The Experts below are selected from a list of 66 Experts worldwide ranked by ideXlab platform
David Haynes - One of the best experts on this subject based on the ideXlab platform.
-
large scale analytics of Vector Raster big spatial data
Advances in Geographic Information Systems, 2017Co-Authors: Ahmed Eldawy, Lyuye Niu, David HaynesAbstract:Significant increases in the volume of big spatial data have driven researchers and practitioners to build specialized systems to process and analyze this data. Existing efforts focus on either big Raster data, e.g., remote sensing data or medical images, or big Vector data, e.g., geotagged tweets or trajectories. However, when Raster and Vector data mix, one dataset must be converted to the other representation requiring Vector-to-Raster or Raster-to-Vector transformation before processing, which is extremely inefficient for large datasets. In this paper, we advocate a third approach that mixes the raw representations of both Vector and Raster data in the query processor. As a case study, we apply this to the zonal statistics problem, which computes the statistics over a Raster layer for each polygon in a Vector layer. We propose a novel method, called Scanline method, which does not require a conversion between Raster and Vector. Experimental evaluation on real datasets as large as 840 billion pixels shows up to three orders of magnitude speedup over the baseline methods.
-
SIGSPATIAL/GIS - Large Scale Analytics of Vector+Raster Big Spatial Data
Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2017Co-Authors: Ahmed Eldawy, Lyuye Niu, David HaynesAbstract:Significant increases in the volume of big spatial data have driven researchers and practitioners to build specialized systems to process and analyze this data. Existing efforts focus on either big Raster data, e.g., remote sensing data or medical images, or big Vector data, e.g., geotagged tweets or trajectories. However, when Raster and Vector data mix, one dataset must be converted to the other representation requiring Vector-to-Raster or Raster-to-Vector transformation before processing, which is extremely inefficient for large datasets. In this paper, we advocate a third approach that mixes the raw representations of both Vector and Raster data in the query processor. As a case study, we apply this to the zonal statistics problem, which computes the statistics over a Raster layer for each polygon in a Vector layer. We propose a novel method, called Scanline method, which does not require a conversion between Raster and Vector. Experimental evaluation on real datasets as large as 840 billion pixels shows up to three orders of magnitude speedup over the baseline methods.
Ahmed Eldawy - One of the best experts on this subject based on the ideXlab platform.
-
large scale analytics of Vector Raster big spatial data
Advances in Geographic Information Systems, 2017Co-Authors: Ahmed Eldawy, Lyuye Niu, David HaynesAbstract:Significant increases in the volume of big spatial data have driven researchers and practitioners to build specialized systems to process and analyze this data. Existing efforts focus on either big Raster data, e.g., remote sensing data or medical images, or big Vector data, e.g., geotagged tweets or trajectories. However, when Raster and Vector data mix, one dataset must be converted to the other representation requiring Vector-to-Raster or Raster-to-Vector transformation before processing, which is extremely inefficient for large datasets. In this paper, we advocate a third approach that mixes the raw representations of both Vector and Raster data in the query processor. As a case study, we apply this to the zonal statistics problem, which computes the statistics over a Raster layer for each polygon in a Vector layer. We propose a novel method, called Scanline method, which does not require a conversion between Raster and Vector. Experimental evaluation on real datasets as large as 840 billion pixels shows up to three orders of magnitude speedup over the baseline methods.
-
SIGSPATIAL/GIS - Large Scale Analytics of Vector+Raster Big Spatial Data
Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2017Co-Authors: Ahmed Eldawy, Lyuye Niu, David HaynesAbstract:Significant increases in the volume of big spatial data have driven researchers and practitioners to build specialized systems to process and analyze this data. Existing efforts focus on either big Raster data, e.g., remote sensing data or medical images, or big Vector data, e.g., geotagged tweets or trajectories. However, when Raster and Vector data mix, one dataset must be converted to the other representation requiring Vector-to-Raster or Raster-to-Vector transformation before processing, which is extremely inefficient for large datasets. In this paper, we advocate a third approach that mixes the raw representations of both Vector and Raster data in the query processor. As a case study, we apply this to the zonal statistics problem, which computes the statistics over a Raster layer for each polygon in a Vector layer. We propose a novel method, called Scanline method, which does not require a conversion between Raster and Vector. Experimental evaluation on real datasets as large as 840 billion pixels shows up to three orders of magnitude speedup over the baseline methods.
