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Assessment of Land Coverage maps
with GRASSMaria Antonia Brovelli, Monia Elisa Molinari
GEOlab
GEOlab, Politecnico di Milano – Como Campus, via Valleggio 11, 22100, Como, Italy
4th High Level Forum on Global Geospatial Information Management (UN-GGIM)Side Event: Globeland30 18-19 April 2016Caucus 11, UNECA Conference Center Addis Ababa, Ethiopia
Index
Comparison methodology
Datasets
Data Processing
Case study: Lombardy Region (Italy)
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A B C D
A fAA fAB fAC fAD
B fBA fBB fBC fBD
C fCA fCB fCC fCD
D fDA fDB fDC fDD
CONFUSION MATRIX
CLA
SSIF
ICA
TIO
N
The confusion matrix (Congalton and Green, 1999) is derived through a pixel by pixel comparison between a classified map and a reference one.
GROUND TRUTH (REFERENCE)
Comparison methodology3
Comparison methodology
A B C D
A fAA fAB fAC fAD fA+
B fBA fBB fBC fBD fB+
C fCA fCB fCC fCD fC+
D fDA fDB fDC fDD fD+
f+A f+B f+C f+D n
CLA
SSIF
ICA
TIO
N
f+i = marginal sum of column i (i = A, B, C, D)
fi+ = marginal sum of row i (i = A, B, C, D)
n = total number of cells
CONFUSION MATRIX
GROUND TRUTH (REFERENCE)
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The confusion matrix (Congalton and Green, 1999) is derived through a pixel by pixel comparison between a classified map and a reference one.
Comparison methodology
A B C D
A fAA fAB fAC fAD fA+
B fBA fBB fBC fBD fB+
C fCA fCB fCC fCD fC+
D fDA fDB fDC fDD fD+
f+A f+B f+C f+D n
CLA
SSIF
ICA
TIO
N
CONFUSION MATRIX DERIVED STATISTICS
(i = A, B, C, D)
Overall accuracy: evaluates the percentage of correctly classified pixels
𝑶𝑨 = 𝑓𝑖𝑖𝑛
GROUND TRUTH (REFERENCE)
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The confusion matrix (Congalton and Green, 1999) is derived through a pixel by pixel comparison between a classified map and a reference one.
Comparison methodology
A B C D
A fAA fAB fAC fAD fA+
B fBA fBB fBC fBD fB+
C fCA fCB fCC fCD fC+
D fDA fDB fDC fDD fD+
f+A f+B f+C f+D n
GROUND TRUTH (REFERENCE)
CLA
SSIF
ICA
TIO
N
CONFUSION MATRIX DERIVED STATISTICS
(i = A, B, C, D)
Allocation disagreement: the amount of difference due to the less then optimal match in the spatial allocation of the categories (Pontius and Millones, 2011)
𝑨𝑫 = (2 ∗ 𝑚𝑖𝑛
𝑓+𝑖𝑛−𝑓𝑖𝑖𝑛,𝑓𝑖+𝑛−𝑓𝑖𝑖𝑛)
2
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The confusion matrix (Congalton and Green, 1999) is derived through a pixel by pixel comparison between a classified map and a reference one.
Comparison methodology
A B C D
A fAA fAB fAC fAD fA+
B fBA fBB fBC fBD fB+
C fCA fCB fCC fCD fC+
D fDA fDB fDC fDD fD+
f+A f+B f+C f+D n
GROUND TRUTH (REFERENCE)
CLA
SSIF
ICA
TIO
N
CONFUSION MATRIX DERIVED STATISTICS
Quantity disagreement: the amount of difference due to the less than perfect match in the proportion of categories (Pontius and Millones, 2011)
𝑸𝑫= 𝑓+𝑖𝑛−𝑓𝑖+𝑛
2
(i = A, B, C, D)
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The confusion matrix (Congalton and Green, 1999) is derived through a pixel by pixel comparison between a classified map and a reference one.
