A user study is conducted to evaluate the efficiency and effectiveness of two types of visualizations to identify damage sites in case of disaster. The test consists out 36 trials (18 for each visualisation) and in each trial an area of 1x1km, located in Ghent, is displayed on a screen. This image shows the combined height information from before and after the disaster. The first visualisation, page flipping, is based on greyscale images with height information from the pre- and post-disaster situation between which users can switch manually. The second visualisation, difference image, is a result of subtracting the heights (before versus after) and assigning a blue-white-red colour ramp. In order to simulate the urgency with which the data is captured, systematic and random imperfections are introduced in the post-disaster data. All participants’ mouse and key interactions are logged, which is further complemented by the registration of their eye movements. This give insights the visualizations’ efficiency, effectiveness and the overall search strategies of the participants.

 A user study is conducted to evaluate the efficiency and effectiveness of two types of visualizations to identify damages sites in case of disaster. The test consists out 36 trials (18 for each visualisation) and in each trial an area of 1×1km, located in Ghent, is displayed on a screen. This image shows the combined height information from before and after the disaster. The first visualisation, page flipping, is based on greyscale images with height information from the pre- and post-disaster situation between which users can switch manually. The second visualisation, difference image, is a result of subtracting the heights (before versus after) and assigning a blue-white-red colour ramp. In order to simulate the urgency with which the data is captured, systematic and random imperfections are introduced in the post-disaster data. All participants’ mouse and key interactions are logged, which is further complemented by the registration of their eye movements. This give insights the visualizations’ efficiency, effectiveness and the overall search strategies of the participants.

In the context of urban planning, it is very important to estimate the nature of the roof of every building and, in particular, to make the difference between flat roofs and gable ones. This analysis is necessary in seismically active areas. Also in the assessment of renewable energy projects such solar energy, the shape of roofs must be accurately retrieved. In order to perform this task automatically on a large scale, aerial photos provide a useful solution. The goal of this research project is the development of algorithm for accurate mapping of two different roof types in digital aerial images. The algorithm proposed in this paper includes several components: pre-processing step to reduce illumination differences of individual roof surfaces, statistical moments calculation and color indexing. Roof models are created and saved as masks with feature specific descriptors. Masks are then used to mark areas that contain elements of the different roof types (e.g. gable and hip). The final orthophoto visualize an accurate position of each of the roof types. The result is evaluated using precision recall method.

This paper proposes an evaluation of data acquired with various sensors and used in coastal water segmentation applications. Correct monitoring of coastal changes in dynamic coastal environments strongly depends on accurate and frequent detection of shoreline position. Automatic shoreline delineation methods are preferable, especially in terms of time, cost, labor intensiveness and difficulties of in-situ measurements. Two main issues have been encountered within this application field, the quality of data and the segmentation algorithms. In this work, potential benefits of various data sources including optical and active sensors for extraction of shorelines have been investigated. The goal with shoreline detection from digital data sources is to obtain information as efficiently as possible and as reliably as necessary. Starting with that observation the paper discusses the effectiveness of coastal information extraction provided different data sources. This question is especially important to address since we observe a fast development of high spatial resolution data acquisition. There are many of segmentation algorithms described in the field of image processing and yet there is currently no single theory or method, no universal segmentation framework, that can be applied on all images to precisely and robustly extract shorelines. Nether there is a uniform standard for the assessment of segmentation results, and this process still largely relies on visual analysis and personal judgment. Out of myriads of image segmentation algorithms, we chose the most frequently and successfully applied within the application field and considering the data sources. In optical sensor data cases, the most frequently used methods are NDWI (Normalized Difference Water Index) and thresholding techniques. We do not aim to create yet another method to segment out the particular objects from remotely sensed data and then tailor it to work efficiently on that data set. Instead, we evaluate the data quality regarding the given application field. The case study is carried out on a 10 km coastal stretch facing the Baltic Sea (Sweden) and belonging to the Municipality of Gävle. In citu measurements were acquired to evaluate the extracted coastal lines and comparisons with reference were performed based on the average mean distance. A conclusion is done regarding the most reliable data source for this particular application of shoreline delineation.

High spatial resolution imagery is often affected by shadows, both in urban environments with large variations in surface elevation and in vegetated areas. It is a common bias in classification when waters and shadows are registered as the same area. The radiometric response for the shadowed regions should be restored prior to classification. To enable that, separate classes of non-shadowed regions and shadowed areas should be created. Previous work on water extraction using low/medium resolution images, mainly faced two difficulties. Firstly, it is difficult to obtain accurate position of water boundary and secondly, shadows of elevated objects e.g. buildings, bridges, towers and trees are a typical source of noise when facing water extraction in urban regions. In high resolution images the problem of separation water and shadows becomes more prominent since the small local variation of intensity values gives rise to misclassification. This paper proposes a robust method for separation of shadowed areas and water bodies in high spatial resolution imagery using hierarchical method on different scales combined with classification of PCA (Principal Component Analysis) bands, which reduces the effects of radiometric and spatial differences that is commonly associated with the pixel-based methods for multisource data fusion. The method uses ancillary data to aid in classification of shadows and waters. The proposed method includes three steps: segmentation, classification and postprocessing. To achieve robust segmentation, we apply the merging region with three features (PCA bands, NSVDI (Normalized Saturation-value Difference Index) and height data). NSVDI discriminates shadows and some water. In the second step we use hierarchic region based classification to identify water regions. After that step candidates for water pixels are verified by the LiDAR DEM data. As a last step we consider shape parameters such as compactness and symmetry to completely remove shadows.

