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  • 1.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Assessing the Impact of Landscape Dynamics on the Terrestrial Biodiversity Using Multisensor Renmote Sensing Project #: DNR 151/05 & DNR 151/05:2: A Project Report Submitted to the Swedish National Space Board2010Report (Other academic)
  • 2.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    ENVISAT ASAR Dual-Polarization Temporal Backscatter Profiles of Urban Land Covers2005In: The 9th International Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS) , 2005Conference paper (Other academic)
  • 3.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    ENVISAT ASAR for Land Cover Mapping and Change Detection: A Report Submitted to the Swedish National Space Board2006Report (Other academic)
  • 4.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Fusion of Spaceborne SAR and Optical Data for Urbanization Monitoring Project #: DNR 144-08: A Project Report Submitted to the Swedish National Space Board2010Report (Other academic)
  • 5.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Multitemporal ERS-1 SAR and Landsat TM data for agricultural crop classification: comparison and synergy2003In: Canadian journal of remote sensing, ISSN 0703-8992, E-ISSN 1712-7971, Vol. 29, no 4, p. 518-526Article in journal (Refereed)
    Abstract [en]

    The objective of this research was to evaluate the synergistic effects of multitemporal European remote sensing satellite 1 (ERS-1) synthetic aperture radar (SAR) and Landsat thematic mapper (TM) data for crop classification using a per-field artificial neural network (ANN) approach. Eight crop types and conditions were identified: winter wheat, corn (good growth), corn (poor growth), soybeans (good growth), soybeans (poor growth), barley/oats, alfalfa, and pasture. With the per-field approach using a feed-forward ANN, the overall classification accuracy of three-date early- to mid-season SAR data improved almost 20%, and the best classification of a single-date (5 August) SAR image improved the overall accuracy by about 26%, in comparison to a per-pixel maximum-likelihood classifier (MLC). Both single-date and multitemporal SAR data demonstrated their abilities to discriminate certain crops in the early and mid-season; however, these overall classification accuracies (<60%) were not sufficiently high for operational crop inventory and analysis, as the single-parameter, high-incidence-angle ERS-1 SAR system does not provide sufficient differences for eight crop types and conditions. The synergy of TM3, TM4, and TM5 images acquired on 6 August and SAR data acquired on 5 August yielded the best per-field ANN classification of 96.8% (kappa coefficient = 0.96). It represents an 8.3% improvement over TM3, TM4, and TM5 classification alone and a 5% improvement over the per-pixel classification of TM and 5 August SAR data. These results clearly demonstrated that the synergy of TM and SAR data is superior to that of a single sensor and the ANN is more robust than MLC for per-field classification. The second-best classification accuracy of 95.9% was achieved using the combination of TM3, TM4, TM5, and 24 July SAR data. The combination of TM3, TM4, and TM5 images and three-date SAR data, however, only yielded an overall classification accuracy of 93.89% (kappa = 0.93), and the combination of TM3, TM4, TM5, and 15 June SAR data decreased the classification accuracy slightly (88.08%; kappa = 0.86) from that of TM alone. These results indicate that the synergy of satellite SAR and Landsat TM data can produce much better classification accuracy than that of Landsat TM alone only when careful consideration is given to the temporal compatibility of SAR and visible and infrared data.

  • 6.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Multitemporal remote sensing: Current status, trends and challenges2016In: Remote Sensing and Digital Image Processing, 2016, p. 1-18Conference paper (Refereed)
    Abstract [en]

    Our planet is facing unprecedented environmental challenges including rapid urbanization, deforestation, pollution, loss of biodiversity, sea-level rising, melting polar ice-caps and climate change. With its synoptic view and the repeatability, remote sensing offers a powerful and effective means to observe and monitor our changing planet at local, regional and global scale. Since the launch of Landsat-1 in 1972, numerous Earth Observation satellites have been launched providing large volumes of multitemporal data acquired by multispectral, hyperspectral, passive microwave, synthetic aperture radar (SAR), and LiDAR sensors. This chapter first presents an overview of the Earth Observation sensors and trends in multitemporal observation capacity. Then the current status, challenges and opportunities of multitemporal remote sensing are discussed. Finally the synopsis of the book is provided covering a wide array of methods and techniques in processing and analysis of multitemporal remotely sensed images as well as a variety of application examples in both land and aquatic environments.

  • 7.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Spaceborne SAR for Analysis of Urban Environment and Detection of Human Settlements Project #: DNR 125-0: A Project Report Submitted to the Swedish National Space Board2010Report (Other academic)
  • 8.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Ahmed, Kazi Ishtiak
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    ENVISAT ASAR for Land Cover Mapping and Change Detection in the Rural-Urban Fringe of the Greater Toronto Area2007In: Proceedings, 5th International Symposium on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Applications, 2007Conference paper (Other academic)
  • 9.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    et, al.
    ViSuCity: A Visual Sustainable City Planning Tool2010Report (Other academic)
  • 10.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Fredman, David
    Jonsson, Martin
    Svensson, Leif
    Multi-Criteria Evaluations for Improved Placement of Defibrillators: Preliminary Results2013In: Circulation, ISSN 0009-7322, E-ISSN 1524-4539, Vol. 128, no 22, p. 78-Article in journal (Other academic)
  • 11.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gamba, P.
    EO4Urban: First-year results on Sentinel-1A SAR and Sentinel-2A MSI data for global urban services2016In: European Space Agency, (Special Publication) ESA SP, 2016Conference paper (Refereed)
    Abstract [en]

    The overall objective of this research is to evaluate multitemporal Sentinel-1A SAR and Sentinel-2A MSI data for global urban services using innovative methods and algorithms, namely KTH-Pavia Urban Extractor, a robust algorithm for urban extent extraction and KTHSEG, a novel object-based classification method for detailed urban land cover mapping. Ten cities around the world in different geographical and environmental conditions were selected as study areas. Large volume of Sentinel-1A SAR and Sentinel-2A MSI data were acquired during vegetation season in 2015 and 2016. The preliminary urban extraction results showed that urban areas and small towns could be well extracted using multitemporal Sentinel-1A SAR data with the KTH-Pavia Urban Extractor. For urban land cover mapping, multitemporal Sentinel-1A SAR data alone yielded an overall classification accuracy of 60% for Stockholm. Sentinel-2A MSI data as well as the fusion of Sentinel-1A SAR and Sentinel-2A MSI data, however, produced much higher classification accuracies, both reached 80%.

