presented in this paper show that GPR can identify certain types of damage associated with a buried pipe under these controlled laboratory conditions.
Sensor calibration, image orientation, object extraction and scene understanding from images and image sequences are important research topics in Photogrammetry, Remote Sensing, Computer Vision and Geoinformation Science. Within these areas, both geometry and semantics play an important role, and high quality results require appropriate handling of all these aspects. While individual algorithms differ according to the imaging geometry and the employed sensors and platforms, all mentioned aspects need to be integrated in a suitable workflow to solve most real-world problems.
The efficiency and high mobility of Unmanned Aerial Vehicle (UAV) made them essential to aerial photography assisted survey and mapping. Especially for urban land use and land cover, that they often changes, and need UAVs to obtain new terrain data and the new changes of land use. This study aims to collect image data and three dimensional ground control points in Taichung city area with Unmanned Aerial Vehicle (UAV), general camera and Real-Time Kinematic with positioning accuracy down to centimetre. The study area is an ecological park that has a low topography which support the city as a detention basin. A digital surface model was also built with Agisoft PhotoScan, and there will also be a high resolution orthophotos. There will be two conditions for this study, with or without ground control points and both were discussed and compared for the accuracy level of each of the digital surface models. According to check point deviation estimate, the model without ground control points has an average two-dimension error up to 40 centimeter, altitude error within one meter. The GCP-free RTK-airborne approach produces centimeter-level accuracy with excellent to low risk to the UAS operators. As in the case of the model with ground control points, the accuracy of x, y, z coordinates has gone up 54.62%, 49.07%, and 87.74%, and the accuracy of altitude has improved the most.
Mobile mapping often occurs in environments with poor or no GNSS signal reception (tunnels, urban canons, canopy …). Most mobile scanning systems are equipped with tactical grade inertial systems, GNSS outages longer than 30-60 seconds may lead to a rapid decrease in absolute positioning accuracy that might be below the client's expectations. In order to guarantee sufficient positioning accuracy even in such environments the data must be corrected. This can either happen by readjusting the final point cloud using control points (3D translations) or by directly correcting the trajectory by adding external position updates. The approach presented in this paper consists in detecting targets (either existing or placed before measurement) that can easily be identified in the point cloud and that have been measured independently. By identifying the 3D point closest to the target's center, computing the coordinate difference to the corresponding GCP and retrieving its GNSS-timestamp and the internal scanner coordinates, an external position update at a given time can be computed. This procedure has proven its efficiency in several projects including scanning tunnels up to 5km length, where the positional error (in 3D) of the resulting point cloud could be reduced from 5m (in the middle of the tunnel) down to better than 5cm using GCPS only every 400m.
Nowadays, the nature of land title and cadastral (LTC) data in the Turkey is dynamic from a temporal perspective which depends on the LTC operations. Functional requirements with respect to the characteristics are investigated based upon interviews of professionals in public and private sectors. These are; Legal authorities, Land Registry and Cadastre offices, Highway departments, Foundations, Ministries of Budget, Transportation, Justice, Public Works and Settlement, Environment and Forestry, Agriculture and Rural Affairs, Culture and Internal Affairs, State Institute of Statistics (SIS), execution offices, tax offices, real estate offices, private sector, local governments and banks. On the other hand, spatio-temporal LTC data very important component for creating infrastructure of Land Administration Model (LADM). For this reason, spatio-temporal LTC data needs for LADM not only updated but also temporal. The investigations ended up with determine temporal analyses of LTC data, traditional LTC system and tracing temporal analyses in traditional LTC system. In the traditional system, the temporal analyses needed by all these users could not be performed in a rapid and reliable way. The reason for this is that the traditional LTC system is a manual archiving system. The aims and general contents of this paper: (1) define traditional LTC system of Turkey; (2) determining the need for spatio-temporal LTC data and analyses for core domain model for LADM. As a results of temporal and spatio-temporal analysis LTC data needs, new system design is important for the Turkish LADM model. Designing and realizing an efficient and functional Temporal Geographic Information Systems (TGIS) is inevitable for the Turkish LADM core infrastructure. Finally this paper outcome is creating infrastructure for design and develop LADM for Turkey.
