Best student pitch:         Thomas Stark (TUM, DLR): Slum mapping in imbalanced remote sensing datasets using transfer learned deep features.

Best Poster:                     Nicolas Kraff (DLR): How dynamic are slums? EO-based assessment of Kibera’s morphologic transformation.

The JURSE 2017 best student papers awards were granted respectively to:

1st – Anil Armagan, “Semantic Segmentation for 3D localization in Urban Environments”.

2nd – Nicolas Audebert, “Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling”.

3rd – Marzena Wicht, “Urban ventilation corridors – performance evaluation using remotely sensed data”.

The JURSE 2015 best student papers awards were granted respectively to:

1st – Mitraka Zina, “Spectral Unmixing of urban landsat imagery by means of neural networks”

2nd – Voltersen Michael, “Expanding an urban structure type mapping approach from a sub area to the entire city of Berlin”

3rd – Krehbiel Cole, “Using Web-enabled Landsat data time series to analyze the impacts of urban areas on remotely sensed vegetation dynamics”

The JURSE 2013 best student papers awards were granted respectively to:

1st – Wen Liu,  “Building height detection from high-resolution TerraSAR-X imagery and GIS data”.

2nd – Simona Verde,  “Toward SAR coherence tomography for analysis of urban areas”.

3rd – Kanika Goel,  “Advanced stacking of TerraSAR-X and TanDEM-X data in complex urban areas”.

The JURSE 2011 best student papers awards were granted respectively to:

1st – T.R. Tooke,  “Classification of residential building architectural typologies using LiDAR”

2nd – D. Reale. “A scatterers detection scheme in SAR tomography for reconstruction and monitoring of individual buildings”

3rd – Y. Wang. “Advanced coherence stacking technique using high resolution TerraSAR-X spotlight data”

4rd – H. Thuni. “Deformation monitoring from high-resolution SAR images in Shanghai Pudong areag. Land use/ land cover classification for applied urban planning – the challenge of automation

5rd – C. Geiss. “Comparison of selected impervious surface products derived from remote sensing data – A case study for the city of Munich”

6rd – P. Iervolino.  “A new local approach for flooding level estimation in urban areas using single SAR images”

7rd – S. Uhlman. “Polarimetric SAR images classification using collective network of binary classifiers”

8rd – X. Niu. “RADARSAT-2 polarimetric SAR data for urban land cover mapping using spatial-temporal SEM algorithm and mixture models”

9rd – PTB Brett. “Bright line detection in COSMO-SkyMed SAR images of urban areas”

10rd – L. Wei. “Deformation monitoring from high-resolution SAR images in Shanghai Pudong area”

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• Xiaoxiang Zhu (German Aerospace Center (DLR))
• Karen Seto (Yale University)
  
• Florence Tupin (Université Paris-Saclay)
  

• Peter Wonka (King Abdullah University of Science and Technology (KAUST)), “Encoding prior knowledge for Urban reconstruction.”
• Qihao Weng (Indiana State University), ” Time series Image Analysis for Urbanization Studies”
• Noel Gorelic (Google), “The Google Earth Engine“ 

• Fabio Pacifici (DigitalGlobe Inc.), “An overview on the status of the geospatial industry and future perspectives”
  
• Florent Lafarge (INRIA), “Urban reconstruction from the airborne data: how to combine semantics and geometry?”
• Andrew J. Tatem (University of South Hampton), “Urban remote sensing in spatial epidemiology”
  

• Hannes Taubenböck (German Aerospace Centre – DLR, Germany), “Quo vadis Urban Remote Sensing? – A critical discussion on the status quo of our community”
• Surv. Eng. Peter Noble (Director of PSN Surveying, Sydney, Australia), “3D North Sydney Project – Urban mapping and modeling for local government administration”

• Massimo Rumor (University of Padova | UNIP) “Remote Sensing for urban applications”
• J. Benediktsson (University of Iceland), “Classification of urban remote sensing imagery based on mathematical morphology”

• C. Elvidge (NOAA), “Change detection in satellite observed nighttime lights: 1992-2003.”
• G. Hepner (ASPRS), “Trends in the U.S. Remote Sensing Industry Forecast – Implications for Urban Remote Sensing.”