81 research outputs found
Comparative analysis of automatic approaches to building detection from multi-source aerial data
No full textAutomatic building detection has been a hot topic since the early 1990’s. Early approaches were based on a single aerial image. Detecting buildings is a difficult task so it can be more effective when multiple sources of information are obtained and fused. The objective of this paper is to provide a comparative analysis of automatic approaches to building detection from multi-source aerial images. We analysed data related to both urban and suburban areas and took into consideration both object based and pixel-based methods. Although many of these methods perform full data classification, we focused only on the detection of building regions. Three measures were used for the evaluation of the performance of each method: number of detected buildings to their total number (detection rate), number of objects wrongly detected as buildings (false positive) and number of missed buildings (false negative) to the number of detected buildings. The data sets we used were RGB and colour infrared (CIR) orthoimages and Digital Surface Models (DSMs) obtained by an airborne laser scanner, which provides a first pulse DSM and a last pulse DSM. In addition, we derived from these data and used other four sources of information: a Digital Terrain Model (DTM) obtained from a filtered version of the last pulse DSM, the height difference between the last pulse and the DTM, the height difference between the first and the last pulse and the Normalized Difference Vegetation Index (NVDI) derived from the red and infrared channels.We analysed results coming from three classification algorithms, namely Bayesian, Dempster-Shafer and AdaBoost, applied to the features extracted both at pixel level and at object level. To obtain a very realistic comparison we used the same training set for all methods, either pixel-based or object-based. Results obtained are interesting and can be synthesised in the need of fusing (the results of) more approaches to yield the best results.Remote SensingAerospace Engineerin
A comparison of Bayesian and evidence-based fusion methods for automated building detection in aerial data
No full textAutomated approaches to building detection are of great importance in a number of different applications including map updating and monitoring of informal settlements. With the availability of multi-source aerial data in recent years, data fusion approaches to automated building detection have become more popular. In this paper, two data fusion methods, namely Bayesian and Dempster-Shafer, are evaluated for the detection of buildings in aerial image and laser range data, and their performances are compared. The results indicate that the Bayesian maximum likelihood method yields a higher detection rate, while the Dempster-Shafer method results in a lower false-positive rate. A comparison of the results in pixel level and object level reveals that both methods perform slightly better in object level.Remote SensingAerospace Engineerin
Crowdsourced WebGIS for routing applications in disaster management situations
No full textSuccessfully navigating a damaged infrastructure is challenging due to a lack of automatic routing solutions and a shortage of real-time infrastructure status information. Remote sensing techniques, which are traditionally used, have drawbacks; clouds obscure the view, observation frequency is low, many phenomena can only be observed from the ground, etc. This report presents an alternative observation strategy in the form of crowdsourcing: untrained volunteers are engaged in observing the state of the infrastructure. A web application is built that enables volunteers to make observations through desktop and mobile devices and use the collected information to plan the shortest route to a certain location. The information collected by both groups is stored in a spatial database and displayed on a Google Maps map. The application extends Google's Direction Service with obstacle avoidance functionality that enable users to find the shortest path in a disaster stricken area. This project is carried out as part of the Crisis and Disaster Management course of MSc Geomatics and is supervised by Sisi Zlatanova.GIS TechnologyOTB Research Institute for the Built Environmen
Enabling obstacle avoidance for Google maps' navigation service
No full textCity infrastructures are sensitive to disasters. To aid rescue workers and citizens, a system is needed which determines the shortest route to a certain location, taking the damages of the infrastructure into account. The biggest disadvantage of current navigation systems is that they are “closed” i.e. they are built on top of commercial software packages and as such are only usable by rescue organizations which own licenses for these software packages. Modern web-technologies provide tools to ease information collection and to facilitate the dissemination of data. Recent successes of crowdsourced platforms such as OpenStreetMap, Ushahidi and Wikipedia, suggest the deployment of the crowdsourcing phenomenon to disaster management. The idea is to let the “crowd” in a disaster area collect information about the state of the infrastructure. People on the street form a highly dispersed network of sensors which is able to provide information in real-time at no cost to the rescue workers. This paper proposes and implements a method for performing shortest path calculations taking crowdsourced information, in the form of constraints and obstacles, into account. The method is built on top of Google Maps (GM) and uses its routing service to calculate the shortest distance between two locations. Users provide the constraints and obstacles in the form of polygons which identify impassable areas in the real world. The A* pathfinding algorithm is used to guide Google's Directions Service around obstacles.OTB ResearchOTB Research Institute for the Built Environmen
Google maps for crowdsourced emergency routing