The United Arab Emirates (UAE) government has declared the increased use of alternative energy a strategic goal and has invested in identifying and developing various sources of such energy. This study aimed at assessing the viability of establishing wind farms offshore the Emirate of Abu Dhabi, UAE and to identify favourable sites for such farms using Geographic Information Systems (GIS) procedures and algorithms. Based on previous studies and on local requirements, a set of suitability criteria was developed including ocean currents, reserved areas, seabed topography, and wind speed. GIS layers were created and a weighted overlay GIS model based on the above mentioned criteria was built to identify suitable sites for hosting a new offshore wind energy farm. Results showed that most of Abu Dhabi offshore areas were unsuitable, largely due to the presence of restricted zones (marine protected areas, oil extraction platforms and oil pipelines in particular). However, some suitable sites could be identified, especially around Delma Island and North of Jabal Barakah in the Western Region. The environmental impact of potential wind farm locations and associated cables on the marine ecology was examined to ensure minimal disturbance to marine life. Further research is needed to specify wind mills characteristics that suit the study area especially with the presence of heavy traffic due to many oil production and shipping activities in the Arabian Gulf most of the year.
The effective access and use of geospatial information (GI) resources acquires a critical value of importance in modern knowledge based society. Standard web services defined by Open Geospatial Consortium (OGC) are frequently used within the implementations of spatial data infrastructures (SDIs) to facilitate discovery and use of geospatial data. This data is stored in databases located in a layer, called the invisible web, thus are ignored by search engines. SDI uses a catalogue (discovery) service for the web as a gateway to the GI world through the metadata defined by ISO standards, which are structurally diverse to OGC metadata. Therefore, a crosswalk needs to be implemented to bridge the OGC resources discovered on mainstream web with those documented by metadata in an SDI to enrich its information extent. A public global wide and user friendly portal of OGC resources available on the web ensures and enhances the use of GI within a multidisciplinary context and bridges the geospatial web from the end-user perspective, thus opens its borders to everybody.Project "Crosswalking the layers of geospatial information resources to enable a borderless geospatial web" with the acronym BOLEGWEB is ongoing as a postdoctoral research project at the Faculty of Geodesy, University of Zagreb in Croatia (http://bolegweb.geof.unizg.hr/). The research leading to the results of the project has received funding from the European Union Seventh Framework Programme (FP7 2007-2013) under Marie Curie FP7-PEOPLE-2011-COFUND. The project started in the November 2014 and is planned to be finished by the end of 2016. This paper provides an overview of the project, research questions and methodology, so far achieved results and future steps.
This paper presents one of the applications of monitoring mechanical tests carried out in Construction Materials Laboratory of Istanbul Technical University. In Turkey, as in many countries, large amount of existing buildings exposed to seismic hazard, therefore various analytical and experimental studies are being conducted to contribute to the solution of the problem. One of the new generation retrofitting techniques is to strength the structural members by using Fiber Reinforcing Polymer (FRP). This study summarize the results of monitoring of deformations short concrete column samples under the incremental compression load. In this study, result of two rectangular short columns are given. One of them was tested as a reference sample, the other sample were tested after strengthening by PET reinforced polymer composite materials. Besides conventional displacement and strain measurement systems, laser scanning method was used to get three dimensional deformed shape of sample at each selected steps.
Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.
90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.
Land abandonment and secondary forest succession have played a significant role in land cover changes and forest cover increase in mountain areas in Europe over the past several decades. Land abandonment can be easily observed in the field over small areas, but it is difficult to map over the large areas, e.g., with remote sensing, due to its subtle and spatially dispersed character. Our previous paper presented how the LiDAR (Light Detection and Ranging) and topographic data were used to detect secondary forest succession on abandoned land in one commune located in the Polish Carpathians by means of object-based image analysis (OBIA) and GIS (Kolecka et al., 2015). This paper proposes how the method can be applied to efficiently map secondary forest succession over the entire Polish Carpathians, incorporating spatial sampling strategy supported by various ancillary data. Here we discuss the methods of spatial sampling, its limitations and results in the context of future secondary forest succession modelling.
The objective of this study is implementation of system architecture for collecting and analysing data as well as visualizing results for hydrodynamic modelling of flood flows in river valleys using remote sensing methods, tree-dimensional geometry of spatial objects and GPU multithread processing. The proposed solution includes: spatial data acquisition segment, data processing and transformation, mathematical modelling of flow phenomena and results visualization. Data acquisition segment was based on aerial laser scanning supplemented by images in visible range. Vector data creation was based on automatic and semiautomatic algorithms of DTM and 3D spatial features modelling. Algorithms for buildings and vegetation geometry modelling were proposed or adopted from literature. The implementation of the framework was designed as modular software using open specifications and partially reusing open source projects. The database structure for gathering and sharing vector data, including flood modelling results, was created using PostgreSQL. For the internal structure of feature classes of spatial objects in a database, the CityGML standard was used. For the hydrodynamic modelling the solutions of Navier-Stokes equations in two-dimensional version was implemented. Visualization of geospatial data and flow model results was transferred to the client side application. This gave the independence from server hardware platform. A real-world case in Poland, which is a part of Widawa River valley near Wroclaw city, was selected to demonstrate the applicability of proposed system.
