Targeted LiDAR use in Support of In-Office Address Canvassing (IOAC) March 13, 2017 MAPPS, Silver Spring MD

Transcription

1 Targeted LiDAR use in Support of In-Office Address Canvassing (IOAC) March 13, 2017 MAPPS, Silver Spring MD

2 Imagery, LiDAR, and Blocks In 2011, the GEO commissioned independent subject matter experts (Jensen, Guptil, Cowen) to evaluate change detection technology resulting in the Change Detection Technology Evaluation, FY2012 Report The report identified using LiDAR and Imagery technologies to help detect change in the landscape particularly as it applies to new structures. When the report came out, the GEO did not have sufficient in house expertize to mount operations using remote sensing technologies. Five years later this has changed..

3 Change Detection Developments Analog Change Detection prove useful: e.g. isimple ( )and Interactive Review (IR) step of IOAC (2015->) In 2015 the Census Bureau submitted an RFI on Structure, Address and Street Centerline Change Detection Respondents provided a variety of approaches usually emphasizing their area of expertise.

4 isimple Project Overview What is isimple? Cell-based change detection (CD) analysis where Missing and Misaligned Features are quickly identified and coded. Rapid-Interactive CD Prioritize areas for updating Latest iteration occurred during benchmarking (June 2 July 2, 2014).

5 Missing and Misaligned Cell Values A user reviews for Missing and Misaligned features per cell using following score values: How Many Problems? # of Problem Features Score High 6 or more 2 Low - Medium None 0 0

6 isimple: Example of a Good Cell All the features of this cell are appropriate and spatially accurate, with no missing features. This cell would be coded 0 in all categories

7 isimple: Misaligned Features Nearly every feature in this cell is misaligned, easily more than 6 features. Score=2

8 What is Address Canvassing (AC)? AC is an operation that assures the completeness and accuracy of the Census Bureau s address inventory. In previous censuses, address canvassing occurred entirely in the field. Imagery combined with related address and geospatial data replicate AC in an office environment.

9 In-Office Address Canvassing (IOAC) In-Office Address Canvassing (IOAC) is a multi-step project that will assess the accuracy and completeness of address and related information in the Geography Division s (GEO) Master Address File (MAF); and is an integral part of the Census Bureau s reengineered Address Canvassing (AC) process for the 2020 Census.

10 IOC Components Interactive Review (IR) Compare Decennial Census baseline imagery to the latest available imagery by tabulation block. Active Block Review (ABR) Review blocks identified by IR as active, e.g. have HU under-coverage or over-coverage

11 Block Assessment Research and Classification Application (BARCA) An in-office block-based data review system using visual inspection of two high resolution imagery vintages to identify stability or change BARCA identifies roughly 80% of the blocks requiring updates by GEO Division staff Reduce in-field work, focus on on-going update work Interactive, requiring large number of analysts to review 11 million blocks Significant savings, however only identifies differences from baseline

12 Title13 BARCA Interface

13 Can IOAC/IR be automated? IR process is ideally suited for automation Replicate what the reviewer is observing Determine whether a block is active or passive Results consistent with or better than IR

14 Can LiDAR Help? Reconcile areas where imagery is not enough to determine change or lack of change. LiDAR coverage is increasing but not yet available everywhere. Validate the existence of a housing unit (structure). Enhance feature extraction & validation (structures; roads; etc.) Produce control points for road network & address location assessment (building footprints) Use to validate existence of feature/structure. Use in HU/Population estimation using structure volume (Dr. Qiu)

15 Preliminary thoughts Could LiDAR penetrate tree canopy to reveal potential housing units? Could it resolve street feature fidelity issues where tree canopy obscures visibility? As tree canopy maximum affects less than 3% of housing inventory, is it cost effective to use LiDAR? Focus on automating IR (Change Detection) instead.

16 NLCD

17 Land Cover by NLCD Zone 43 Mixed Forest 1% 31 Barren Land 2% 90 Woody Wetlands 4% 51 Dwarf Scrub 4% 21 Developed, Open 1% 12 Perennial Ice/Snow 1% 72 Sedge Herbaceous 1% 95 Emerging Herbaceous Wetlands 22 Developed, Low 1% 1% 23 Developed, Medium 0% 11 Open Water 0% 24 Developed, High 0% 81 Pasture/Hay 6% 52 Shrub/Scrub 25% 41 Deciduous Forest 12% 71 Grassland Herbaceous 12% 42 Evergreen Forest 15% 82 Cultivated Crops 16%

18 Distribution of Housing Units by 90 Woody Wetlands 2% 42 Evergreen Forest 3% 81 Pasture/Hay 4% 82 Cultivated Crops 4% 24 Developed, High 6% NLCD Zone 52 Shrub/Scrub 2% 71 Grassland Herbaceous 2% 99 Misc 1% 22 Developed, Low 29% 41 Deciduous Forest 9% 21 Developed, Open 14% 23 Developed, Medium 24%

19 Housing Unit Density by Zone HU DENSITY BY NLCD ZONE DEVELOPED, MEDIUM DEVELOPED, HIGH DEVELOPED, LOW DEVELOPED, OPEN MOSS DECIDUOUS FOREST PASTURE/HAY MIXED FOREST WOODY WETLANDS EMERGING HERBACEOUS WETLANDS CULTIVATED CROPS EVERGREEN FOREST GRASSLAND HERBACEOUS BARREN LAND SHRUB/SCRUB SEDGE HERBACEOUS DWARF SCRUB

