A Fully Automated Approach to Classifying Urban Land Use and

Cover from LiDAR, Multi-spectral Imagery, and Ancillary Data

Jason ParentQian Lei

University of Connecticut

Land cover and land use

Land cover: the physical material on the earth’s surface (e.g. water, grass, asphalt, etc.)

Land use: the use of the land by humans (e.g. reservoir, agriculture, parking lot, etc.)

Fundamental to landscape analyses and urban planning.2

Opportunities and challenges for high resolution data

Increasing availability of airborne light detection and ranging (LiDAR) and aerial imagery offers opportunities to study landscapes in great detail.

Technically challenging to process… require lots of hard drive space. datasets must be divided into small subsets for

processing. conventional algorithms not well suited to

processing large numbers of subsets

Study objectives and justification Develop fully automated algorithm to

classify high resolution (1-meter) land cover / land use which is applicable over large areas. no previously presented algorithm has been

feasible to apply over large areas.

Specifically, we developed python scripts with ArcGIS to… classify 1-meter land cover from LiDAR and

multispectral data. infer land use from object geometry and

spatial context of land cover features.4

Study area

Located in eastern Connecticut in the northeastern U.S.

Semi-random stratified sample of 30 1x1 km tiles.

Stratified by % impervious cover (according to Connecticut’s Changing Landscape land cover data).

5

0 - 3333 - 6666 – 100

% impervious

4800 km2

Data

LiDAR 2010 leaf-off fall

acquisition Small footprint (44

cm) Near-infrared (1064

nm) > 1.5 pts/m2

6

Aerial orthophotos 2012 leaf-off spring

acquisition Blue, green, red,

and NIR 0.3 meter resolution

Land cover classification rules

7

Land cover Primary characteristicsBuilding Height > 2.5m; no ground

returns

Low impervious cover (low IC)

Low NDVI; no returns 2 to 4.5 meters above ground

Deciduous forest Height > 3m; high NDVI

Coniferous forest Height > 3m; very high NDVI

Medium vegetation Height 0.5 to 3m; high NDVI

Water No returns

Riparian wetlands Low reflectance in all bands; adjacent to water

Low vegetation High return intensity

Pixel- and object-based rules using

structural and spectral properties

Land cover classification example

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Land Cover

deciduous trees

evergreen trees

medium vegetation

grass / low vegetation

water

wetland

building

road / parking lot / barren

deciduousconiferousmed. veg.low veg.waterwetlandbuildinglow IC

Land cover class accuracies

Class User acc. (%)

Prod. acc. (%)

Water 96 85Building 99 97Low vegetation

91 94

Wetland 26 35Low impervious

93 91

Med. vegetation

61 60

Coniferous trees

90 76

Deciduous trees

95 96

93% overall

Kappa = 0.90

n = 3200

User accuracy: probability that a cell label is correct. Producer accuracy: probability that a cell is correctly labelled.

Land use classification rules

10

Building use Primary characteristic

Non-Residential Large parking area; flat roof; large building size

Multi-family residential

Large parking area; narrow building width; similar building shapes

Single family residential

Small parking area; peak roof; small building size Parcel cadastral information not used

because of limited availability.

Object- and parcel-based rules using object shape/size and parcel land

cover composition

Land use preliminary resultsLand Cover

deciduous trees

evergreen trees

medium vegetation

grass / low vegetation

water

wetland

building

road / parking lot / barren

deciduousconiferousmed. veg.low veg.waterwetlandbuildinglow IC

Land Use 18TBG5110_bldg

ParcelCls

Multi-Family Residential

Non-Residential

Single-Family Residential

multi-familynon-resid.single-family

Land use classification assessment

12

small commercial buildings misclassified as single family due to similar structural characteristics

problems caused by mismatch between land cover and parcel data

Qualitative assessment notes…

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Conclusions and future work Land cover classification:

Use of airborne LiDAR and multi-spectral data proved highly effective in classification of high resolution land cover.

Developed fully automated algorithm that performs well over large area.

13

Land use classification: Use of building shape and context is promising Future work will develop rules for classification

of… roads vs. parking lots urban vs. non-urban forest agriculture vs. turf

A Fully Automated Approach to Classifying Urban Land Use

and Cover from LiDAR, Multi-spectral Imagery, and Ancillary

DataJason Parent

Qian Lei

University of Connecticut

Questions?