LiDAR Data Management Lessons for Geospatial Data Managers

Transcription

1 LiDAR Data Management Lessons for Geospatial Data Managers By: Morris County Department of Planning, Development & Technology

2 Background 2005 Morris County LiDAR Acquisition 5 year Orthophotography Plan Beaver Brook Watershed 2 2 Contours

3 LiDAR presents the same issues, now, that Orthophotograpy data raised 15 Years ago. Data storage issues Software rendering issues Plotting issues Extraction issues Data distribution issues Staff experience

4 LiDAR s Challenge Simple data complicated merely by its size

5 DTM - Filtered LiDAR with Breaklines

6 Guiness Record Cake Analagy Simple Cake Wedding Cake Guiness Record Cake

7 Draw from your Ortho Experience Many similarities exist between LiDAR and Orthophotography Large, continuous datasets Tile based storage requirements Feature extraction potential Historical, change detection potential Generally used by the same professionals and individuals.

8 Leverage LiDAR data beyond a single purpose to maximize your ROI The decision to obtain LiDAR often originates from a single need in a single agency. We felt a responsibility to look for data uses beyond the Beaver Brook Project, because LiDAR data is too valuable to shelve. shelve.

9 Managing LiDAR Data Elevation Dataset Choices Supported Applications Data Storage Decisions Data Users

10 Elevation Dataset Choices Data Storage Decisions Supported Applications Data Users Data Users Internal user needs will dictate which datasets you may want to store develop Telecommunications use LiDAR to examine vegetative growth beneath power lines Hydrologists interested in DEMs to model surface water flow characteristics Engineers use contours for site analysis Planners use TINS, DEMS and DSMs to provide clearer 3 dimensional views of places for public presentations

11 Elevation Dataset Choices Data Storage Decisions Supported Applications Data Users Data Users External user needs will influence your data storage decisions Typical town in Morris County requires tiles of data. Extremely inefficient to extract data manually especially when you re considering multiple datasets

12 Elevation Dataset Choices Data Storage Decisions Supported Applications Data Users Data Users Staff Constraints Availability Technical Skills Other Project Commitments Time Constraints

13 Elevation Dataset Choices Data Storage Decisions Supported Applications Elevation Dataset Choices Data Users You should always keep raw LiDAR data Addition Elevation Datasets Digital Elevation Models (Bare Earth) Surface Models (First return and/or last return LiDAR) TINs Contours

14 Elevation Dataset Choices Data Storage Decisions Supported Applications Supported Applications Data Users AutoCAD ESRI ArcGIS Desktop Spatial Analyst 3D Analyst Applied Imagery QT Modeler

15 Elevation Dataset Choices Data Storage Decisions Data Users Supported Applications Data Storage Decisions Data Types Data Manager time constraints Storage constraints

16 Elevation Dataset Choices Data Storage Decisions Supported Applications Data Storage Decisions Data Users Need to consider the software and workstation limitations when creating larger aggregated datasets.

17 Elevation Dataset Choices Data Storage Decisions Supported Applications Data Storage Decisions Data Users Tile Based Municipal based County based Watershed based

18 Elevation Dataset Choices Data Storage Decisions Data Users Supported Applications Data Storage Decisions Tiling LiDAR Should be consistent with your other spatial datasets Tiling size depends on density, point spacing, of your data. If your going to reproduce your data on CD s you need to make sure your tile size complements disk storage characteristics (650 MB CD 4.5 GB DVD)

19 Elevation Dataset Choices Data Storage Decisions Supported Applications Data Storage Decisions 5,000 Data Users 5,000 Morris County LiDAR Tiles

20 Indexing your LiDAR Data OBJECTID SHAPE * XMIN YMIN XMAX YMAX Image Label 1 Polygon 490, , , ,000 MC Polygon 480, , , ,000 MC Polygon 485, , , ,000 MC Polygon 490, , , ,000 MC Polygon 495, , , ,000 MC Polygon 500, , , ,000 MC Polygon 505, , , ,000 MC Polygon 510, , , ,000 MC Polygon 465, , , ,000 MC Polygon 470, , , ,000 MC Polygon 475, , , ,000 MC Polygon 480, , , ,000 MC Polygon 485, , , ,000 MC Polygon 490, , , ,000 MC Polygon 495, , , ,000 MC Polygon 500, , , ,000 MC500800

21 Data Extraction Typical town in Morris County requires tiles of data. Extremely inefficient to extract data manually especially when you re considering multiple datasets Need good file structure 1 need to develop scripts to perform basic file i/o

22 Programmatic Data Extraction

23 Folder Based Data Extraction

24 Delivery Media CDs DVDs Portable Hard Drives FTP Sites Direct Downloads

25 County of Morris??? Questions???

26

27 Provided LiDAR to FEMA for map modernization program Provided Elevation datasets to all municipalities in Morris County

28 ' Create the RasterSurfaceOp object Dim psurfaceop As esrigeoanalyst.isurfaceop Set psurfaceop = New RasterSurfaceOp ' Declare the input raster object Dim pinputdataset As IGeoDataset ' Calls function to open a raster dataset from disk Set pinputdataset = OpenRasterDataset("J:\Elevation Elevation\Morris4FtDEM", "r380715") ' Declare the output raster object Dim poutputraster As s IGeoDataset ' Calls the method Set poutputraster = psurfaceop.contour(pinputdataset, 4, 100)