  • 12.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Gamba, Paolo
    University of Pavia.
    Gong, Peng
    Du, Peijun
    Satellite Monitoring of Urbanization in China for Sustainable Development: Preliminary Results2010In: Proceedings of ESA Living Planet Symposium, 2010Conference paper (Other academic)
  • 13.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gamba, Paolo
    Gong, Peng
    Du, Peijun
    Satellite Monitoring of Urbanization in China for Sustainable Development: The Dragon 'Urbanization' Project2011In: IEEE EarthzineArticle in journal (Other (popular science, discussion, etc.))
  • 14.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gamba, Paolo
    Jacob, Alexander
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Salentining, A.
    Multitemporal, multi-rsolution SAR data for urbanization mapping and monitoring: midterm results2014In: Proceedings of the Dragon 3 mid-term results Symposium, ESA , 2014Conference paper (Other academic)
  • 15.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gong, P.
    Gamba, P.
    Du, P.
    Satellite monitoring of urbanization in China for sustainable development: Final results2013In: European Space Agency, (Special Publication) ESA SP, Volume 704 SP, 2013, European Space Agency, 2013Conference paper (Refereed)
    Abstract [en]

    The overall objectives of this research are to investigate spaceborne SAR data, optical data and fusion of SAR and optical data for urbanization monitoring in China, and to assess the impact of urbanization on the environment for sustainable development. Effective segmentation and classification methods for urban extent extraction and land cover mapping were developed. Several change detection algorithms and approaches using SAR and optical data were evaluated. Further, synergistic effects of multisensor SAR data as well as ASAR and HJ-1B data are examined. The results show that the developed methods were effective for urban extent extraction, land cover mapping and change detection. The fusion of multisensor spaceborne SAR as well as fusion of ASAR and HJ-1 data were beneficial for urban land cover mapping. The spatiotemporal patterns of urbanization in China were analyzed. The results show that rapid urbanization in Yangtze River Delta, Jingjinji and Pearl River Delta has a significant impact on the environment in terms of landscape fragmentation and ecosystem services.

  • 16.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Howarth, P. J.
    Multitemporal ERS-1 SAR data for crop classification: a sequential-masking approach1999In: Canadian journal of remote sensing, ISSN 0703-8992, E-ISSN 1712-7971, Vol. 1999, no 25, p. 438-447, article id 5Article in journal (Refereed)
    Abstract [en]

    Based on photo-interpretation procedures, the technique of sequential masking can be used to differentiate image features using a series of multitemporal images. In this study, a set of nine ERS-1 SAR images is analyzed using this technique to determine the earliest dates for identifying different crop types in an agricultural area of southern Ontario, Canada. SAR temporal backscatter profiles of crops were generated from calibrated radar imagery. Based on these temporal backscatter profiles, per-field classifications using the sequential-masking technique were performed on the early- and mid-season multitemporal SAR data. It was found that using only three images, acquired on May 31, June 16 and July 5, it is possible to differentiate winter wheat, alfalfa/hay, barley/oats, soybeans and corn with an overall validation accuracy of 88.5% and a Kappa coefficient of 0.85.

  • 17.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Howarth, P. J.
    Orbital effects on ERS-1 SAR temporal backscatter profiles of agricultural crops1997In: ESA SP, 1997, p. 179-183Conference paper (Other academic)
  • 18.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Howarth, P. J.
    Orbital effects on ERS-1 SAR temporal backscatter profiles of agricultural crops1998In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 19, no 17, p. 3465-3470Article in journal (Refereed)
    Abstract [en]

    Multi-temporal radar backscatter characteristics of crops and their underlying soils were analysed for an agricultural area in south-western Ontario, Canada using nine dates of ERS-1 SAR imagery acquired during the 1993 growing season. From the calibrated data, SAR temporal backscatter profiles were generated for each crop type. The results indicate that small changes in incidence-angle can have strong impacts on radar backscatter. Thus, attention must be given to local incidence-angle effects when using ERS-1 SAR data,especially when comparing backscatter coefficients of the same area from different scenes or different areas within the same scene.

  • 19.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Hu, Hongtao
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    RADARSAT Fine-Beam SAR Data for Land-Cover Mapping and Change Detection in the Rural-Urban Fringe of the Greater Toronto Area2007In: Proceedings, Urban Remote Sensing Joint Event, 2007, 2007Conference paper (Other academic)
    Abstract [en]

    This research investigates the capability of the multitemporal RADARSAT Fine-Beam C-HH SAR imagery for landuse/land-cover mapping and change detection in therural-urban fringe of the Greater Toronto Area (GTA). Five-date RADARSAT fine-beamSAR images were acquired during May to August in 2002. One scene of Landsat TM imagery was acquired in 1988 for change detection. The major landuse/land-coverclasses were high-density built-up areas, low-density built-up areas, roads, forests, parks, golf courses, water and three types of agricultural lands. These ten classes were chosen to characterize the complex landuse/land-cover types in the rural-urban fringe of the GTA. The results demonstrated that, for identifying landuse/land-cover classes, five-date raw SAR imagery yielded very poor result due to speckles. Much better results were achieved with combined Mean, Standard Deviation and Correlation texture images using artificial neural networks (ANN) and with raw images using object-based classification. The change detection procedure was able to identify the areas of significant changes, for example, major new roads, new low-density and high-density built up areas and golf courses, even though the overall accuracy of the change detection was rather low. 

  • 20.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Hu, Hongtao
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Rangel, Irene
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Fusion of RADARSAT fine-beam SAR and QuickBird data for land-cover mapping and change detection2007In: Geoinformatics 2007Proceedings of SPIE - The International Society for Optical Engineering: Remotely Sensed Data And Information, Pts 1 And 2 / [ed] Ju, W; Zhao, S, 2007, Vol. 6752, p. H7522-H7522Conference paper (Refereed)
    Abstract [en]

    The objective of this research is to evaluate multitemporal RADARSAT Fine-Beam C-HH SAR data, QuickBird MS data, and fusion of SAR and MS for urban land-cover mapping and change detection One scene of QuickBird imagery was acquired on July 18, 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. Landsat TM imagery from 1988 was used for change detection. QucikBird images were classified using an object-based and rule-based approach. RADARSAR SAR texture images were classified using a hybrid approach. The results demonstrated that, for identifying 19 land-cover classes, object-based and rule-based classification of Quickbird data yielded an overall classification accuracy of 86.7% (kappa 0.857). For identifying I I land-cover classes, ANN classification of the combined Mean, Standard Deviation and Correlation texture images yielded an overall accuracy: 71.4%, (Kappa: 0.69). The hybrid classification of RADARSAT fine-beam SAR data improved the ANN classification accuracy to 83.56% (kappa: 0.803). Decision level fusion of RADARSAT SAR and QuickBird data improved the classification accuracy of several land cover classes. The post-classification change detection was able to identify the areas of significant change, for example, major new roads, new low-density and high-density, builtup areas and golf courses, even though the change detection results contained large amount of noise due to classification errors of individual images. QuickBrid classification result was able add detailed change information to the major changes identified.