Brian Graff - One of the best experts on this subject based on the ideXlab platform.
-
MapSnap system to perform Vector-to-Raster fusion
Geospatial InfoFusion Systems and Solutions for Defense and Security Applications, 2011Co-Authors: Boris Kovalerchuk, Peter Doucette, Gamal H. Seedahmed, Jerry D. Tagestad, Sergei Kovalerchuk, Brian GraffAbstract:As the availability of geospatial data increases, there is a growing need to match these datasets together. However, since these datasets often vary in their origins and spatial accuracy, they frequently do not correspond well to each other, which create multiple problems. to accurately align with imagery, analysts currently either: 1) manually move the Vectors, 2) perform a labor-intensive spatial registration of Vectors to imagery, 3) move imagery to Vectors, or 4) redigitize the Vectors from scratch and transfer the attributes. All of these are time consuming and labor-intensive operations. Automated matching and fusing Vector datasets has been a subject of research for years, and strides are being made. However, much less has been done with matching or fusing Vector and Raster data. While there are initial forays into this research area, the approaches are not robust. The objective of this work is to design and build robust software called MapSnap to conflate Vector and image data in an automated/semi-automated manner. This paper reports the status of the MapSnap project that includes: (i) the overall algorithmic approach and system architecture, (ii) a tiling approach to deal with large datasets to tune MapSnap parameters, (iii) time comparison of MapSnap with re-digitizing the Vectors from scratch and transfer the attributes, and (iv) accuracy comparison of MapSnap with manual adjustment of Vectors. The paper concludes with the discussion of future work including addressing the general problem of continuous and rapid updating Vector data, and fusing Vector data with other data.
-
Automated Vector-to-Raster Image Registration
Algorithms and Technologies for Multispectral Hyperspectral and Ultraspectral Imagery XIV, 2008Co-Authors: Boris Kovalerchuk, Peter Doucette, Gamal H. Seedahmed, Robert T. Brigantic, Michael Kovalerchuk, Brian GraffAbstract:The variability of panchromatic and multispectral images, Vector data (maps) and DEM models is growing. Accordingly, the requests and challenges are growing to correlate, match, co-register, and fuse them. Data to be integrated may have inaccurate and contradictory geo-references or not have them at all. Alignment of Vector (feature) and Raster (image) geospatial data is a difficult and time-consuming process when transformational relationships between the two are nonlinear. The robust solutions and commercial software products that address current challenges do not yet exist. In the proposed approach for Vector-to-Raster Registration (VRR) the candidate features are auto-extracted from imagery, Vectorized, and compared against existing Vector layer(s) to be registered. Given that available automated feature extraction (AFE) methods quite often produce false features and miss some features, we use additional information to improve AFE. This information is the existing Vector data, but the Vector data are not perfect as well. to deal with this problem the VRR process uses an algebraic structural algorithm (ASA), similarity transformation of local features algorithm (STLF), and a multi-loop process that repeats (AFE-VRR) process several times. The experiments show that it was successful in registering road Vectors to commercial panchromatic and multi-spectral imagery.
-
AIPR - A Methodology for Automated Vector-to-Image Registration
36th Applied Imagery Pattern Recognition Workshop (aipr 2007), 2007Co-Authors: Peter Doucette, Boris Kovalerchuk, Gamal H. Seedahmed, Robert T. Brigantic, Brian GraffAbstract:Registration and alignment of feature (e.g., Vector) and Raster geospatial data is a difficult and time-consuming process when performed manually. This paper presents an approach for Vector-to-Raster registration. Candidate features are auto-extracted and Vectorized from imagery, which are the basis to compare against existing Vector layer(s) to be registered. Given that automated feature extraction (AFE) methods are imperfect, the objective is to determine and gather a sufficient signal-to-noise ratio from AFE upon which to base a registration process between Vector data sets. Two Vector registration methods were investigated. The first is based on an algebraic structural algorithm (ASA) in which structural components (e.g., angles, lengths and areas) are used as similarity metrics. The second is based on a similarity transformation of local features (STLF) in which a 4-parameter transformation is used to align features on a local basis. Experiments were performed to register road Vector data to commercial panchromatic and multispectral QuickBird imagery.
Maria J. Vale - One of the best experts on this subject based on the ideXlab platform.