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Comparison methodology
A B C D
A fAA fAB fAC fAD fA+
B fBA fBB fBC fBD fB+
C fCA fCB fCC fCD fC+
D fDA fDB fDC fDD fD+
f+A f+B f+C f+D n
GROUND TRUTH (REFERENCE)
CLA
SSIF
ICA
TIO
N
CONFUSION MATRIX DERIVED STATISTICS
User’s accuracy: percentage of classified pixels which correctly match the ground truth
𝑼𝑨𝒊 =𝑓𝑖𝑖𝑓𝑖+
(i = A, B, C, D)
The confusion matrix (Congalton and Green, 1999) is derived through a pixel by pixel comparison between a classified map and a reference one.
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Comparison methodology
Producer’s accuracy: percentage of pixels of ground truth correctly detected in the classified map
𝑷𝑨𝒊 =𝑓𝑖𝑖𝑓+𝑖
A B C D
A fAA fAB fAC fAD fA+
B fBA fBB fBC fBD fB+
C fCA fCB fCC fCD fC+
D fDA fDB fDC fDD fD+
f+A f+B f+C f+D n
GROUND TRUTH (REFERENCE)
CLA
SSIF
ICA
TIO
N
CONFUSION MATRIX DERIVED STATISTICS
(i = A, B, C, D)
The confusion matrix (Congalton and Green, 1999) is derived through a pixel by pixel comparison between a classified map and a reference one.
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Datasets: GlobeLand30 (GL30)
UTM32 UTM33
• FORMAT: RASTER
• YEARS: 2000, 2010
• REFERENCE SYSTEM: WGS84/UTM32 - WGS84/UTM33
Chen et al., 2014
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Datasets: Italian Land Cover Data
Friuli Venezia GiuliaLand Cover 2000 - 1:25’000
VenetoLand Cover 2007/2009 - 1:10’000
Emilia RomagnaLand Cover 2003 - 1:10’000Land Cover 2008 - 1:10’000
SardegnaLand Cover 1997/2000 - 1:25’000Land Cover 2003/2006 - 1:25’000
LiguriaLand Cover 2000 - 1:10’000Land Cover 2012 - 1:10’000
LombardiaLand Cover 1999/2000 - 1:10’000Land Cover 2012 - 1:10’000
Autonomous Province of BolzanoLand Cover 2000 - 1:10’000
Autonomous Province of TrentoLand Cover 2000 - 1:10’000
AbruzzoLand Cover 1997- 1:25’000
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Data Processing
REGIONGL30GL30
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Data Processing
RE-PROJECTION RE-PROJECTION
REGIONGL30GL30
v.proj r.proj
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Data Processing
REGIONGL30GL30
RE-PROJECTION RE-PROJECTION
MERGING
v.proj r.proj
r.patch
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Data Processing
REGIONGL30GL30
RE-PROJECTION RE-PROJECTION
v.proj r.proj
MERGING
r.patch
RASTERIZATION
v.to.rast
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Data Processing
REGIONGL30GL30
RE-PROJECTION RE-PROJECTION
v.proj r.proj
MERGING
r.patch
RASTERIZATION
v.to.rast
RECLASSIFICATION RECLASSIFICATION
r.reclass r.reclass
CORINE LEGEND GLOBELAND30 LEGEND
Artificial surfaces Artificial cover
Agricultural areas Croplands
Forests and semi natural areasMixed forest, Broadleaf forest, Coniferous forest,
Grass, Shrub, Bare land, Permanent ice or snow
Wetlands Wetlands
Water bodies Water
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Data Processing
REGIONGL30GL30
RE-PROJECTION RE-PROJECTION
v.proj r.proj
MERGING
r.patch
RASTERIZATION
v.to.rast
RECLASSIFICATION RECLASSIFICATION
r.reclass r.reclass
COMPARISON 1
r.kappa
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Lombardy Region case study
The procedure has been applied taking into account the influence on the comparison results of:
• Different rasterization resolutions and methods
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Lombardy Region case study
The procedure has been applied taking into account the influence on the comparison results of:
• Different rasterization resolutions and methods
• Different classification schemes
I METHOD
II METHOD
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Lombardy Region case study
The procedure has been applied taking into account the influence on the comparison results of:
• Different rasterization resolutions and methods
• Different classification schemes
• The co-location tolerance (Gallego, 2001) of GL30, which is equal to 70 m
All cells belonging to a buffer of 70 m around GL30 classes border were eliminated and the confusion matrix and statistics were calculated on the other pixels.
