Digital Government: Knowledge Management Over Time-Varying Geospatial Datasets

Spatially-related data is collected by many government agencies in various formats and for various uses. This project seeks to facilitate the integration of these data, thus providing new uses. This will require the development of a knowledge management framework to provide syntax, context, and semantics, as well as exploring the introduction of time-varying data into the framework. Education and outreach will be part of the project through the development of an on-line short courses related to data integration in the area of geographical information systems. The grantees will be working with government partners (National Imagery and Mapping Agency, the National Agricultural Statistics Service, and the US Army Topographic Engineering Center), as well as an industrial organization, Base Systems, and the non-profit OpenGIS Consortium, which works closely with vendors of GIS products

Semi-automatic Road Extraction from Very High Resolution Remote Sensing Imagery by RoadModeler

Accurate and up-to-date road information is essential for both effective urban planning and disaster management. Today, very high resolution (VHR) imagery acquired by airborne and spaceborne imaging sensors is the primary source for the acquisition of spatial information of increasingly growing road networks. Given the increased availability of the aerial and satellite images, it is necessary to develop computer-aided techniques to improve the efficiency and reduce the cost of road extraction tasks. Therefore, automation of image-based road extraction is a very active research topic. This thesis deals with the development and implementation aspects of a semi-automatic road extraction strategy, which includes two key approaches: multidirectional and single-direction road extraction. It requires a human operator to initialize a seed circle on a road and specify a extraction approach before the road is extracted by automatic algorithms using multiple vision cues. The multidirectional approach is used to detect roads with different materials, widths, intersection shapes, and degrees of noise, but sometimes it also interprets parking lots as road areas. Different from the multidirectional approach, the single-direction approach can detect roads with few mistakes, but each seed circle can only be used to detect one road. In accordance with this strategy, a RoadModeler prototype was developed. Both aerial and GeoEye-1 satellite images of seven different types of scenes with various road shapes in rural, downtown, and residential areas were used to evaluate the performance of the RoadModeler. The experimental results demonstrated that the RoadModeler is reliable and easy-to-use by a non-expert operator. Therefore, the RoadModeler is much better than the object-oriented classification. Its average road completeness, correctness, and quality achieved 94%, 97%, and 94%, respectively. These results are higher than those of Hu et al. (2007), which are 91%, 90%, and 85%, respectively. The successful development of the RoadModeler suggests that the integration of multiple vision cues potentially offers a solution to simple and fast acquisition of road information. Recommendations are given for further research to be conducted to ensure that this progress goes beyond the prototype stage and towards everyday use

Mapping Road Infrastructure in Developing Countries Applying Remote Sensing and GIS : The Case of the Taita Hills, Kenya

