fabrizio de fausti, stefano mugnoli, diego zardetto ......supervisors: monica scannapieco –sandro...
TRANSCRIPT
Fabrizio De Fausti, Stefano Mugnoli, Diego Zardetto,
Francesco Pugliese
Supervisors: Monica Scannapieco – Sandro Cruciani
Italian National Institute of Statistics
Email : [email protected], [email protected], [email protected], [email protected]
• Eurostat has been carrying out the LUCAS survey every 3 years since 2006 (last round in 2018) in order to estimate the Land Cover (LC) and Land Use (LU) within the European Union:
A 2-phase area sample survey of the whole EU territory
1st phase: Master Sample of ~1.1 million points in a square grid of (2 km x 2 km) cells
2nd phase: ~330,000 random points from the Master Sample
Direct data collection, mainly on the ground (~70% of 2nd phase points), the rest by clerical
photo-interpretation
Provides LC estimates for all Member States up to NUTS-2 territorial level
• Computer Vision methods (e.g. Deep Learning) + Satellite Imagery data (e.g. Sentinel-2) can be used for LC estimation:
Classify-and-Count approach
Train an image classification algorithm to predict the LC class of a satellite image tile
Divide the satellite images covering a target area into tiles and use the trained algorithm to predict LC classes
Obtain LC statistics for the target area by simply computing the relative frequencies of predicted LC classes
• Pros: (i) dramatically reduction of data collection costs/burden, (ii) providing more timely statistics, (iii) supplying LC statistics beyond the NUTS-2 level, (iv) producing moderate resolution maps of the whole territory
• Challenge: Can a fully automated approach provide LC estimates of satisfactory accuracy? 2
MOTIVATION OF THE WORK
3
From enumeration areas
Census 2011
To microzones
Continuous Census
The images have a size of 64x64 pixels.
Each class contains 2,000 to 3,000 images.
The dataset contains 27,000 images
INDUSTRIAL RESIDENTIAL ANNUALCROP PERMANENT CROP RIVER
SEA LAKE HERBACEOUS VEGETATION HIGHWAY PASTURE FOREST
4
EUROSAT DATASET [1]
[1] Elber P.., Bischke, B., Dengel A.& Borth D. (2017). EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and
Land Cover Classification. arXiv preprint arXiv:1709.00029v2, 2019.
5
SATELLITE-NET MODEL
10% 10%80%
TRAINING
TEST
VALIDATION
DATA AUGMENTATION
Case study n.1
‘S2A_MSIL1C_20160629T094032_N0204_R036_T34TBK_20160629T094727’
- Large area of Apulia
- Well defined area (i.e. towns is still visible with welldefined edges);
- Large amount of reference data (i.e. Technicalcartography produced by National and local Institute;
- No cloud cover
7
PERFORMANCE
Save best model
CLASSLAND
COVER
… …
Residential 14.8 %
… …
Satellite image
(Puglia crop)
TRAINED DEEP CNN
RESIDENTIAL
8
MAPPING LAND COVER CLASSIFICATIONS
LUCAS EUROSAT
ARTIFICIAL LAND Industrial
ARTIFICIAL LAND Residential
ARTIFICIAL LAND Highway
BARE LAND & LICHENS/MOSS Non contemplata
WETLANDS Non contemplata
CROPLAND Annual crop
CROPLAND Pasture
CROPLAND Permanent crop
WOODLAND Forest
SHRUBLAND Non contemplata
GRASSLAND Herbaceous vegetation
WATER AREAS River
WATER AREAS Lake
LUCAS CORINE
ARTIFICIAL LAND Continuous urban fabric
ARTIFICIAL LAND Discontinuous urban fabric
ARTIFICIAL LAND Industrial or commercial units