“Ecological Infrastructure (EI)” are defined as naturally functioning ecosystems that deliver valuable services to people, such as healthy mountain catchments, rivers, wetlands, coastal dunes, and nodes and corridors of natural habitat, which together form a network of interconnected structural elements in the landscape. On the other hand, natural disaster occur at the locations where habitat was reduced due to the changes of land use, in which the land was converted to the settlements and agricultural cropland. Hence, habitat loss and natural disaster are linked closely. Ecological infrastructure is the nature-based equivalent of built or hard infrastructure, and is as important for providing services and underpinning socio-economic development. Hence, ecological infrastructure is expected to contribute to functioning as ecological disaster reduction, which is termed Ecosystem-based Solutions for Disaster Risk Reduction (Eco-DRR). Although ecological infrastructure already exists in the landscape, it might be degraded, needs to be maintained and managed, and in some cases restored. Maintenance and restoration of ecological infrastructure is important for security of human lives. Therefore, analytical tool and effective visualization tool in spatially explicit way for the past natural disaster and future prediction of natural disaster in relation to ecological infrastructure is considered helpful. Hence, Web-GIS based Ecological Infrastructure Environmental Information System (EI-EIS) has been developed. This paper aims to describe the procedure of development and future application of EI-EIS. The purpose of the EI-EIS is to evaluate functions of Eco-DRR. In order to analyse disaster data, collection of past disaster information, and disaster-prone area is effective. First, a number of digital maps and analogue maps in Japan and Europe were collected. In total, 18,572 maps over 100 years were collected. The Japanese data includes Future-Pop Data Series (1,736 maps), JMC dataset 50m grid (elevation) (13,071 maps), Old Edition Maps: Topographic Map (325 maps), Digital Base Map at a scale of 2500 for reconstruction planning (808 maps), Detailed Digital Land Use Information for Metropolitan Area (10 m land use) (2,436 maps), and Digital Information by GSI (national large scale map) (71 maps). Old Edition Maps: Topographic Map were analogue maps, and were scanned and georeferenced. These geographical area covered 1) Tohoku area, 2) Five Lakes of Mikata area (Fukui), 3) Ooshima Island (Tokyo), 4) Hiroshima area (Hiroshima), 5) Okushiri Island (Hokkaido), and 6) Toyooka City area (Hyogo). The European data includes topographic map in Germany (8 maps), old topographic map in Germany (31 maps), ancient map in Germany (23 maps), topographic map in Austria (9 maps), old topographic map in Austria (17 maps), and ancient map in Austria (37 maps). Second, focusing on Five Lakes of Mikata area as an example, these maps were integrated into the ArcGIS Online® (ESRI). These data can be overlaid, and time-series data can be visualized by a time slider function of ArcGIS Online.
In this paper a method is presented which allows the measurement of deflections and torsion by means of the silhouette of an object in images. The method is based on a finite element description of a beam. The benefit of this method is the determination of the deformation out of the silhouette of an object in images without the need of signalization. The presented method is tested against simulated data as well as against real objects in laboratory tests. As an outlook the presented method can be further modified. By combination with a laser scanner it seems to be possible to replace the CAD model of an object with the point clouds of one or more kinematic laser scans.
Existing 3D indoor mapping of RGB-D data are prominently point-based and feature-based methods. In most cases iterative closest point (ICP) and its variants are generally used for pairwise registration process. Considering that the ICP algorithm requires an relatively accurate initial transformation and high overlap a weighted closed-form solution for RGB-D data registration is proposed. In this solution, we weighted and normalized the 3D points based on the theoretical random errors and the dual-number quaternions are used to represent the 3D rigid body motion. Basically, dual-number quaternions provide a closed-form solution by minimizing a cost function. The most important advantage of the closed-form solution is that it provides the optimal transformation in one-step, it does not need to calculate good initial estimates and expressively decreases the demand for computer resources in contrast to the iterative method. Basically, first our method exploits RGB information. We employed a scale invariant feature transformation (SIFT) for extracting, detecting, and matching features. It is able to detect and describe local features that are invariant to scaling and rotation. To detect and filter outliers, we used random sample consensus (RANSAC) algorithm, jointly with an statistical dispersion called interquartile range (IQR). After, a new RGB-D loop-closure solution is implemented based on the volumetric information between pair of point clouds and the dispersion of the random errors. The loop-closure consists to recognize when the sensor revisits some region. Finally, a globally consistent map is created to minimize the registration errors via a graph-based optimization. The effectiveness of the proposed method is demonstrated with a Kinect dataset. The experimental results show that the proposed method can properly map the indoor environment with an absolute accuracy around 1.5% of the travel of a trajectory.
