{"title":"Source area estimation of urban air temperatures","authors":"K. Zakšek, B. Bechtel","doi":"10.1109/JURSE.2015.7120499","DOIUrl":"https://doi.org/10.1109/JURSE.2015.7120499","url":null,"abstract":"In the urban areas, the estimation of air temperature from satellite derived land surface temperature is especially difficult due to complex topological settings with a large number of active surfaces and the unknown heat flux source areas. Since the source area approximates the surface area that interacts with the atmosphere at a given position and sensor height, knowledge of source areas is essential for air temperature estimation from satellite data. In this contribution we analyse a week of remote sensing observations made by an infrared camera and in situ air temperature measurements. The first results show that the source area is significantly smaller than operational weather satellites resolution.","PeriodicalId":207233,"journal":{"name":"2015 Joint Urban Remote Sensing Event (JURSE)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121584009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Dwivedi, P. Varshney, A. Tiwari, Avadh Bihari Narayan, Ashutosh Kumar Singh, O. Dikshit, K. Pallav
{"title":"Monitoring of landslides in Nainital, Uttarakhand, India: Validation of PS-InSAR results","authors":"R. Dwivedi, P. Varshney, A. Tiwari, Avadh Bihari Narayan, Ashutosh Kumar Singh, O. Dikshit, K. Pallav","doi":"10.1109/JURSE.2015.7120538","DOIUrl":"https://doi.org/10.1109/JURSE.2015.7120538","url":null,"abstract":"This paper investigates the efficacy of Stanford Method for Persistent Scatterers (StaMPS) to monitor the critical landslides in Nainital township and adjoining areas of state of Uttarakhand, India which has problems due to inadequate drainage management system, seismically active Main Boundary Thrust (MBT) passing from the area, unwanted construction on highly unstable slopes and low insitu strength of rocks. In this research work, 13 descending ENVISAT ASAR C-Band images of Nainital acquired between October 2008 to August 2010 are processed to study the PS-InSAR time series analysis for measuring surface displacement. The case study indicates that StaMPS efficiently extracted sufficient number of PS pixels and detected the slow movement occurring in some regions. In the study area, few critical zones are also identified where time series 1D-Line of Sight (LOS) displacement map shows the maximum deformation (away from the satellite) of about 17 mm/year.","PeriodicalId":207233,"journal":{"name":"2015 Joint Urban Remote Sensing Event (JURSE)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124460720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Detection of buildings in spaceborne TomoSAR point clouds via hybrid region growing and energy minimization technique","authors":"M. Shahzad, Xiaoxiang Zhu","doi":"10.1109/JURSE.2015.7120480","DOIUrl":"https://doi.org/10.1109/JURSE.2015.7120480","url":null,"abstract":"In this paper, an automatic approach is presented to detect/extract buildings from spaceborne TomoSAR point clouds. The approach is systematic and allows robust detection of both tall and low height buildings and is, therefore, well suited for urban monitoring of larger areas from space. The presented approach is illustrated and validated by examples using TomoSAR point clouds generated from a stack of TerraSAR-X high resolution spotlight images covering an area of approximately 1.5 km2 containing mostly moderate sized buildings in the city of Berlin, Germany. The depicted results validate the effectiveness of the proposed approach.","PeriodicalId":207233,"journal":{"name":"2015 Joint Urban Remote Sensing Event (JURSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129700397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluating the health of urban forests using airborne LiDAR","authors":"Andrew A. Plowright, N. Coops, Neal W. Aven","doi":"10.1109/JURSE.2015.7120461","DOIUrl":"https://doi.org/10.1109/JURSE.2015.7120461","url":null,"abstract":"With increased interest in the environmental, psychological and social benefits provided by urban forests, the need for accurate and cost-effective methods for monitoring tree condition within an urban landscape is becoming critical. Light Detection and Ranging (LiDAR) has been used as an efficient tool for measuring tree and forest stand structure in commercial forestry applications for more than a decade, however its application in urban forestry remains nascent. In this paper, we present an approach to detect and delineate individual trees from high density discrete return LiDAR data in an urban context. To do so, the approach exploits tree inventories maintained by city managers to overcome the unique challenges presented by an urban forest, such as a broad range of tree species both native and exotic and age classes. Using tree inventory data to “seed” automated detection and delineation processes, we are able to detect 88.3% of a set of reference trees, and achieve an average similarity ratio of 0.66 between the automatically-delineated and reference crown outlines, with a ratio of 1 indicating a perfect match. By accurately delineating tree crowns, various tree metrics can be extracted from the LiDAR point cloud, which can be used to create maps of tree condition across the city for use in management and monitoring activities.","PeriodicalId":207233,"journal":{"name":"2015 Joint Urban Remote Sensing Event (JURSE)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Structural deformation and non-seasonal motion of single buildings in urban areas revealed by PSI","authors":"S. Gernhardt, R. Bamler","doi":"10.1109/JURSE.2015.7120465","DOIUrl":"https://doi.org/10.1109/JURSE.2015.7120465","url":null,"abstract":"Over the past years, several results of persistent scatterer interferometry (PSI) deformation analyses based on meter resolution synthetic aperture radar (SAR) data stacks have been shown, revealing thermal dilation effects of modern steel constructions. Besides, there exists linear motion even in urban areas of European cities where one would not expect such phenomenon to occur, at least at a first thought. A long term study shows that there are many local areas in Berlin and Munich affected by subsidence or uplift which, fortunately, do not pose a threat on human life. Nevertheless, these effects are very interesting to be analyzed in details, especially as they can be monitored by a PSI analysis, i.e., from space during a period of several years. The examples shown in this paper demonstrate the great potential of space borne high resolution radar sensors for urban monitoring