  • 21.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Hu, Hongtao
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Rangel, Irene M.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach2010In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 31, no 6, p. 1391-1410Article in journal (Refereed)
    Abstract [en]

    The objective of this research is to evaluate Quickbird multi-spectral (MS) data, multi-temporal RADARSAT Fine-Beam C-HH synthetic aperture radar (SAR) data and fusion of Quickbird MS and RADARSAT SAR for urban land-use/land-cover mapping. One scene of Quickbird multi-spectral imagery was acquired on 18 July 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August 2002. Quickbird MS images and RADARSAT SAR data were classified using an object-based and rule-based approach. The results demonstrated that the object-based and knowledge-based approach was effective in extracting urban land-cover classes. For identifying 16 land-cover classes, object-based and rule-based classification of Quickbird MS data yielded an overall classification accuracy of 87.9% (kappa: 0.868). For identifying 11 land-cover classes, object-based and rule-based classification of RADARSAT SAR data yielded an overall accuracy: 86.6% (kappa: 0.852). Decision level fusion of Quickbird classification and RADARSAT SAR classification was able to take advantage of the best classifications of both optical and SAR data, thus significantly improving the classification accuracies of several land-cover classes (25% for pasture, 19% for soybeans, 17% for rapeseeds) even though the overall classification accuracy of 16 land-cover classes increased only slightly to 89.5% (kappa: 0.885).

  • 22.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Jacob, Alexander
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Fusion of multitemporal spaceborne SAR and optical data for urban mapping and urbanization monitoring2016In: Remote Sensing and Digital Image Processing, ISSN 1567-3200, p. 107-123Article in journal (Refereed)
    Abstract [en]

    The overall objective of this research is to evaluate multitemporal spaceborne SAR and optical data for urban land cover mapping and urbanization monitoring. Multitemporal Sentinel-1A SAR and historical ERS SAR and ENVISAT ASAR data as well as HJ-1B multispectral data were acquired in Beijing, Chendgdu and Nanchang, China where rapid urbanization has taken place. KTHSEG, a novel object-based classification method is adopted for urban land cover mapping while KTH-Pavia Urban Extractor, a robust algorithm is improved for urban extent extraction and urbanization monitoring. The research demonstrates that, for urban land cover classification, the fusion of multitemporal SAR and optical data is superior to SAR or optical data alone. The second best classification result is achieved using fusion of 4-date SAR and one HJ-1B image. The results indicate that carefully selected multitemporal SAR dataset and its fusion with optical data could produce nearly as good classification accuracy as the whole multitemporal dataset. The results also show that KTH-SEG, the edge-aware region growing and merging segmentation algorithm, is effective for classification of SAR, optical and their fusion. KTH-SEG outperforms eCognition, the commonly used commercial software, for image segmentation and classification of linear features. For Urban extent extraction, single-date and multitemporal SAR data including ERS SAR, ENVISAT ASAR and Sentinel-1A SAR achieved very promising results in all study areas using the improved KTH-Pavia Urban Extractor. The results showed that urban areas as well as small towns and villages could be well extracted using multitemporal Sentinel-1A SAR data while major urban areas could be well extracted using a single-date single-polarization SAR image. The results clearly demonstrate that multitemporal SAR data are cost- and time-effective way for monitoring spatiotemporal patterns of urbanization. © Springer International Publishing AG 2016.

  • 23.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Jacob, Alexander
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Object-Based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ-1B Multispectral Data for Urban Land-Cover Mapping2013In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 51, no 4, p. 1998-2006Article in journal (Refereed)
    Abstract [en]

    The objectives of this research are to develop robust methods for segmentation of multitemporal synthetic aperture radar (SAR) and optical data and to investigate the fusion of multitemporal ENVISAT advanced synthetic aperture radar (ASAR) and Chinese HJ-1B multispectral data for detailed urban land-cover mapping. Eight-date multiangle ENVISAT ASAR images and one-date HJ-1B charge-coupled device image acquired over Beijing in 2009 are selected for this research. The edge-aware region growing and merging (EARGM) algorithm is developed for segmentation of SAR and optical data. Edge detection using a Sobel filter is applied on SAR and optical data individually, and a majority voting approach is used to integrate all edge images. The edges are then used in a segmentation process to ensure that segments do not grow over edges. The segmentation is influenced by minimum and maximum segment sizes as well as the two homogeneity criteria, namely, a measure of color and a measure of texture. The classification is performed using support vector machines. The results show that our EARGM algorithm produces better segmentation than eCognition, particularly for built-up classes and linear features. The best classification result (80%) is achieved using the fusion of eight-date ENVISAT ASAR and HJ-1B data. This represents 5%, 11%, and 14% improvements over eCognition, HJ-1B, and ASAR classifications, respectively. The second best classification is achieved using fusion of four-date ENVISAT ASAR and HJ-1B data (78%). The result indicates that fewer multitemporal SAR images can achieve similar classification accuracy if multitemporal multiangle dual-look-direction SAR data are carefully selected.

  • 24.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Jacob, Alexander
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Gamba, Paolo
    University of Pavia, Pavia, Italy.
    Spaceborne SAR Data for Global Urban Mapping at 30m Resolution Utilizing a Robust Urban Extractor2015In: ISPRS journal of photogrammetry and remote sensing (Print), ISSN 0924-2716, E-ISSN 1872-8235, Vol. 103Article in journal (Refereed)
    Abstract [en]

    With more than half of the world population now living in cities and 1.4 billion more people expected to move into cities by 2030, urban areas pose significant challenges on local, regional and global environment. Timely and accurate information on spatial distributions and temporal changes of urban areas are therefore needed to support sustainable development and environmental change research. The objective of this research is to evaluate spaceborne SAR data for improved global urban mapping using a robust processing chain, the KTH-Pavia Urban Extractor. The proposed processing chain includes urban extraction based on spatial indices and Grey Level Co-occurrence Matrix (GLCM) textures, an existing method and several improvements i.e., SAR data preprocessing, enhancement, and post-processing. ENVISAT Advanced Synthetic Aperture Radar (ASAR) C-VV data at 30m resolution were selected over 10 global cities and a rural area from six continents to demonstrated robustness of the improved method. The results show that the KTH-Pavia Urban Extractor is effective in extracting urban areas and small towns from ENVISAT ASAR data and built-up areas can be mapped at 30m resolution with very good accuracy using only one or two SAR images. These findings indicate that operational global urban mapping is possible with spaceborne SAR data, especially with the launch of Sentinel-1 that provides SAR data with global coverage, operational reliability and quick data delivery.