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22 Project Objective Create a geoprocessing tool using a combination of aerial imagery, LiDAR, and Census data to identify Census blocks that experienced change. Prioritize the blocks by amount and type of change. Potentially reduce operational costs Potentially improve Census housing & population accuracy

23 Data Sources Must Be Available nationally. No to Low cost Vintages need to line up with Census objectives High resolution/block level

24 U.S Interagency Elevation Inventory

25 Study Area Central Kent County, Delaware Mixed development and vegetation types Large areas of agricultural land Contains the City of Dover Experiencing growth at its urban fringes as well as within the existing urban footprint

26 Data Sources Source Type Format U.S. Census Bureau Census Tabulation Block polygons Geodatabase Census road centerlines Census State/County Boundary polygons Housing unit location points (MSPs) Geodatabase Geodatabase Geodatabase ESRI/NAIP Aerial imagery (1 meter or better) Image Service NOAA COAST Data Access viewer/ USGS CMGP (Delaware) LiDAR point clouds ESRI LAS dataset

27 Python Processing Flow

28 Processing - NDVI Once extent is set for a connected groups of Census blocks, NDVI data is pulled from the image service.

29 Processing - NDVI An NDVI data threshold for Built/Not-Built cover is stored to a layer for each date. The resulting layers are combined into a single raster layer from which change can be calculated. Multi-date Built layer

30 Processing - LiDAR LiDAR point cloud queried for ground points Points converted to raster Expanded to fill small gaps LiDAR point cloud LiDAR Ground points Expanded Ground raster layer Ground points as raster layer

31 Processing LiDAR Buildings Expanded Ground raster used as mask to remove low areas of Built/Not Built layer for latest year (NDVI) Resultant raster layer represents primarily buildings Ground points as raster layer 2015 Built layer Buildings raster layer

32 Processing LiDAR Buildings Building raster converted to polygons Small polygons deleted, other noise filtered Buildings Polygons

33 Processing LiDAR Buildings Building polygons spatially joined to housing unit points to identify points that do not intersect building footprints Misaligned points written to output geodatabase Building footprints and approximate heights written to output geodatabase Title13 Buildings Polygons

34 Processing LiDAR Roads Building layer and nonground portion of LiDAR based ground raster are subtracted from Built/Not Built layer of latest year Buildings raster layer Resultant layer primarily represents roads, driveways, and parking lots Built/Not Built layer Paved area layer

35 Processing LiDAR Roads Contraction and dilation used to separate roads from parking lots Small road segments eliminated Paved area layer Resulting road layer intersected with Census road layer Census road polylines on roadbed Misaligned road segments written to output database Bad road polylines identified

36 Title13 Building Footprints

37 Parcel Centroids over building footprints Title13

38 Parcel Centroids not in building footprint Title13

39 Title13 New Construction

40 Title13 Suburban

41 Title13 Townhomes

42 IOAC/IR Block Product For each census (tabulation) block, calculate and report to geodatabase: the number of misaligned features; the total number of building features found by tool; tally MSPs from the MAF/TIGER database (buildings match MSP; MSPs notmatching building footprint, etc.) Total and relative amount of area change per census tabulation block is also reported to assist analyst with assignment prioritization.

43 Preliminary Results Building and road errors are identified at rates similar to human analyst More new buildings and roads are detected by the tool than human analyst. Most are not relevant to Census Locations of buildings and roads is more information than is currently collected by the BARCA analysts Vegetation change is over represented in agricultural regions Imagery and LiDAR date mismatch plus data vintage is a significant problem.

44 Additional Benefits from Automating IR Releases analysts to work on other operations like Active Block Review. Run as-needed or on-going basis, triggered by updated imagery or other operational needs. Use the building footprints/road extracts to assess and eventually improve the spatial quality of MAF Structure Points as well as road features.

45 Additional Work Analysis of nearby features Neighboring houses and addresses Streets Add parcel boundary to analysis where available Targeted processing based on lower resolution dataset triggers Image only analysis

46 Other Benefits In addition to identifying structure locations it can also provide attributes like area, height and volume Volume & height can be used to differentiate between single-family & multi-unit structures; as well as trailer homes/parks, high-rises, or row-houses. If necessary it can identify other physical features including roads, railroads, hydrography, etc.

47 Downstream Possibilities Develop new Quality Indicators Use the resulting building footprints to expand existing coverage (metro) to national coverage Evaluate different approaches to improve quality and process throughput. Research population estimation (Dr. Qiu)

48 Title

49 Title

50 Title Address points

51 Title13 Quality Indicators: Hierarchical Geocode Quality

52 Sample HGQ Analysis Block/Tract Level Census Tract Block A Block B Block C Block D Block E Block F Count Mean Distance (in meters) StDev Low (Shortest distance to structure centroid) High (Longest distance to structure centroid) Gold (in Structure) Silver (in parcel) Bronze (in block) Diff Parcel 27 1 No Match 4 No Structure 5

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