This research focuses on automatic extraction of river banks and other inland waters from remote sensing images. There are no up to date accessible databases of rivers and most of other waters objects for modelling purposes. The main reason for that is that some regions are hard to access with the traditional ground through techniques and thus the boundary of river banks are uncertain in many geographical positions. The other reason is the limitations of widely applied method for extraction of water bodies called normalized-difference water index (NDWI). There is a novel approach to extract water bodies, which is based on pixel level variability or entropy, however, the methods work somewhat satisfactory on high spatial resolution images, there is no verification of the method performance on moderate or low resolution images. Problems encounter identification of mixed water pixels and e.g. roads, which are built in attachment to river banks and thus can be classified as rivers. In this work we propose an automatic extraction of river banks using image entropy, combined with NDWI identification. In this study only moderate spatial resolution Landsat TM are tested. Areas of interest include both major river banks and inland lakes. Calculating entropy on such poor spatial resolution images will lead to misinterpretation of water bodies, which all exhibits the same small variation of pixel values as e.g. some open or urban areas. Image entropy thus is calculated with the modification that involves the incorporation of local normalization index or variability coefficient. NDWI will produce an image where clear water exhibits large difference comparing to other land features. We are presenting an algorithm that uses an NDWI prior to entropy processing, so that bands used to calculate it, are chosen in clear connection to water body features that are clearly discernible.As a result we visualize a clear segmentation of the water bodies from the remote sensing images and verify the coordinates with a given geographic reference.

Recognition of buildings is not a trivial task, yet highly demanded in many applications including augmented reality for mobile phones. Recognition rate can be increased significantly if building façade extraction will take place prior to the recognitionprocess. It is also a challenging task since eachbuilding can be viewed from different angles or under differentlighting conditions. Natural situation outdoor is when buildings are occluded by trees, street signs and other objects. This interferes for successful building façade recognition. In this paper we evaluate the knowledge based approach  to automatically segment out the whole buildingfaçade or major parts of thefaçade. This automatic building detection algorithm is then evaluated against other segmentation methods such as SIFT and vanishing point approach. This work contains two main steps: segmentation of building façades region using two different approaches and evaluation of the methods using database of reference features. Building recognition model (BRM) includes evaluation step that uses Chamfer metrics. BMR is then compared to vanishing points segmentation. In the evaluation mode, comparison of these two different segmentation methods is done using the data from ZuBuD.Reference matching is also done using Scale Invariant Feature Transform. Theresults show that the recognition rate is satisfactory for the BMR model and there is no need to extract the whole building façade for the successful recognition.

The paper presents a three-dimensional (3D) geovisualisation model of uncertainties in simulated flood maps that can help communicate uncertain information in the data being used. An entropy-based measure was employed for uncertainty quantification. In developing the model, Visualisation Toolkit (VTK) was utilised. Different data derived from earlier simulation study and other maps were represented in the model. Cartographic principles were considered in the map design. A Graphical User Interface (GUI), which was developed in Tkinter, was also created to further support exploratory data analysis. The resulting model allowed visual identification of uncertain areas, as well as displaying spatial relationship between the entropy and the slope values. This geovisualisation has still to be tested to assess its effectiveness as a communication tool. However, this type of uncertainty visualisation in flood mapping is an initial step that can lead to its adoption in decision-making when presented comprehensively to its users. Thus, further improvement and development is still suggested for this kind of information presentation.

Building facade extraction is the primary step in the recognition process in outdoor scenes. It is also a challenging task since each building can be viewed from different angles or under different lighting conditions. In outdoor imagery, regions, such as sky, trees, pavement cause interference for a successful building facade recognition. In this paper we propose a knowledge based approach to automatically segment out the whole facade or major parts of the facade from outdoor scene. The found building regions are then subjected to recognition process. The system is composed of two modules: segmentation of building facades region module and facade recognition module. In the facade segmentation module, color processing and objects position coordinates are used. In the facade recognition module, Chamfer metrics are applied. In real time recognition scenario, the image with a building is first analyzed in order to extract the facade region, which is then compared to a database with feature descriptors in order to find a match. The results show that the recognition rate is dependent on a precision of building extraction part, which in turn, depends on a homogeneity of colors of facades.

The analysis of flooding levels is a highly complex temporal and spatial assessment task that involves estimation of distances between references in geographical space as well as estimations of instances along the time-line that coincide with  given spatial locations. This work has an aim to interactively explore changes of Amur River boundaries caused by the severe flooding in September 2013. In our analysis of river bank changes we use satellite imagery (Landsat 7) to extract parts belonging to Amur River. We use imagery from that covers time interval July 2003 until February 2014. Image data is pre-processed using low level image processing techniques prior to visualization. Pre-processing has a purpose to extract information about the boundaries of the river, and to transform it into a vectorized format, suitable as inputs subsequent visualization. We develop visualization tools to explore the spatial and temporal relationship in the change of river banks. In particular the visualization shall allow for exploring specific geographic locations and their proximity to the river/floods at arbitrary times. We propose a time space visualization that emanates from edge detection, morphological operations and boundary statistics on Landsat 2D imagery in order to extract the borders of Amur River. For the visualization we use the time-space-cube metaphor. It is based on a 3D rectilinear context, where the 2D geographical coordinate system is extended with a time-axis pointing along the 3^rd Cartesian axis. Such visualization facilitates analysis of the channel shape of Amur River and thus enabling for a conclusion regarding the defined problem. As a result we demonstrate our time-space visualization for river Amur and using some amount of geographical point data as a reference we suggest an adequate method of interpolation or imputation that can be employed to estimate value at a given location and time.