(Brovelli et al., 2015)
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Lombardy Region case study
DUSAF 1.1 DUSAF 2.0 DUSAF 2.1 DUSAF 3.0 DUSAF 4.0
YEAR 1999 - 2000 2005 - 2007 2007 2009 2012
SCALE 1:10’000 1:10’000 1:10’000 1:10’000 1:10’000
REF SYS WGS84/UTM32N WGS84/UTM32N WGS84/UTM32N WGS84/UTM32N WGS84/UTM32N
LEGEND CORINE CORINE CORINE CORINE CORINE
SOURCE Aerial photosAerial photos and regional databases
Aerial photos and regional databases
Aerial photos and regional databases
Aerial photos and regional databases
EXTENT whole region whole region whole region BS, MI, MB, SO, CR whole region
DUSAF Land Cover Database
DUSAF (“Use Categories of Agricultural and Forest Soils”) isa land cover database created in 2000-2001 within aproject promoted and funded by Lombardy Region.
The database provides a polygonal layer representing theland use and cover. Currently five releases are available.
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GL30 2000 – DUSAF1.1
OVERALL STATISTICS I METHOD
RESOLUTION OA [%] AD [%] QD [%]
NO BUFFER
30 m 86.39 11.61 2.00
30 m (prevalence) 86.50 11.52 1.98
5 m 86.34 11.66 2.00
BUFFER
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GL30 2000 – DUSAF1.1
OVERALL STATISTICS I METHOD
RESOLUTION OA [%] AD [%] QD [%]
NO BUFFER
30 m 86.39 11.61 2.00
30 m (prevalence) 86.50 11.52 1.98
5 m 86.34 11.66 2.00
BUFFER
30 m 90.53 7.40 2.07
30 m (prevalence) 90.64 7.33 2.03
5 m 90.83 7.10 2.07
• No significant changes in the results with different input dataset resolutions• The removal of the cells influenced by the co-location tolerance (buffer case)
leads to an increase of the OA
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GL30 2000 – DUSAF1.1
OVERALL STATISTICS I METHOD
RESOLUTION OA [%] AD [%] QD [%]
NO BUFFER
30 m 86.39 11.61 2.00
30 m (prevalence) 86.50 11.52 1.98
5 m 86.34 11.66 2.00
BUFFER
30 m 90.53 7.40 2.07
30 m (prevalence) 90.64 7.33 2.03
5 m 90.83 7.10 2.07
C1: artificial surfaces C2: Agricultural areas C3: Forests and semi natural areas C4: wetlands C5: water bodies
PER-CLASS AGREEMENT MEASURES (30 m)
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GL30 2000 – DUSAF1.1
OVERALL STATISTICS II METHOD
RESOLUTION OA [%] AD [%] QD [%]
NO BUFFER
30 m 77.18 17.97 4.85
30 m (prevalence) 77.30 17.84 4.85
5 m 77.13 18.02 4.85
BUFFER
30 m 82.03 12.82 5.15
30 m (prevalence) 82.15 12.74 5.10
5 m 82.43 12.36 5.21
9.26.3 2.85
Differences with respect the I method
• The introduction of a greater level of classification detail entails an increase of both the allocation and quantity disagreement values.
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GL30 2000 – DUSAF1.1
OVERALL STATISTICS II METHOD
RESOLUTION OA [%] AD [%] QD [%]
NO BUFFER
30 m 77.18 17.97 4.85
30 m (prevalence) 77.30 17.84 4.85
5 m 77.13 18.02 4.85
BUFFER
30 m 82.03 12.82 5.15
30 m (prevalence) 82.15 12.74 5.10
5 m 82.43 12.36 5.21
PER-CLASS AGREEMENT MEASURES (30 m)
9.26.3 2.85
Differences with respect the I method
C31: forests C32: scrub and herbaceous vegetation C330: open space C335: glaciers /permanent snow
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GL30 2010 – DUSAF4.0
OVERALL STATISTICS II METHOD
RESOLUTION OA [%] AD [%] QD [%]
NO BUFFER
30 m 79.88 16.12 4.00
30 m (prevalence) 80.01 16.09 3.89
5 m 79.83 16.17 4.00
BUFFER
30 m 85.06 10.74 4.19
30 m (prevalence) 85.20 10.68 4.20
5 m 85.57 10.19 4.24
PER-CLASS AGREEMENT MEASURES (30 m)
2.7
1.85 0.85Differences with respect the year 2000
2000 value
C31: forests C32: scrub and herbaceous vegetation C330: open space C335: glaciers /permanent snow
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GL30 - Italian regions
*OVERALL STATISTICS I METHOD - 2000
* No buffer 30 m resolution case
*OVERALL STATISTICS I METHOD - 2010
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GL30 - Italian regions
*OVERALL STATISTICS II METHOD - 2010
*OVERALL STATISTICS II METHOD - 2000
* No buffer 30 m resolution case
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Conclusions
• The thematic accuracy assessment performed between GlobeLand30 and Italian regional land cover maps shows overall accuracy varying between:
81% - 92% (I method)62% - 81% (II method)
• The disagreement can be due to the fact that the images were taken on different dates, to the different thematic classification and resolution.