Road transport and infrastructure has a fundamental meaning for the developing world. Poor quality and inadequate coverage of roads, lack of maintenance operations and outdated road maps continue to hinder economic and social development in the developing countries. This thesis focuses on studying the present state of road infrastructure and its mapping in the Taita Hills, south-east Kenya. The study is included as a part of the TAITA-project by the Department of Geography, University of Helsinki. The road infrastructure of the study area is studied by remote sensing and GIS based methodology. As the principal dataset, true colour airborne digital camera data from 2004, was used to generate an aerial image mosaic of the study area. Auxiliary data includes SPOT satellite imagery from 2003, field spectrometry data of road surfaces and relevant literature. Road infrastructure characteristics are interpreted from three test sites using pixel-based supervised classification, object-oriented supervised classifications and visual interpretation. Road infrastructure of the test sites is interpreted visually from a SPOT image. Road centrelines are then extracted from the object-oriented classification results with an automatic vectorisation process. The road infrastructure of the entire image mosaic is mapped by applying the most appropriate assessed data and techniques. The spectral characteristics and reflectance of various road surfaces are considered with the acquired field spectra and relevant literature. The results are compared with the experimented road mapping methods. This study concludes that classification and extraction of roads remains a difficult task, and that the accuracy of the results is inadequate regardless of the high spatial resolution of the image mosaic used in this thesis. Visual interpretation, out of all the experimented methods in this thesis is the most straightforward, accurate and valid technique for road mapping. Certain road surfaces have similar spectral characteristics and reflectance values with other land cover and land use. This has a great influence for digital analysis techniques in particular. Road mapping is made even more complicated by rich vegetation and tree canopy, clouds, shadows, low contrast between roads and surroundings and the width of narrow roads in relation to the spatial resolution of the imagery used. The results of this thesis may be applied to road infrastructure mapping in developing countries on a more general context, although with certain limits. In particular, unclassified rural roads require updated road mapping schemas to intensify road transport possibilities and to assist in the development of the developing world.Tieliikenteellä ja -infrastruktuurilla on keskeinen merkitys kehitysmaissa. Tiestön kattavuudessa, kunnossa, tienpidossa ja kartoituksessa on puutteita, jotka rajoittavat taloudellista ja sosiaalista kehitystä. Tämä tutkimus keskittyy tieinfrastruktuurin nykytilan tutkimiseen ja kartoitukseen Taita Hillsin alueella Kaakkois-Keniassa, sekä tiekartoituksen mahdollisuuksien arviointiin yleisesti kehitysmaissa. Tutkimus on osa Helsingin yliopiston maantieteen laitoksen TAITA-projektia. Tutkimusalueen tieinfrastruktuuria tutkitaan kaukokartoitus- ja paikkatietomenetelmien avulla. Tutkimuksen pääaineistona ovat vuoden 2004 digitaaliset oikeaväri-ilmakuvat, joista muodostetaan ilmakuvamosaiikki. Lisäaineistona ovat SPOT väärävärisatelliittikuva vuodelta 2003, tiepintojen spektrometrimittaukset, olemassa olevat kartta-aineistot sekä aihetta käsittelevä kirjallisuus. Tiestö tulkitaan ilmakuvamosaiikin kolmelta testialueelta pikselipohjaisella ohjatulla luokituksella, objekti-orientoiduilla ohjatuilla luokituksilla sekä visuaalisella tulkinnalla. SPOT kuvalta testialueiden tiestö tulkitaan visuaalisesti. Toisen objekti-orientoidun luokituksen tuloksesta tiestön keskilinjat irrotetaan automaattisella vektoroinnilla. Lopuksi tieverkko kartoitetaan koko ilmakuvamosaiikin alueelta parhaiksi havaituilla menetelmillä ja aineistolla. Spektrometrimittauksien ja kirjallisuuden avulla tarkastellaan eri tiepintojen heijastusarvoja ja teiden spektraalisia ominaisuuksia ja tuloksia verrataan testattujen tulkintamenetelmien tuloksiin. Yhteenvetona voidaan sanoa, että tieverkon luokittelu ja irrottaminen on digitaalisilla menetelmillä vaikeaa ja tulokset epätarkkoja ilmakuva-aineiston korkeasta spatiaalisesta resoluutiosta huolimatta. Visuaalinen tulkinta ja digitointi on toistaiseksi yksinkertaisin, tarkin ja validein tutkituista menetelmistä. Tietyillä tiepinnoilla on koostumuksen ja rakennusmateriaalien takia samankaltaiset heijastusominaisuudet muun maanpeitteen- ja maankäytön kanssa, mikä vaikuttaa etenkin digitaalisten tulkintamenetelmien tuloksiin. Teiden kartoitusta vaikeuttaa myös runsas kasvillisuus, puiden latvuskerros, pilvet, varjot, heikko kontrasti ympäristöön ja teiden kapeus suhteessa käytetyn aineiston spatiaaliseen resoluutioon. Tämän tutkimuksen tuloksia ja menetelmiä voidaan soveltaa tietyin rajoituksin myös laajempaan, kehitysmaiden tiekartoituksen kontekstiin. Erityisesti maaseutujen luokittelemattomat tiet ovat ajantasaisen tiekartoituksen tarpeessa tieliikenteen tehostamiseksi ja kehityksen edesauttamiseksi kehitysmaissa