This paper introduces two archaeological sites documented during the MULINEM (The Medieval Urban Landscape in Northeastern Mesopotamia) project. This project investigates the Late Sasanian and Islamic urban network in the land of Erbil, a historic province of Hidyab (Adiabene) that is located in northern Iraq. The investigated sites are the two deserted cities of Makhmúr al-Quadíma and Al-Hadítha. It is assumed that these two sites used to form large cities with high business and cultural importance in the medieval period. The archaeological locations are endangered by various threats.The Al-Hadítha site seems to be under the control of the „Islamic state“ at the moment and Makhmúr al-Quadíma is located just next to the town of new Makhmúr that expands rapidly and without complex urban plans. Documentation of the archaeological sites has been done by using remotely sensed methods together with in-situ measurements (where available). FORMOSAT-2 data that has been gained through a research announcement: Free FORMOSAT-2 satellite imagery and when combined with other sources (recent and historical data) it provides a powerful documentation tool. In-situ RPAS measurements and a DTM creation furnish a new source of highly valuable information. Influence of the political and security situation in Al-Hadítha will be analysed.
Debris flows cause an average € 30 million damages and 1-2 fatalities every year in Austria. Detailed documentation of their extent and magnitude is essential for understanding, preventing and mitigating these natural hazard events. The recent development of unmanned aerial systems (UAS) has provided a new possibility for on-demand high-resolution monitoring and mapping. Here, we present a study, where the spatial extent and volume of a large debris flow event were mapped with different UAS, fitted with commercial off-the-shelf sensors. Orthophotos and digital terrain models (DTM) were calculated using structure-from-motion photogrammetry software. Terrain height differences caused by the debris flow in the catchment and valley floor were derived by subtracting the pre-event airborne laser scanning (ALS) DTM from a post-event UAS-DTM. The analysis of the volumetric sediment budget showed, that approximately 265,000 m³ material was mobilised in the catchment, of which 45,000 m³ settled there; of the material, which reached the valley floor, 120,000 m³ was deposited, while another 10,000 m³ was eroded from there. The UAS-results were validated against ALS data and imagery from a traditional manned-aircraft photogrammetry campaign. In conclusion, the UAS-data can reach an accuracy and precision comparable to manned aircraft data, but with the added benefits of higher flexibility, easier repeatability, less operational constraints and higher spatial resolution.
This study presents a new methodological approach for assessment of spatial and qualitative aspects of forest disturbance based on the use of multispectral imaging camera with the UAV photogrammetry. We have used the miniaturized multispectral sensor Tetracam Micro Multiple Camera Array (μ-MCA) Snap 6 with the multirotor imaging platform to get multispectral imagery with high spatial resolution. The study area is located in the Sumava Mountains, Central Europe, heavily affected by windstorms, followed by extensive and repeated bark beetle (Ips typographus [L.]) outbreaks in the past 20 years. After two decades, there is apparent continuous spread of forest disturbance as well as rapid regeneration of forest vegetation, related with changes in species and their diversity. For testing of suggested methodology, we have launched imaging campaign in experimental site under various stages of forest disturbance and regeneration. The imagery of high spatial and spectral resolution enabled to analyse the inner structure and dynamics of the processes. The most informative bands for tree stress detection caused by bark beetle infestation are band 2 (650nm) and band 3 (700nm), followed by band 4 (800 nm) from the, red-edge and NIR part of the spectrum. We have identified only three indices, which seems to be able to correctly detect different forest disturbance categories in the complex conditions of mixture of categories. These are Normalized Difference Vegetation Index (NDVI), Simple 800/650 Ratio Pigment specific simple ratio B1 and Red-edge Index.
We present a novel ground filter for remotely sensed height data. Our filter has two phases: the first phase segments the DSM with a slope threshold and uses gradient direction to identify candidate ground segments; the second phase fits surfaces to the candidate ground points and removes outliers. Digital terrain is obtained by a surface fit to the final set of ground points. We tested the new algorithm on digital surface models (DSMs) for a 9600 km(2) region around Perth, Australia. This region contains a large mix of land uses (urban, grassland, native forest and plantation forest) and includes both a sandy coastal plain and a hillier region (elevations up to 0.5km). The DSMs are captured annually at 0:2 m resolution using aerial stereo photography, resulting in 1:2 TB of input data per annum. Overall accuracy of the filter was estimated to be 89:6 % and on a small semi-rural subset our algorithm was found to have 40 % fewer errors compared to Inpho's Match-T algorithm.