  • 25.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Jakobsson, Pontus
    Kjelldhal, Lars
    KTH, School of Computer Science and Communication (CSC), Human - Computer Interaction, MDI (closed 20111231).
    Ranhagen, Ulf
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Urban and Regional Studies.
    Visualization in ViSuCity: a tool for sustainable city planning2011In: SIGRAD2011, 2011, p. 105-109Conference paper (Refereed)
    Abstract [en]

    This paper gives an overview of several aspects of visualization for city planning as they were used in the projectViSuCity. The overall objective of ViSuCity is to develop an effective web-based, interactive visualization demonstrator,ViSuCity, to support sustainable city planning in terms of information sharing, analysis, development,presentation and communication of ideas and proposals throughout the city planning processes. In this paper, wediscuss and show some results regarding LOD, scalability, streaming, and examples of visualization of roads, etcthat are important for city planning.

  • 26.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Jian, L.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Kazi, I.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Ihse, M.
    Stockholm University.
    Synergy of ENVISAT ASAR and MERIS Data for Landuse/Land-Cover Mapping: Earsel symposium, Warsaw, Poland2006Other (Other academic)
  • 27.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Marullo, Salvatore
    Eklundh, Lars
    European Remote Sensing: progress, challenges, and opportunities2017In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 38, no 7, p. 1759-1764Article in journal (Refereed)
  • 28.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Niu, Xin
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    RADARSAT-2 Polarimetric SAR Data for Urban Land Cover Classification: A Multitemporal Dual-Orbit Approach2011In: / [ed] Lena Halounová, 2011, p. 450-456Conference paper (Refereed)
    Abstract [en]

    This research investigates multitemporal dual-orbit RADARSAT-2 polarimetric SAR data for urban land cover classification using an object-based support vector machine (SVM). Six-date RADARSAT-2 high-resolution SAR data in both ascending and descending orbits were acquired in the rural-urban fringe of the Greater Toronto Area during the summer of 2008. The major landuse/land-cover classes include high-density residential area, low-density residential area, industrial and commercial area, construction site, park, golf course, forest, pasture, water and two types of agricultural crops. The results show that multitemporal SAR data improve urban land cover classification and the best classification result is achieved using data from all six-dates. However, similar accuracies could be achieved using only three-date data from both ascending and descending orbits with relatively longer temporal span. Combinations of SAR data with relatively short temporal span are observed to yield lower classification accuracy. Similarly, combinations of SAR data from either ascending or descending orbit alone yield lower accuracy than the combinations of ascending and descending data. The results indicate that the combination of both the ascending and descending spaceborne SAR data with appropriate temporal span are suitable for urban land cover mapping.

  • 29.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Wallin, Johan
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Fusion of ALOS PALSAR and SPOT HRG Data for Urban Land-Cover Mapping in Stockholm:  2009Conference paper (Other academic)
  • 30.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Webber, Luke
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gamba, P.
    Paganini, M.
    EO4Urban: Sentinel-1A SAR and Sentinel-2A MSI data for global urban services2017In: 2017 Joint Urban Remote Sensing Event, JURSE 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, article id 7924550Conference paper (Refereed)
    Abstract [en]

    The overall objective of this research is to evaluate multitemporal Sentinel-1A SAR and Sentinel-2A MSI data for global urban services using innovative methods and algorithms, namely KTH-Pavia Urban Extractor, a robust algorithm for urban extent extraction and KTH-SEG, a novel object-based classification method for detailed urban land cover mapping. Ten cities around the world in different geographical and environmental conditions were selected as study areas. Large volumes of Sentinel-1A SAR and Sentinel-2A MSI data were acquired during the vegetation season in 2015 and 2016. The urban extraction results showed that urban areas and small towns could be well extracted using multitemporal Sentinel-1 SAR, Sentinel-2A MSI data and their fusion using the Urban Extractors developed within the project. For urban land cover mapping, multitemporal Sentinel-1A SAR data alone yielded an overall classification accuracy of 60% for Stockholm. Sentinel-2A MSI data as well as the fusion of Sentinel-1A SAR and Sentinel-2A MSI data, however, produced much higher classification accuracies, both reached 80%.

  • 31.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Wu, Qiaojun
    RADARSAT SAR data for landuse/land-cover classification in the rural-urban fringe of the greater Toronto area2005In: Proceedings 2005: The 8th AGILE International Conference on Geographic Information Science, AGILE 2005, 2005Conference paper (Refereed)
    Abstract [en]

    This research investigates the capability of the multitemporal RADARSAT Fine-Beam C-HH SAR imagery for extracting landuse/land-cover information in the rural-urban fringe of the Greater Toronto Area (GTA) using various image processing techniques and classification algorithms. Five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. The major landuse/land-cover classes were high-density built-up areas, low-density built-up areas, roads, forests, parks, golf courses, water and three types of agricultural lands. These ten classes were chosen to characterize the complex landuse/land-cover types in the rural-urban fringe of the GTA. The results demonstrated that, for identifying landuse/land-cover classes, five-date raw SAR imagery yielded very poor result due to speckles. The best result was achieved for combined Mean, Standard Deviation and Correlation texture images using artificial neural networks (ANN) (overall accuracy: 89.7% and Kappa: 0.886). These high accuracies indicated that RADARSAT fine-beam SAR has the potential for operational landuse/land-cover mapping in urban environments.