• Removing the part of the disagreement due to the co-location tolerance the overall accuracy increases:
84% - 96 % (I method)65% - 86% (II method)
• In addition, in most of literature the reference data has been used as an accurate representation of the reality but they may contain errors.
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References
• Brovelli, M.A., Molinari, M.E., Hussein, E., Chen, J., Li, R. The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results. Remote Sens. 2015, 7(4), 4191-4212; doi:10.3390/rs70404191
• Chen, J.; Chen, J.; Liao, A.; Cao, X.; Chen, L.; Chen, X.; He, C.; Han, G.; Peng, S.; Lu, M.; et al. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogram. Remote Sens. 2014
• Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices; Lewis Publishers: Boca Raton, FL, USA, 1999; p. 137
• Gallego, J. Comparing CORINE Land Cover with a more Detailed Database in Arezzo (Italy). Towards Agri-Environmental Indicators; Topic report 6/2001 European Environment Agency 2001; European Environment Agency: Copenhagen, Danmark, 2001; pp. 118–125
• GRASS Development Team, 2015. Geographic Resources Analysis Support System (GRASS) Software, Version 6.4.5. Open Source Geospatial Foundation. http://grass.osgeo.org
• Pontius, R.G.; Millones, M. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 2011, 32, 4407–4429
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What is GRASS GIS
GEOlab, Politecnico di Milano – Como Campus
http://www.openstreetmap.org/
Geographic Resources Analysis Support System
• 1982: first developments carried out at USA/CERL (Construction Engineering Research Laboratory)
• 1985: version 1.0 was released
• 1998: development transferred to an international GRASS Development Team led by Markus Neteler
• 1999: GRASS 5.0 release under General Public License (GPL)
• 2016: stable long term release GRASS 7.0.3
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Download GRASS GIS
GEOlab, Politecnico di Milano – Como Campus
http://www.openstreetmap.org/https://grass.osgeo.org/download/
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GEOlab, Politecnico di Milano – Como Campus
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Starting with GRASS GIS
1. GIS Database directory: directory containing all the GRASS GIS data
2. Location: it is a sort of data library for the region of interest. It contains datasets with the same coordinate system
3. Mapset: it is a location subdirectory where GIS maps are organized thematically or geographically
DATABASE
LOCATION 1 LOCATION 2 LOCATION N
PERMANENT MAPSET 1 MAPSET N
MULTI-USER
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GEOlab, Politecnico di Milano – Como Campus
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Location
To create a Location click on New button in the GRASS GIS startup window.
Then, in the GRASS GIS Location window define:
The path to the GIS database directory
The name of the location
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GEOlab, Politecnico di Milano – Como Campus
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Location
To create a Location click on New button in the GRASS GIS startup window.
Then, in the GRASS GIS Location window define:
The path to the GIS database directory
The name for the project location
The reference system
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GEOlab, Politecnico di Milano – Como Campus
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Location
To create a Location click on New button in the GRASS GIS startup window.
Then, in the GRASS GIS Location window define:
The path to the GIS database directory
The name for the project location
The reference system
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GEOlab, Politecnico di Milano – Como Campus
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Location
To create a Location click on New button in the GRASS GIS startup window.