tasks. In addition, a surprising kind of nonlinear - albeit non-destructive - deformation is revealed which is only known from experiments and experience, but has not been measured at a building in situ, yet.","PeriodicalId":207233,"journal":{"name":"2015 Joint Urban Remote Sensing Event (JURSE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132708768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Structured prediction for urban scene semantic segmentation with geographic context","authors":"M. Volpi, V. Ferrari","doi":"10.1109/JURSE.2015.7120490","DOIUrl":"https://doi.org/10.1109/JURSE.2015.7120490","url":null,"abstract":"In this work we address the problem of semantic segmentation of urban remote sensing images into land cover maps. We propose to tackle this task by learning the geographic context of classes and use it to favor or discourage certain spatial configuration of label assignments. For this reason, we learn from training data two spatial priors enforcing different key aspects of the geographical space: local co-occurrence and relative location of land cover classes. We propose to embed these geographic context potentials into a pairwise conditional random field (CRF) which models them jointly with unary potentials from a random forest (RF) classifier. We train the RF on a large set of descriptors which allow to properly account for the class appearance variations induced by the high spatial resolution. We evaluate our approach by an exhaustive experimental comparisons on a set of 20 QuickBird pansharpened multi-spectral images.","PeriodicalId":207233,"journal":{"name":"2015 Joint Urban Remote Sensing Event (JURSE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131728734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Khodadadzadeh, Jun Yu Li, A. Plaza, J. Bioucas-Dias
{"title":"Hyperspectral image classification based on union of subspaces","authors":"M. Khodadadzadeh, Jun Yu Li, A. Plaza, J. Bioucas-Dias","doi":"10.1109/JURSE.2015.7120510","DOIUrl":"https://doi.org/10.1109/JURSE.2015.7120510","url":null,"abstract":"Characterizing mixed pixels is an important topic in the analysis of hyperspectral data. Recently, a subspace-based technique in a multinomial logistic regression (MLR) framework called MLRsub has been developed to address this issue. MLRsub assumes that the training samples of each class live in a single low-dimensional subspace. However, having in mind that materials in a given class tend to appear in groups and the (possible) presence on nonlinear mixing phenomena, a more powerfull model is a union of subspaces. This paper presents a new approach based on union of subspaces for hyperspectral images. The proposed method integrates subspace clustering with MLR method for supervised classification. Our experimental results with an urban hyperspectral image collected by the NSF-funded Center for Airborne Laser Mapping (NCALM) over the University of Houston campus indicate that the proposed method exhibits state-of-the-art classification performance.","PeriodicalId":207233,"journal":{"name":"2015 Joint Urban Remote Sensing Event (JURSE)","volume":"310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132127489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Expanding an urban structure type mapping approach from a subarea to the entire city of Berlin","authors":"M. Voltersen, C. Berger, S. Hese, C. Schmullius","doi":"10.1109/JURSE.2015.7120462","DOIUrl":"https://doi.org/10.1109/JURSE.2015.7120462","url":null,"abstract":"Each city exhibits recurring patterns consisting of similar building types, vegetation structures, and open spaces, enabling environmental and socio-economic investigations of the urban fabric. In this study, urban structure types (UST) of the city of Berlin are mapped on the basis of a prior land cover classification utilizing a synergistic approach of knowledge based classification and Random Forests. The results are then compared to the outcomes of a previous analysis regarding a subarea of the utilized high spatial resolution airborne data. Results show that UST classification based on a combination of prototype objects and Random Forests is suitable to generate accurate UST maps for these areas with only minor adaptations. Future analyses will focus on transferring the processes to different German cities and data of several sensors.","PeriodicalId":207233,"journal":{"name":"2015 Joint Urban Remote Sensing Event (JURSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130668959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Assessing validation methods for building identification and extraction","authors":"E. Wentz, Qunshan Zhao","doi":"10.1109/JURSE.2015.7120453","DOIUrl":"https://doi.org/10.1109/JURSE.2015.7120453","url":null,"abstract":"As data sources and algorithm choices become increasingly more available for automatically extracting and reconstructing 3D buildings, methods are needed to assess the accuracy of the classification process. Our research goal is to compare validation data sources and methods to assess objectivity, sensitivity, and reliability of current validation approaches. Our results show that when relying on object-oriented methods for building classification and extraction, methods other than pixel assessment are needed.","PeriodicalId":207233,"journal":{"name":"2015 Joint Urban Remote Sensing Event (JURSE)","volume":"375 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131934738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Three dimensional modelization of aerial elements of the Geneva public transport network using mobile scanning systems","authors":"Florian Gandor","doi":"10.1109/JURSE.2015.7120533","DOIUrl":"https://doi.org/10.1109/JURSE.2015.7120533","url":null,"abstract":"This paper presents an application of mobile scanning systems in the urban area of Geneva done by HKD Géomatique SA. In order to facilitate the maintenance of the public transport network, a full three dimensional modelization of the aerial objects was created with a precision of 20 centimetres. Numerous quality controls were performed to assess the required precision. In the end, 38 517 point objects and 428.2 kilometres of linear entities with attributes were digitized and stored in a geodatabase1. Moreover, this model is accessible for everyone conforming to the openData approach through the web mapping service of Geneva2. This access will thus enable further development and applications.","PeriodicalId":207233,"journal":{"name":"2015 Joint Urban Remote Sensing Event (JURSE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121055765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}