  • 32.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Yousif, Osama A.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Change detection techniques: A review2016In: Remote Sensing and Digital Image Processing, Springer, 2016, p. 19-43Conference paper (Refereed)
    Abstract [en]

    With its synoptic view and the repeatability, satellite remote sensing can provide timely, accurate and consistent information about earth’s surface for costeffective monitoring of environmental changes. In this chapter, recent development in change detection techniques using multitemporal remotely sensed images were reviewed. The chapter covers change detection methods for both optical and SAR images. Various aspects of change detection processes were presented including data preprocessing, change image generation and change detection algorithms such as unsupervised and supervised change detection as well as pixel-based and objectbased change detection. The review shows that significant progress has been made in the field of change detection and innovative methods have been developed for change detection using both multitemporal SAR and optical data. Attempts have been made for change detection using multitemporal multisensor/cross-sensor images. The review also identified a number of challenges and opportunities in change detection.

  • 33.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Yousif, Osama A.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Multitemporal Spaceborne SAR Data for Urban Change Detection in China2012In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 5, no 4, p. 1087-1094Article in journal (Refereed)
    Abstract [en]

    The objective of this research is to examine effective methods for urban change detection using multitemporal spaceborne SAR data in two rapid expanding cities in China. One scene of ERS-2 SAR C-VV image was acquired in Beijing in 1998 and in shanghai in 1999 respectively and one scene of ENVISAT ASAR C-VV image was acquired in near-anniversary dates in 2008 in Beijing and Shanghai. To compare the SAR images from different dates, a modified ratio operator that takes into account both positive and negative changes was developed to derive a change image. A generalized version of Kittler-Illingworth minimum-error thresholding algorithm was then tested to automatically classify the change image into two classes, change and no change. Various probability density functions such as Log normal, Generalized Gaussian, Nakagami ratio, and Weibull ratio were investigated to model the distribution of the change and no change classes. The results showed that Kittler-Illingworth algorithm applied to the modified ratio image is very effective in detecting temporal changes in urban areas using SAR images. Log normal and Nakagami density models achieved the best results. The Kappa coefficients of these methods were of 0.82 and 0.71 for Beijing and Shanghai respectively while the false alarm rates were 2.7% and 4.75%. The findings indicated that the change accuracies obtained using Kittler-Illingworth algorithm vary depending on how the assumed conditional class density function fits the histograms of change and no change classes.

  • 34.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Yousif, Osama A
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Multitemporal Spaceborne SAR data for urbanization monitoring in China: Preliminary Result2010In: Proceedings, ESA/MOST Dragon 2 Program Midterm Symposium, 2010Conference paper (Other academic)
    Abstract [en]

    The objective of this research is to investigate multitemporal spaceborne SAR data for urbanization monitoring in China. A generalized version of Kittler- Illingworth minimum-error thresholding algorithm, that takes into account the non-Gaussian distribution of SAR images, was tested to automatically classify the change variable derived from SAR multitemporal images into two classes, change and no change. A modified ratio operator was examined for identifying both positive and negative changes by comparing the multitemporal SAR images on a pixel-by-pixel basis. Various probability density functions such as Log normal, Generalized Gaussian, Nakagami ratio, and Weibull ratio models were tested to model the distribution of the change and no change classes. The preliminary results showed that this unsupervised change detection algorithm is very effective in detecting temporal changes in urban areas using multitemporal SAR images. The initial findings indicated that change detection accuracy varies depending on how the assumed conditional class density function fits the histograms of change and no change classes.

  • 35.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Yousif, Osama A
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Unsupervised Change Detection Using Multitemporal Spaceborne SAR Data: A Case Study in Beijing2011In: 2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings, IEEE , 2011, p. 161-164Conference paper (Refereed)
    Abstract [en]

    The objective of this research is to examine unsupervised change detection methods using multitemporal spaceborne SAR data for urbanization monitoring in Beijing. One scene of ENVISAT ASAR C-VV image was acquired in July, 2008 and one scene of ERS-2 SAR C-VV image was acquired in July, 1998. To compare the two SAR images, a modified ratio operator that takes into account both positive and negative changes was developed to derive a change image. A generalized version of Kittler-Illingworth minimum-error thresholding algorithm was then tested to automatically classify the change image into two classes, change and no-change. Various probability density functions such as Log normal, Generalized Gaussian, Nakagami ratio, and Weibull ratio were investigated to model the distribution of the change and no-change classes. The preliminary results showed that Kittler-Illingworth algorithm applied to the modified ratio image is very effective in detecting temporal changes in urban areas using SAR images. Log normal and Nakagami density models achieved the best results. The Kappa coefficients of the these solutions were of 0.82 while the false alarm rates were 2.7%. The initial findings indicated that the accuracy of the change result obtained using Kittler-Illingworth algorithm varies depending on how the assumed conditional class density function fits the histograms of change and no-change classes.

  • 36.
    Ban, Yifang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Yousif, Osama
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Hu, Hongtao
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Fusion of SAR and Optical Data for Urban Land Cover Mapping and Change Detection2014In: Global Urban Monitoring and Assessment through Earth Observation / [ed] Qihao Weng, CRC Press, 2014Chapter in book (Refereed)
  • 37. Cartalis, C.
    et al.
    Asimakopoulos, D. N.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Bao, Y.
    Bi, Y.
    Defourny, P.
    Del Barrio, G.
    Fan, J.
    Gao, Z.
    Gong, H.
    Gong, J.
    Gong, P.
    Li, C.
    Pignatti, S.
    Sarris, A.
    Yang, G.
    Earth observation in support of science and applications development in the field "land and Environment": Synthesis results from the ESA-most dragon cooperation Programme2015In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2015, no 7W3, p. 1075-1081Conference paper (Refereed)
    Abstract [en]

    Dragon is a cooperation Programme between the European Space Agency (ESA) and the Ministry of Science and Technology (MOST) of the P.R. China. The Programme, initiated in 2004, focuses on the exploitation of ESA, Third Party Missions (TPM) and Chinese Earth Observation (EO) data for geo-science and applications development in land, ocean and atmospheric applications. In particular, the Programme brings together joint Sino- European teams to investigate 50 thematic projects. In this paper, the results of the research projects1 in the thematic field "Land and Environment" will be briefly presented, whereas emphasis will be given in the assessment of the usefulness of the results for an integrated assessment of the state of the environment in the respective study areas. Furthermore new knowledge gained in such fields as desertification assessment, drought and epidemics' monitoring, forest modeling, cropwatch monitoring, climate change vulnerability (including climate change adaptation and mitigation plans), urbanization monitoring and land use/cover change assessment and monitoring, will be presented. Such knowledge will be also linked to the capacities of Earth Observation systems (and of the respective EO data) to support the temporal, spatial and spectral requirements of the research studies. The potential of DRAGON to support such targets as "technology and knowledge transfer at the bilateral level", "common EO database for exploitation" and "data sharing and open access data policy" will be also presented. Finally special consideration will be given in highlighting the replication potential of the techniques as developed in the course of the projects, as well as on the importance of the scientific results for environmental policy drafting and decision making.