Then, in the GRASS GIS Location window define:
The path to the GIS database directory
The name for the project location
The reference system
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GEOlab, Politecnico di Milano – Como Campus
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Mapset
Once the location is created, the system requires information about:
region extents
mapset name
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GEOlab, Politecnico di Milano – Como Campus
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GRASS GIS interface
Layer Manager Toolbar to manage displayed map
layers Menu bar with all GRASS GIS
functions Python shell
Map Display 2D and 3D view of the maps Tools for map navigation Tools for map analysis Map elements Tools for export and printing
Bash Shell It runs GRASS GIS modules without
GUI
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GEOlab, Politecnico di Milano – Como Campus
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http://www.openstreetmap.org/
GRASS GIS modules
PREFIX CLASS FUNCTION
d.* display Visualization
db.* database Database management
g.* general General file operations
i.* image Image processing
ps.* postscript Map creation in Postscript
r.* raster Raster analysis
r3.* voxel Voxel analysis
v.* vector Vector analysis
t.* timeseries Temporal data processing
m.* miscellaneous Miscellaneous functions
GRASS is a very powerful GIS suite with over than 400 standard modules in the core version.
https://grass.osgeo.org/grass70/manuals/index.html
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GEOlab, Politecnico di Milano – Como Campus
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http://www.openstreetmap.org/
Data import: v.in.ogr
v.in.ogr module converts OGR vector layers to GRASS vector map.
https://grass.osgeo.org/grass70/manuals/v.in.ogr
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GEOlab, Politecnico di Milano – Como Campus
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http://www.openstreetmap.org/
Data import: r.in.gdal
r.in.gdal module imports GDAL supported raster file into a binary raster map layer.
https://grass.osgeo.org/grass70/manuals/r.in.gdal
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GEOlab, Politecnico di Milano – Como Campus
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GRASS region
g.region module manages the boundary definitions for the geographic region.
The region is a geographic area with defined boundaries (N,W,S,E) and specific E-W and N-S resolutions of its smallest units (cells). Most of the raster and displays modules are affected by the current region settings.
https://grass.osgeo.org/grass70/manuals/g.region
Define the current region specifying coordinates and resolutions
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GEOlab, Politecnico di Milano – Como Campus
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GRASS region
g.region module manages the boundary definitions for the geographic region.
The region is a geographic area with defined boundaries (N,W,S,E) and specific E-W and N-S resolutions of its smallest units (cells). Most of the raster and displays modules are affected by the current region settings.
Define the current region settings to match the extension of a raster/vector map
https://grass.osgeo.org/grass70/manuals/g.region
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GEOlab, Politecnico di Milano – Como Campus
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Re-projection: v.proj / r.proj
v.proj / r.proj modules re-projects a vector / raster map from one location to the current location.
1. Create a location in the layer reference system
2. Import the layer into the location
3. Create the location in the reference system you want to re-project the layer into
4. Run v.proj (for vector map) or r.proj (for raster map) modules
https://grass.osgeo.org/grass70/manuals/v.proj.html
https://grass.osgeo.org/grass70/manuals/r.proj.html
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GEOlab, Politecnico di Milano – Como Campus
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Merging: r.patch
r.patch module creates a composite raster map layer by using known category values from one (or more) map layer(s) to fill in areas of "no data" in another map layer.
https://grass.osgeo.org/grass70/manuals/r.patch.html
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GEOlab, Politecnico di Milano – Como Campus
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Rasterization: v.to.rast
v.to.rast converts a vector map into a raster map. The resolution of the output raster map is the same of the current region.
https://grass.osgeo.org/grass70/manuals/v.to.rast.html
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GEOlab, Politecnico di Milano – Como Campus
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Reclassification: r.reclass
r.reclass reclassifies a raster map based on category values.
https://grass.osgeo.org/grass70/manuals/r.reclass.html
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GEOlab, Politecnico di Milano – Como Campus
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Matrix confusion calculation: r.kappa
r.kappa calculates error matrix for accuracy assessment of classification result.
https://grass.osgeo.org/grass70/manuals/r.kappa.html
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GEOlab, Politecnico di Milano – Como Campus
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Matrix confusion calculation: r.kappa
r.kappa calculates error matrix for accuracy assessment of classification result.
https://grass.osgeo.org/grass70/manuals/r.kappa.html
Stats.txt
Confusion matrix
Commission and omission errors
Overall accuracy
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