E Cognition User Summit2009 A Tewkesbury Infoterra Semi Automated Landscape Analysis
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1. The document discusses a semi-automated process for classifying high resolution land cover imagery into a standardized land cover scheme using Definiens software. 2. It describes challenges with processing a large volume of data from multiple sources and the need to change strategies to a more generic classification approach. 3. The process involves automated classification of image objects using rulesets, with manual editing to manage errors and achieve detailed attribution, allowing for thematic map creation and applications like habitat mapping and change detection.
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E Cognition User Summit2009 Pregesbauer Geo Info Li Dar Basic Landcover
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Sridhar S - Resume - Mar-2016
Sridhar S is seeking a position as a GIS/MIS professional with over 10 years of experience in projects involving utilities mapping. He has expertise in survey planning, data conversion, network mapping, and report generation. Notable projects include electrical asset mapping for TANGEDCO, telecom network data cleanup for AT&T, and cable TV network modeling for Comcast. Sridhar aims to effectively manage teams and deliver high-quality work on schedule.
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eCognition 8.9 features new capabilities for analyzing land mobile data from different perspectives, improved point cloud analysis using a temporary point cloud concept, and increased flexibility when developing rule sets. It provides multi-perspective feature extraction from land mobile data, enhanced point cloud handling through temporary vector layers, and allows rule sets to use different information types and machine learning algorithms.
Whats new in eCognition 8.8
Pioneered a decade ago for satellite remote sensing, eCognition enables geospatial data such as images, point clouds and Geographic Information System (GIS) vectors to be integrated and analyzed to quantify features or detect changes over time. eCognition 8.8 now extends these capabilities for mobile terrestrial data collection applications.
eCognition 8.64 Highlights
The document describes eCognition 8.64, a new version that provides native 64-bit support for large scale image analysis. Key advantages include being able to generate billions of image objects and segment very large image mosaics in high detail by fully leveraging state-of-the-art hardware. The update also offers better usability with no need for complex tiling/stitching and the ability to generate unlimited image objects. It further prepares users for large, high resolution datasets by allowing analysis of combinations of datasets. All users with a valid maintenance contract as of December 2010 are eligible for the free update, which is available for both 64-bit and 32-bit environments.
E Cognition User Summit2009 G Binnig Definiens
1. The document discusses principles of human cognition that can be utilized for automated image analysis, including context-driven and object-oriented thinking.
2. It describes a Cognitive Network Language (CNL) that allows programming through multiple choices, natural formulation, and directed local processing to evolve object hierarchies.
3. The document provides examples of context-driven image segmentation and analysis in various domains like cell biology, medical imaging, and earth observation.
E Cognition User Summit2009 M Wurm Dlr Structure Types C
This document summarizes a method for deriving urban structure types from remote sensing data. It involves segmenting and classifying very high resolution imagery and digital surface models to extract buildings and characterize urban landscapes. A multi-step workflow segments the images, optimizes the segments, classifies buildings and land cover, then aggregates the results into homogeneous urban structure types at the block level for applications like urban planning, risk assessment, and analyzing local heating potentials.
E Cognition User Summit2009 R Lucas University Wales National Vegetation Mapping
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E Cognition User Summit2009 Jo Neil Dunne Uvm Mapping Green Infrastructure
1) Jarlath O'Neil-Dunne from the University of Vermont Spatial Analysis Laboratory presented on using object-based image analysis (OBIA) to map green infrastructure.
2) Existing data and traditional pixel-based classification are insufficient to meet decision-maker needs, while OBIA can extract meaningful information by combining data with advanced analysis.
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eCognition 8 introduces several new features including Quickmap mode for simple tasks like land cover mapping, improved manual editing tools, multi-user workspace collaboration, native LiDAR support, object generalization tools, and improved performance for image segmentation and data loading. A trial version is available for download on the eCognition website.
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