  • 38. Claesson, A.
    et al.
    Fredman, D.
    Svensson, L.
    Ringh, M.
    Hollenberg, J.
    Nordberg, P.
    Rosenqvist, M.
    Djarv, T.
    Österberg, S.
    Lennartsson, J.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Unmanned aerial vehicles (drones) in out-of-hospital-cardiac-arrest2016In: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, ISSN 1757-7241, E-ISSN 1757-7241, Vol. 24, no 1, article id 124Article in journal (Refereed)
    Abstract [en]

    Background: The use of an automated external defibrillator (AED) prior to EMS arrival can increase 30-day survival in out-of-hospital cardiac arrest (OHCA) significantly. Drones or unmanned aerial vehicles (UAV) can fly with high velocity and potentially transport devices such as AEDs to the site of OHCAs. The aim of this explorative study was to investigate the feasibility of a drone system in decreasing response time and delivering an AED. Methods: Data of Global Positioning System (GPS) coordinates from historical OHCA in Stockholm County was used in a model using a Geographic Information System (GIS) to find suitable placements and visualize response times for the use of an AED equipped drone. Two different geographical models, urban and rural, were calculated using a multi-criteria evaluation (MCE) model. Test-flights with an AED were performed on these locations in rural areas. Results: In total, based on 3,165 retrospective OHCAs in Stockholm County between 2006-2013, twenty locations were identified for the potential placement of a drone. In a GIS-simulated model of urban OHCA, the drone arrived before EMS in 32 % of cases, and the mean amount of time saved was 1.5 min. In rural OHCA the drone arrived before EMS in 93 % of cases with a mean amount of time saved of 19 min. In these rural locations during (n = 13) test flights, latch-release of the AED from low altitude (3-4 m) or landing the drone on flat ground were the safest ways to deliver an AED to the bystander and were superior to parachute release. Discussion: The difference in response time for EMS between urban and rural areas is substantial, as is the possible amount of time saved using this UAV-system. However, yet another technical device needs to fit into the chain of survival. We know nothing of how productive or even counterproductive this system might be in clinical reality. Conclusions: To use drones in rural areas to deliver an AED in OHCA may be safe and feasible. Suitable placement of drone systems can be designed by using GIS models. The use of an AED equipped drone may have the potential to reduce time to defibrillation in OHCA.

  • 39.
    Deng, Juan
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Liu, Jinshuo
    Li, Li
    Niu, Xin
    Zou, Bin
    Hierarchical Segmentation of Multitemporal RADARSAT-2 SAR Data Using Stationary Wavelet Transform and Algebraic Multigrid Method2014In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 52, no 7, p. 4353-4363Article in journal (Refereed)
    Abstract [en]

    The objective of this paper is to develop a new effective method for hierarchical segmentation of multitemporal ultrafine-beam synthetic aperture radar (SAR) data in urban areas. Multitemporal RADARSAT-2 ultrafine-beam high-resolution horizontal transmit and horizontal receive-Synthetic Aperture Radar (HH-SAR) images acquired in the rural-urban fringe of the Greater Toronto Area during the summer of 2008 are selected for this research. Stationary wavelet transform (SWT) and algebraic multigrid (AMG) method are proposed for segmentation of SAR data. SWT is applied for decomposition of multitemporal SAR images in image preprocessing. The hierarchical and matrix-based AMG method is applied for segmentation. A pyramid of fine-to-coarse grids is constructed by iteration of selecting representative pixels and calculating the interpolation matrix between a fine-level grid and a coarse-level grid. When the pyramid is completed, segments are determined by a top-down scanning based on the interpolation matrices. The AMG techniques provide a complete hierarchical segmentation of SAR data. The experimental results show that our method produces higher accuracy than eCognition.

  • 40.
    Duc, Khanhngo
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.
    Vu, T.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Ushahidi and Sahana Eden Open-Source Platforms to Assist Disaster Relief: Geospatial Components and Capabilities2014In: Geoinformation for Informed Decisions, Springer, 2014, Vol. 199679, p. 163-174Conference paper (Refereed)
    Abstract [en]

    In responses to recent large-scale disaster events, huge amount of ground information have been collected in addition to the synoptic views from satellite images. Different platforms have been in place to facilitate the collection and management of such critical location-based information from the crowd. This study investigated the current implementation of geospatial components and their capabilities in open-source platforms, particularly Ushahidi and Sahana Eden. Using the 2011 Christchurch earthquake data and following the four main functions of a geo-info system: Data input, Geospatial analysis, Data management, and Visualization, the performance of geospatial-components were evaluated by a group of users. The result showed that with rich visualization on interactive map both Sahana Eden and Ushahidi enable emergency managers to track the needs of disaster-affected people. While Ushahidi can only filter incidents records by time or category, geospatial data management of Sahana Eden is proven to be more powerful, allowing emergency managers input different geospatial data such as incidents, organizations, human resource, warehouses, hospitals, shelters, assets, and projects and visualizing all of these features on a map. It also helps to simplify the coordination among aids agencies. However, geospatial analysis is the limitation of both platforms. The findings recommended that data input with more variety of formats and more geospatial analysis functions should be added. Further research will expand to more case studies taking into account the requirements of disaster management practitioners and emergency responders.