No full textGathering infrastructure data in emergency situations is challenging. The affected by a disaster areas are often large and the needed observations numerous. Spaceborne remote sensing techniques cover large areas but they are of limited use as their field of view may be blocked by clouds, smoke, buildings, highways, etc. Remote sensing products furthermore require specialists to collect and analyze the data. This contrasts the nature of the damage detection problem: almost everyone is capable of observing whether a street is usable or not. The crowd is fit for solving these challenges as its members are numerous, they are willing to help and are often in the vicinity of the disaster thereby forming a highly dispersed sensor network. This paper proposes and implements a small WebGIS application for performing shortest path calculations based on crowdsourced information about the infrastructure health. The application is built on top of Google Maps and uses its routing service to calculate the shortest distance between two locations. Impassable areas are indicated on a map by people performing in-situ observations on a mobile device, and by users on a desktop machine who consult a multitude of information sources.OTB ResearchOTB Research Institute for the Built Environmen
Knowledge-based optimisation of three-dimensional city models for car navigation devices
No full textThree-dimensional maps are deemed better for navigation purposes as they offer a larger number and more realistic navigation cues than two-dimensional maps. Improvements in two key technologies have opened the doors towards utilization of 3D maps for car navigation devices. Advances in data acquisition technologies and data processing methods have made creating photorealistic three-dimensional city models cheaper and to a large extent automatic, while advances in mobile technologies have made e.g. modern smartphones powerful enough to visualize photorealistic 3D graphics. Despite the latter improvements, making three-dimensional mobile maps remains a challenge due to the large amounts of data and the device’s limited amount of memory and pro- cessing power. These limitations can be overcome by intelligently reducing the amount of information that is handled and displayed by the device. This thesis presents an information reduction and prototyping framework that reduces the amount of information contained in city models so a to enable their loading and display on car navigation devices. The information reduction method consists of two steps. The first step selects buildings that are close to the driver’s route with the idea that these aid the driver in navigating. Buildings that are far from the route are discarded. In the second step, the selected buildings’ external representation is adapted to match their navigational value that is based on their thematic, semantic and cognitive properties. For instance, a building of type ’restaurant’ and ’brand’ McDonald’s offers more navigational cues than a block of gray, anonymous residential buildings. The latter are styled in generic textures whereas the former is styled in photorealistic textures. The relations between a building’s semantic and thematic properties and its external representation are captured in visualisation rules. A prototype is built that implements the designed information reduction methods and tests their effectivity. The selection step is performed using a spatial database while the visualisation rules are processed by an expert system. The reduced 3D scenes are displayed in a game engine that also performs performance measurements. The obtained results are conclusive: the performance of a visualisaion in terms of frame rate and used graphics memory is governed by the the amount of textures, much more so than the number of geometries. Effort should therefore be directed towards the reduction and/or simplification of textures rather than geometries.GeomaticsGIS technologyOTB Research Institute for the Built Environmen
Measure the climate, model the city
No full textModern large cities are characterized by a high building concentration, little aeration and lack of green spaces. Such characteristics create an urban climate which is different from the climate outside of cities. An example of an urban climate effect is the so-called Urban Heat Island: cities tend to be warmer than the surrounding rural areas. The higher temperature results in an increase in energy consumption since people, especially in summer, use artificial means to cool themselves. Although means of mitigating the UHI effect exist, they are difficult to justify, as knowledge about urban climate is limited, and analysis tools are lacking. This paper presents the work carried during the 2010 MSc Geomatics Synthesis Project. A 3D spatial relational database has been implemented which is meant to act as starting point in the development of a 3D climate-enabled geographical information system. To this end, the database stores 3D geometries representing the built environment and its thematic properties. The database is also able to store measurements of climate parameters, in this case temperature, obtained through mobile sensors. Spatial analyses and queries are supported, allowing users to calculate areas, distances, buffers, add and remove geometries and thematic attributes. The database design is based on the CityGML information model which has been extended to allow the storage of climate parameters relevant to urban climate research.OTB ResearchOTB Research Institute for the Built Environmen
Behavior of Cations in the Aqueous and Alcoholic Solution of
No full textWe investigated the optical properties of cations in the aqueous and alcoholic solution of at room temperature. We measured the absorption spectra of these solutions in the spectral region 395-800 nm. The Racah parameters and the exchange integrals of the aqueous complex are calculated. The parameters and are also calculated on the basis of our experimental data. The parameters δ σ and δπ which are connected with the symmetry of this complex are also determined
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