ARTIFICIAL LAND Road and rail networks and associated land
ARTIFICIAL LAND Port areas
ARTIFICIAL LAND Airports
ARTIFICIAL LAND Dump sites
ARTIFICIAL LAND Construction sites
ARTIFICIAL LAND Sport and leisure facilities
CROPLAND Non-irrigated arable land
CROPLAND Permanently irrigated land
CROPLAND Rice fields
CROPLAND Vineyards
CROPLAND Fruit trees and berry plantations
CROPLAND Olive groves
CROPLAND Pastures
CROPLAND Annual crops associated with permanent crops
CROPLAND Complex cultivation patterns
CROPLAND Land principally occupied by agriculture, with
significant areas of natural vegetation
CROPLAND Agro-forestry areas
LUCAS CORINE
WOODLAND Broad-leaved
forest
WOODLAND Coniferous forest
WOODLAND Mixed forest
SHRUBLAND Sclerophyllous
vegetation
SHRUBLAND Transitional
woodland-shrub
GRASSLAND Natural grassland
GRASSLAND Moors and
heathland
BARE LAND & LICHENS/MOSS Mineral extraction
sites
BARE LAND & LICHENS/MOSS Beaches, dunes,
sands
BARE LAND & LICHENS/MOSS Bare rocks
BARE LAND & LICHENS/MOSS Sparsely vegetated
areas
BARE LAND & LICHENS/MOSS Burnt areas
WATER AREAS Glaciers and
perpetual snow
WATER AREAS Water courses
WATER AREAS Water bodies
WATER AREAS Coastal lagoons
WATER AREAS Estuaries
WATER AREAS Sea and ocean
WETLANDS Inland marshes
WETLANDS Peat bogs
WETLANDS Salt marshes
WETLANDS Salines
WETLANDS Intertidal flats
? Green urban areas
10
EXAMPLE: Puglia Crop of size (25 Km x 30 Km)
LUCAS (from Corine) Land Cover (%)
CROPLAND 87,05
ARTIFICIAL LAND 12,17
BARE LAND & LICHENS/MOSS 0,53
WOODLAND 0,20
SHRUBLAND 0,03
WETLANDS 0,02
Corine layerSentinel2
LC Benchmark
11
12
13
LAND COVER ESTIMATES: Puglia Crop of size (25 Km x 30 Km)
LUCAS Land Cover
BENCHMARK
from Corine(%)
Land Cover
ESTIMATES
(%)
CROPLAND 87.05 24.58
GRASSLAND 0.00 57.31
ARTIFICIAL LAND 12.17 18.01
BARE LAND &
LICHENS/MOSS
0.53 0.00
WOODLAND 0.20 0.004
SHRUBLAND 0.03 0.00
WETLANDS 0.02 0.00
Land Cover Estimates (%):
Ann. Crop Forest Herb. Vegetation Highway Industrial Pasture Perm. Crop Residential River
0.19829202 0.00401040 57.31040276 2.30664858 0.79049446 0.43601963 23.94765982 14.91378753 0.09268481
14
15
• Our Satellite-Net CNN proved to perform very well in the Land Cover classification task when tested on the EUROSAT dataset
• However, its accuracy seems to decrease when tested on tiles taken from generic Sentinel-2 images. We are currently investigating this issue (JPG compression seems to play a role)
• Moreover, the EUROSAT dataset comes with LC classes that differ from (and cannot be unequivocally mapped to) LUCAS classes
• In addition, EUROSAT classes “Highway” and “River” do not fit well with our classify-and-count approach, leading to upward biased LC estimates
• Next Step: Build our own training dataset for Land Cover classification according to LUCAS
Get available in situ microdata of LUCAS 2018 for Italy
Use the geolocation of LUCAS microdata to build a Sentinel-2 tile centered around each LUCAS sample point
Annotate all the obtained Sentinel-2 tiles with the LUCAS class attached to the corresponding sample point
FUTURE WORK
16
STRIDE 64
17
STRIDE 32
18
STRIDE 16
19
STRIDE 08
20
STRIDE 04
21
STRIDE 02
22
STRIDE 01
23
Four bands ortophoto
(50*50 and 20*20 cm)