  • 41. Fredman, D.
    et al.
    Haas, Jan
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Jonsson, M.
    Svensson, L.
    Djarv, T.
    Hollenberg, J.
    Nordberg, P.
    Ringh, M.
    Claesson, A.
    Use of a geographic information system to identify differences in automated external defibrillator installation in urban areas with similar incidence of public out-of-hospital cardiac arrest: A retrospective registry-based study2017In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 7, no 5, article id e014801Article in journal (Refereed)
    Abstract [en]

    Objectives Early defibrillation in out-of-hospital cardiac arrest (OHCA) is of importance to improve survival. In many countries the number of automated external defibrillators (AEDs) is increasing, but the use is low. Guidelines suggest that AEDs should be installed in densely populated areas and in locations with many visitors. Attempts have been made to identify optimal AED locations based on the incidence of OHCA using geographical information systems (GIS), but often on small datasets and the studies are seldom reproduced. The aim of this paper is to investigate if the distribution of public AEDs follows the incident locations of public OHCAs in urban areas of Stockholm County, Sweden. Method OHCA data were obtained from the Swedish Register for Cardiopulmonary Resuscitation and AED data were obtained from the Swedish AED Register. Urban areas in Stockholm County were objectively classified according to the pan-European digital mapping tool, Urban Atlas (UA). Furthermore, we reclassified and divided the UA land cover data into three classes (residential, non-residential and other areas). GIS software was used to spatially join and relate public AED and OHCA data and perform computations on relations and distance. Results Between 1 January 2012 and 31 December 2014 a total of 804 OHCAs occurred in public locations in Stockholm County and by December 2013 there were 1828 AEDs available. The incidence of public OHCAs was similar in residential (47.3%) and non-residential areas (43.4%). Fewer AEDs were present in residential areas than in non-residential areas (29.4% vs 68.8%). In residential areas the median distance between OHCAs and AEDs was significantly greater than in non-residential areas (288 m vs 188 m, p<0.001). Conclusion The majority of public OHCAs occurred in areas classified in UA as 'residential areas' with limited AED accessibility. These areas need to be targeted for AED installation and international guidelines need to take geographical location into account when suggesting locations for AED installation.

  • 42. Fredman, D.
    et al.
    Svensson, L.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Jonsson, M.
    Hollenberg, J.
    Nordberg, P.
    Ringh, M.
    Rosenqvist, M.
    Lundén, M.
    Claesson, A.
    Expanding the first link in the chain of survival – Experiences from dispatcher referral of callers to AED locations2016In: Resuscitation, ISSN 0300-9572, E-ISSN 1873-1570, Vol. 107, p. 129-134Article in journal (Refereed)
    Abstract [en]

    Introduction Early use of automated external defibrillators (AED) increases survival in cases of out-of-hospital cardiac arrest (OHCA). Dispatchers play important roles in identifying OHCA, dispatching ambulances and providing callers with telephone-assisted cardiopulmonary resuscitation. Guidelines recommend that AED registries be linked to dispatch centres as tools to refer callers to nearby AED. Aim The aim of this study was to investigate to what extent dispatchers, when provided with a tool to display AED locations and accessibility, referred callers to nearby AED. Methods An application providing real-time visualization of AED locations and accessibility was implemented at four dispatch centres in Sweden. Dispatchers were instructed to refer callers to nearby AED when OHCA was suspected. Such cases were prospectively collected, and geographic information systems were used to identify those located ≤100 m from an AED. Audio recordings of emergency calls were assessed to evaluate the AED referral rate. Results Between February and August 2014, 3009 suspected OHCA calls were received. In 6.6% of those calls (200/3009), an AED was ≤100 m from the suspected OHCA. The AED was accessible and the caller was not alone on scene in 24% (47/200) of these cases. In two of those 47 cases (4.3%), the dispatcher referred the caller to the AED. Conclusion Despite a tool for dispatchers to refer callers to a nearby AED, referral was rare. Only a minority of the suspected OHCA cases occurred ≤100 m from an AED. We identified AED accessibility and callers being alone on scene as obstacles for AED referral.

  • 43.
    Furberg, Dorothy
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Satellite Monitoring and Impact Assessment of Urban Growth in Stockholm, Sweden between 1986 and 20062010In: Imagin[e,g] Europe: Proceedings of the 29th Symposium of the European Association of Remote Sensing Laboratories, Chania, Greece / [ed] Ioannis Manakos, Chariton Kalaitzidis, IOS Press, 2010, p. 131-142Conference paper (Refereed)
    Abstract [en]

    The objective of this research is to investigate the extent of land-cover change in and around Stockholm from 1986 to 2006 and the nature of the resulting landscape fragmentation with a particular focus on the possible environmental impact. Four scenes of SPOT imagery over the Stockholm area were acquired for this study: two on 13 June 1986, one on 5 August 2006 and one on 4 June 2008. Various image processing and classification algorithms were tested and compared. The best classification results were obtained using an object-based and rule-based approach with texture measures as well as spectral data as inputs. The image pairs from the two decades were classified into seven land cover categories for Stockholm Municipality, i.e., low-density built-up, high-density built-up, industrial areas, open land, forest, mixed forest and open land, and water. The overall accuracies were 93% (kappa: 0.91) for 1986 and 97% (kappa: 0.96) for 2006. Landscape fragmentation and change was evaluated using spatial metrics. The spatial metric results reveal that urban areas increased at the expense of non-built up areas by around 2% both on the municipal and regional levels. The 2006/2008 classification gives evidence of being a more fragmented landscape than that of 1986. While urban areas have become denser within Stockholm municipality, which is in line with the region's development policy, more natural land cover types have at the same time been eroded; a development not in line with the regional goal of maintaining the area's green spaces. The classification technique used on the municipality will be expanded to the region as a whole, and regional trends and consequent recommendations will be the focus of future research

  • 44.
    Furberg, Dorothy
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Satellite Monitoring of Urban Land Cover Change in Stockholm Between 1986 and 2006 and Indicator-Based Environmental Assessment2013In: Earth Observation of Global Changes (EOGC), Springer Berlin/Heidelberg, 2013, p. 205-222Chapter in book (Refereed)
    Abstract [en]

    Over the past few decades, there has been substantial urban growth in Stockholm, Sweden, now the largest city in Scandinavia. This research investigates and evaluates the evolution of land cover/use change in Stockholm between 1986 and 2006 with a particular focus on what impact urban growth has had on the environment using indicators derived from remote sensing and environmental data. Four scenes of SPOT imagery over the Stockholm County area were acquired for this study including two on 13 June 1986, one on 5 August 2006 and one on 4 June 2008. These images are classified into seven land cover categories using an object-based and rule-based approach with spectral data and texture measures as inputs. The classification is then used to generate spatial metrics and environmental indicators for evaluation of fragmentation and land cover/land use change. Based on the environmental indicators, an environmental impact index is constructed for both 1986 and 2006 and then compared. The environmental impact index is based on the proportion and condition of green areas important for ecosystem services, proximity of these areas to intense urban land use, proportion of urban areas in their immediate vicinity, and how impacted they are by noise. The analysis units are then ranked according to their indicator values and an average of the indicator rankings gives an overall index score. Results include a ranking of the landscape in terms of environmental impact in 1986 and 2006, as well as an analysis of which units have improved the least or the most and why. The highest ranked units are located most often to the north and east of the central Stockholm area, while the lowest tend to be located closer to the center itself. Yet units near the center also tended to improve the most in ranking over the two decades, which would suggest a convergence towards modest urban expansion and limited environmental impact.

