Automatische Generalisierung der neuen Schweizer Landeskarte mit ESRI-Tools

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Roxanne Holt
7 years ago
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1 Automatische Generalisierung der neuen Schweizer Landeskarte mit ESRI-Tools D. Käuferle

2 «System» GenDab for automatic generalization In production: DCM10/50/500 In development: DCM25/100 Planned:DCM200 Based on GP-Tools, ArcPy, ModelBuilder by Esri Building the system is done by plugging together existing components to a workflow Agile development done inhouse Not 100% automation Some remaining conflicts are accepted and worked over manually in a custom ArcGIS application Text placement is handled currently manual 2

3 How we do it armasuisse

4 GenDab Development Cycle 1. Geoprocessing-Modelling with ArcGIS ModelBuilder 2. Generation of Python-Scripts 3. Batch-Processing with Python-Scripts 4. Semi-Automatic Quality Assurance Ready for production

5 armasuisse Automatic Generalization Modeling

6 Geoprocessing / Model Builder

7 Why we use Model Builder It s visual and powerful Cartographers can model themselves Less programmers required than in conventional software development Short development cycles -> agility

8 Input to line Generalization 8

9 Results line generalization 9

10 Implementation line modification ESRI Geoprocessing-Tool Adjustments necessary to fulfill criteria 10

11 Results buildings generalization ungeneralized generalized 11

12 Implementation buildings generalization 12 12

13 Hydrology 13

14 Railways 14

15 Roundabouts 15

16 Single Trees 16

17 armasuisse Development workflow and QA

18 Generate Python Scripts from GPModel Modelling rules Flat model organisation Restartable models with local intermediate data Naming, parameters Funcionality Model2Python-Program Generates one python script per model Introduces logging and exception handling Supports conditional flows (exists, count) Creates a corresponding windows batch file Restrictions Not supported: Model iterators, model only tools No submodels, model parameters etc. Must be extended for new requirements

19 Generate Python Scripts from GPModel

20 Batch processing Batch processing with Python: Implements complete generalization process Processes a complete map sheet in background Ensures traceability through logging Executes stable also in error situations Enables systematic and quick error analysis Can restart an aborted process Selects 32Bit / 64Bit-Prozessing per script Creates a complete set of logs, intermediate results, configuration, scripts in one place

21 Batchprocessing

22 Quality Assurance Target: Automate QA as far as possible to minimize manual inspection effort. Important: Test prozesses fast minimize amount of data Test results are reliable Select representative test data to reflect all important cases Instant test result Automated validation based on statistics

23 Performance Analysis DCM50 map sheet 224 Minutes

24 Quality Assurance Automated statistical validation

25 Optimization potential / Findings Some GP Tools only support 32-Bit and cannot run in a server environment Isolate 32Bit-GP-Tools in separate models to maximize 64Bit run time. Improve I/O-Performance using In-Memory- Workspaces Only a few tools support please open more configuration option for the main carto tools to carto power users

26 more at 26

27 and map.geo.admin.ch

28 Questions? 28

ESRI Technical Certification Overview. Amy Daniels Instructor, Greenville Tech

ESRI Technical Certification Overview Amy Daniels Instructor, Greenville Tech Why have certification by ESRI? It communicates your proficiency in ESRI software Similar to Microsoft Certification, it can