-
The Effects of Land Use and Land Cover Geoinformation Raster Generalization in the Analysis of LUCC in Portugal
ISPRS International Journal of Geo-Information, 2018Co-Authors: Bruno M. Meneses, Eusébio Reis, Rui Reis, Maria J. ValeAbstract:Multiple land use and land cover (LUC) datasets are available for the analysis of LUC changes (LUCC) in distinct territories. Sometimes, different LUCC results are produced to characterize these changes for the same territory and the same period. These differences reflect: (1) The different properties of LUC geoinformation (GI) used in the LUCC assessment, and (2) different criteria used for Vector-to-Raster conversion, namely, those deriving from outputs with different spatial resolutions. In this research, we analyze LUCC in mainland Portugal using two LUC datasets with different properties: Corine Land Cover (CLC 2006 and 2012) and LUC official maps of Portugal (Carta de Ocupacao do Solo, COS 2007 and 2010) provided by the European Environment Agency (EEA) and the General Directorate for Territorial Development (DGT). Each LUC dataset has undergone Vector-to-Raster conversion, with different resolutions (10, 25, 50, 100, and 200 m). LUCC were analyzed based on the Vector GI of each LUC dataset, and with LUC Raster outputs using different resolutions. Initially, it was observed that the areas with different LUC types in two LUC datasets in Vector format were not similar—a fact explained by the different properties of this type of GI. When using Raster GI to perform the analysis of LUCC, it was observed that at high resolutions, the results are identical to the results obtained when using Vector GI, but this ratio decreases with increased cell size. In the analysis of LUCC results obtained with Raster LUC GI, the outputs with pixel size greater than 100 m do not follow the same trend of LUCC obtained with high Raster resolutions or using LUCC obtained with Vector GI. These results point out the importance of the factor form and the area of the polygons, and different effects of amalgamation and dilation in the Vector-to-Raster conversion process, more evident at low resolutions. These findings are important for future evaluations of LUCC that integrate Raster GI and Vector/Raster conversions, because the different LUC GI resolution in line with accuracy can explain the different results obtained in the evaluation of LUCC. The present work demonstrates this fact, i.e., the effects of Vector-to-Raster conversions using various resolutions culminated in different results of LUCC.
Lyuye Niu - One of the best experts on this subject based on the ideXlab platform.
-
large scale analytics of Vector Raster big spatial data
Advances in Geographic Information Systems, 2017Co-Authors: Ahmed Eldawy, Lyuye Niu, David HaynesAbstract:Significant increases in the volume of big spatial data have driven researchers and practitioners to build specialized systems to process and analyze this data. Existing efforts focus on either big Raster data, e.g., remote sensing data or medical images, or big Vector data, e.g., geotagged tweets or trajectories. However, when Raster and Vector data mix, one dataset must be converted to the other representation requiring Vector-to-Raster or Raster-to-Vector transformation before processing, which is extremely inefficient for large datasets. In this paper, we advocate a third approach that mixes the raw representations of both Vector and Raster data in the query processor. As a case study, we apply this to the zonal statistics problem, which computes the statistics over a Raster layer for each polygon in a Vector layer. We propose a novel method, called Scanline method, which does not require a conversion between Raster and Vector. Experimental evaluation on real datasets as large as 840 billion pixels shows up to three orders of magnitude speedup over the baseline methods.
-
SIGSPATIAL/GIS - Large Scale Analytics of Vector+Raster Big Spatial Data
Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2017Co-Authors: Ahmed Eldawy, Lyuye Niu, David HaynesAbstract:Significant increases in the volume of big spatial data have driven researchers and practitioners to build specialized systems to process and analyze this data. Existing efforts focus on either big Raster data, e.g., remote sensing data or medical images, or big Vector data, e.g., geotagged tweets or trajectories. However, when Raster and Vector data mix, one dataset must be converted to the other representation requiring Vector-to-Raster or Raster-to-Vector transformation before processing, which is extremely inefficient for large datasets. In this paper, we advocate a third approach that mixes the raw representations of both Vector and Raster data in the query processor. As a case study, we apply this to the zonal statistics problem, which computes the statistics over a Raster layer for each polygon in a Vector layer. We propose a novel method, called Scanline method, which does not require a conversion between Raster and Vector. Experimental evaluation on real datasets as large as 840 billion pixels shows up to three orders of magnitude speedup over the baseline methods.