  • 45.
    Furberg, Dorothy
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Satellite Monitoring of Urban Sprawl and Assessing the Impact of Land-Cover Changes in the Greater Toronto Area2008In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2008Conference paper (Other academic)
  • 46.
    Furberg, Dorothy
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Satellite Monitoring of Urban Sprawl and Assessment of its Potential Environmental Impact in the Greater Toronto Area Between 1985 and 20052012In: Environmental Management, ISSN 0364-152X, E-ISSN 1432-1009, Vol. 50, no 6, p. 1068-1088Article in journal (Refereed)
    Abstract [en]

    This research investigates urban sprawl in the Greater Toronto Area (GTA) between 1985 and 2005 and the nature of the resulting landscape fragmentation, particularly with regard to the Oak Ridges Moraine (ORM), an ecologically important area for the region. Six scenes of Landsat TM imagery were acquired in summer of 1985, 1995, and 2005. These images and their texture measures were classified into eight land cover classes with very satisfactory final overall accuracies (93-95 %). Analysis of the classifications indicated that urban areas grew by 20 % between 1985 and 1995 and by 15 % between 1995 and 2005. Landscape fragmentation due to spatio-temporal land cover changes was evaluated using urban compactness indicators and landscape metrics, and results from the latter were used to draw conclusions about probable environmental impact. The indicator results showed that urban proportions increased in nearly all areas outside of the metropolitan center, including on portions of the ORM. The landscape metrics reveal that low density urban areas increased significantly in the GTA between 1985 and 2005, mainly at the expense of agricultural land. The metric results indicate increased vulnerability and exposure to adverse effects for natural and semi-natural land cover through greater contrast and lowered connectivity. The degree of urban perimeter increased around most environmentally significant areas in the region. Changes like these negatively impact species and the regional water supply in the GTA. Further investigation into specific environmental impacts of urban expansion in the region and which areas on the ORM are most at risk is recommended.

  • 47.
    Gao, Liang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Investigations of SAR Polarimetric Features on Land-Cover Classification2008In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2008Conference paper (Other academic)
  • 48.
    Gao, Liang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Multitemporal RADARSAT-2 Polarimetric SAR Data for Urban Land-Cover Mapping2010In: Proceedings of SPIE - The International Society for Optical Engineering, Bellingham: SPIE-INT SOC OPTICAL ENGINEERING , 2010, Vol. 7841Conference paper (Refereed)
    Abstract [en]

    The objective of this research is to evaluate the performance of multitemporal RADARSAT-2 polarimetric SAR data for urban land use/land-cover classification. Three dates of RADARSAT-2 polarimetric SAR data were acquired during the summer of 2008 over the rural-urban fringe of the Greater Toronto Area. The major land-cover types are residential areas, industry areas, bare land, golf courses, forest, and agricultural crops. The methodology used in this study follow the manner that first extracting the features and then carrying out the supervised classification taking the different feature combinations as an input. Support vectors machine is selected to be the classifier. SAR features including amplitude, intensity, long-term coherence, Freeman-Durden decomposition are extracted and compared by evaluating the classification abilities. Long-term coherence plays an important role in building discrimination in this study. The best classification results achieved by using the three dates HH, VH, HV amplitude layers and the coherence map. The overall accuracy is 82.3%. The results indicate that RADARSAT-2 polarimetric data has a potential to urban land-cover classification with the proper feature combinations.

  • 49. Haas, J.
    et al.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Sentinel-1A SAR and sentinel-2A MSI data fusion for urban ecosystem service mapping2017In: Remote Sensing Applications: Society and Environment, ISSN 2352-9385, Vol. 8, p. 41-53Article in journal (Refereed)
    Abstract [en]

    The two main objectives of this study are to evaluate the potential use and synergetic effects of ESA Sentinel-1A C-band SAR and Sentinel-2A MSI data for classification and mapping of ecologically important urban and peri-urban space and to introduce spatial characteristics into ecosystem service analyses based on remotely sensed data. Image resolutions between 5 m and 20 m provided by the Sentinel satellites introduce a new relevant spatial scale in-between high and medium resolution data at which not only urban areas but also their important hinterlands can be effectively and efficiently mapped. Sentinel-1/2 data fusion facilitates both the capture of ecologically relevant details while at the same time also enabling large-scale urban analyses that draw surrounding regions into consideration. The combined use of Sentinel-1A SAR in Interferometric Wide Swath mode and simulated Sentinel-2A MSI (APEX) data is being evaluated in a classification of the Zürich metropolitan area, Switzerland. The SAR image was terrain-corrected, speckle-filtered and co-registered to the simulated Sentinel-2 image. After radiometric and spatial resampling, the fused image stack was segmented and classified by SVM. After post-classification, landscape elements were investigated in terms of spatial characteristics and topological relations that are believed to influence ecosystem service supply and demand, i.e. area, contiguity, perimeter-to-area ratio and distance. Based on the classification results, ecosystem service supplies and demands accounting for spatial and topological patch characteristics were attributed to 14 land cover classes. The quantification of supply and demand values resulted in a positive ecosystem service budget for Zürich. The spatially adjusted service budgets and the original budgets are similar from a landscape perspective but deviate up to 50% on the patch level. The introduction of spatial and topological patch characteristics gives a more accurate impression of ecosystem service supply and demands and their distributions, thus enabling more detailed analyses in complex urban surroundings. The method and underlying data are considered suitable for urban land cover and ecosystem service mapping and the introduction of spatial aspects into relative ecosystem service valuation concepts is believed to add another important aspect in currently existing approaches.

  • 50.
    Haas, Jan
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Ban, Yifang
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Bidecadal urban land cover and ecosystem service changes in three highly urbanized regions2013Conference paper (Refereed)
    Abstract [en]

    In the past 20 years, China has experienced rapid urbanization as a consequence of economic reforms and population growth.  Urbanization is still proceeding at staggering speed. Therefore, the development of effective analytical methods to monitor the unprecedented growth of Chinese cities and the resulting environmental impacts are crucial for urban planning and sustainable development. The overall objective of this research is to investigate urban land cover change between 1990 and 2010 and the resulting effects upon ecosystem services by analysis of multitemporal Landsat 5 and HJ1-A/B images in three highly urbanized regions.

1234 1 - 50 of 156
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