Big Remotely Sensed Data: tools, applications and experiences

The increased availability of large remote sensing datasets is generating heightened interest within the geoscience community, and more generally within human society. Indeed, remote sensing datasets that have commonly been analyzed as single scenes, or neighboring scenes, or temporally sequential scenes can now be analyzed en masse. This is due to the accumulation of large data volumes through time by increasing numbers of satellites, data access efficiencies due to technical advances and policy changes, and advances in hardware and software processing capabilities.

[1]  D. Lobell,et al.  Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring , 2017 .

[2]  Emanuele Santi,et al.  Investigating spatiotemporal snow cover variability via cloud-free MODIS snow cover product in Central Alborz Region , 2017 .

[3]  F. Casu,et al.  Large areas surface deformation analysis through a cloud computing P-SBAS approach for massive processing of DInSAR time series , 2017 .

[4]  M. G. Ciminelli,et al.  Analysis of surface deformations over the whole Italian territory by interferometric processing of ERS, Envisat and COSMO-SkyMed radar data , 2017 .

[5]  Bo Huang,et al.  Using multi-source geospatial big data to identify the structure of polycentric cities , 2017 .

[6]  Achille Peternier,et al.  Near-real-time focusing of ENVISAT ASAR Stripmap and Sentinel-1 TOPS imagery exploiting OpenCL GPGPU technology , 2017 .

[7]  Zhong Lu,et al.  Consolidation settlement of Salt Lake County tailings impoundment revealed by time-series InSAR observations from multiple radar satellites , 2017 .

[8]  Andre Cahyadi Kalia,et al.  A Copernicus downstream-service for the nationwide monitoring of surface displacements in Germany , 2017 .

[9]  Gabriel B. Senay,et al.  Satellite-based water use dynamics using historical Landsat data (1984-2014) in the southwestern United States , 2017 .

[10]  Francesca Cigna,et al.  The relationship between intermittent coherence and precision of ISBAS InSAR ground motion velocities: ERS-1/2 case studies in the UK , 2017 .

[11]  A. Karpatne,et al.  An approach for global monitoring of surface water extent variations in reservoirs using MODIS data , 2017 .

[12]  Jessica L. McCarty,et al.  Extracting smallholder cropped area in Tigray, Ethiopia with wall-to-wall sub-meter WorldView and moderate resolution Landsat 8 imagery , 2017 .

[13]  Ben Evans,et al.  The Australian Geoscience Data Cube - foundations and lessons learned , 2017 .

[14]  Surya S. Durbha,et al.  High performance GPU computing based approaches for oil spill detection from multi-temporal remote sensing data , 2017 .

[15]  Denisa Rodila,et al.  Live Monitoring of Earth Surface (LiMES): A framework for monitoring environmental changes from Earth Observations , 2017 .

[16]  Yuqi Bai,et al.  Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine , 2017 .

[17]  Qi Liu,et al.  Unsupervised detection of contextual anomaly in remotely sensed data , 2017 .

[18]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[19]  D. Lobell,et al.  Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries , 2017 .

[20]  Alan C. Bovik,et al.  RivaMap: An automated river analysis and mapping engine , 2017 .