The document discusses the automated classification of land cover using Sentinel data for the Common Agricultural Policy (CAP), highlighting a proactive approach to support farmers through smart farming solutions and advisory services. It emphasizes the integration of various data sources, including satellite imagery, weather data, and soil sensors, to enhance decision-making and compliance monitoring. The initiative aims to improve crop management, reduce errors, and increase overall efficiency in agriculture through advanced data analytics and cloud-based technologies.

1. Automated classification of land cover for the
needs of CAP using Sentinel data.
A proactive approach.
PanagiotisILIAS(NP)
p_ilias@neuropublic.gr
DimitrisKAPNIAS(GAIA)
d_kapnias@c-gaia.gr
22nd CAP/IACS conference, 24-25 November 2016, Lisbon - Portugal

2. Farmers’
Organizations
70 Coops
150 K
Farmers
0.75 B €
Turnover
0.5 M ha
Area
Neuropublic
Gaia Cloud
Platform
AgriTech
Know-How
AgriTech
Solutions
Smart
Farming
Piraeus Bank
No 1 GR
Contract
Farming
AgriCard
Young
Farmer loans
The shareholders legacy to the coalition
Farmers
Tech
Bank

4. Society Management
Subsidy
(CAP)
Commerce
Smart
Farming
92 Farmer Service Points (FSP) certified as AID
Application assistants to the beneficiaries
GAIA’s involvement in the Aid Application Scheme
Nationwide
Network of
local service
advisors
(FSP)
GAIA is certified as the coordination &
support body (2014-2020)

5. SupportTraining
Advice
Facts
Data
Weather
Forecast
Remote
Sensing
Proximity
Sensing
Field
Sensors
Farm
Data
Data fusion
Data assimilation
Data interpolation
Farmer
Eligibility status? Parcel crop group?
Coupled payments & greening requirements?
Which choices to make?
Which financial instruments to use?
How can I support my business?
How much water and when?
When do I have to spray?
What is the risk level?
What is the precise type and the exact
amount of fertilizers needed?
Supporting Farmer using Data – Advisory Services

6. Atmospheric
Soil Data
GAIA Cloud
RPAS
IoT
GAIA Sense
Satellite Sensors Weather
Data Fusion
Decision Making
Data Analysis
API
GAIA Smart Farm & GAIA Subsidy (CAP)
Advisor
Farmer
Data Collection
Other sources
IoT and Cloud Infrastructure for Agricultural Monitoring
Other Platforms
Other Platforms
large scale agricultural areas
Sentinel-2
• Sort revisit
• HR
• MS
• Open

7. Subsidies control Smart Farming Services
The role of Sentinels
On-the-fly automated cross checks of parcels
declaration (proactive control):
• Reducing errors in the location of parcels
• Discouraging false claims
Full scale automatic compliance cross checks (post-
declaration control):
• crop-wise in terms of modelling and time
windows
• Allows better risk analysis, less RFV, more
effective controls
• Reducing overall error rates
Pest management/Early hazard warnings
Irrigation
Fertilisation
CAP eligibility - Compliance
Sentinel can been used for the
multitemporal extraction of
vegetation, soil and water indices and
the monitoring of the phenological
growth.
Parcel Level

8. Multi-Temporal Object-based Monitoring
Collect
Store
Define
Objects
Assign
Properties
Create
Models
Decision
Making
Take
Actions
Monitor
Actions
[ 0. 0.2 0.5 0.8 0.2 0.7 0.2 0.9 0.7 0. 0.74 0.97
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.39 1. 0.07 0.01 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.1 1. 0.26 0.06 0.04 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 1. 0.14 0.03 0. 0. 0. 0. ]
Time
Space
Vegetation – Soil – water Indices at parcel level,
for (single or multi) crop growing period
Crop Models
Parcel = Object
Cloud based services that integrate earth observation data with Image Processing, Machine Learning, Spatial Modeling

9. S2 Collection dates
Extracted Indices patterns agree with phenological growth for the specific
crop (beans)
Multitemporal Visualization of the Prespa area, for the growth period,
using indices values (R=NDVI, G=NDWI, B=SAVI)
Temperature data for area Leukonas - Prespes
Rainfall data for area Leukonas - Prespes
Indices in Agricultural Monitoring
Indices allows us too visualize the behavior of specific crops at parcel level through time.

10. Land Cover Classification using +Indices
b1
b2
b3
bn
Image (b1, b2, b3, … , bn) Object (reference parcel or holding LPIS) Land Cover Class System
Crop Code & Crop Description
1.1.1 xxxxxxxxxxxx1.1.1
Segment
(Polygon: x1y1, x2y2, x3y3, x4y4)
PHASE 2: Descriptor Extraction for each object per Image
Feature Extraction t22
All pixels
All Bands or/ and Indices
SIFT 
SURF 
ORB 
CHIST 
Method(s)
[ 0, 0, 0, 32, 0 , 0, …., 123, 0 ]
Descriptor (scaling)
PHASE 1: Indices (i) extraction using atmospherically corrected bands (b)
PHASE 3: Model Creation (data driven)
Train set
Input (time series data for polygons) Output( Labe)l
[ 0, 0, 0, 22, 0, 5, …., 100, 0, 12/04/2016] [1.1.1]
[ 0, 0, 0, 32, 0, 0, …., 123, 0, 07/05/2016] [1.1.1]
…….
[ 4, 1, 1, 22, 0, 0, …., 123, 0, 22/10/2016] [1.1.1]
Training
SVM 
RF 
NN 
Evaluation
PHASE 4: Crop estimation for other parcels using the data-driven input -output model(s)
Input (time series data for a polygon)
[ 0, 0, 0, 22, 0 , 5, …., 100, 0, 12/8/2016 ]
…….
[ 4, 1, 1, 22, 0 , 0, …., 123, 0, 12/8/2016 ]
Land Cover
Classification Model
Model
Multi-Dimensional
Dimension varies
i1
i2
i3
in
Image (i1, i2, i3, … , in)
b1
b2
b3
bn
Image (b1, b2, b3, … , bn) Object (reference parcel or holding LPIS) Land Cover Class System
Crop Code & Crop Description
1.1.1 xxxxxxxxxxxx1.1.1
Segment
(Polygon: x1y1, x2y2, x3y3, x4y4)
PHASE 2: Descriptor Extraction for each object per Image
Feature Extraction t22
All pixels
All Bands or/ and Indices
SIFT 
SURF 
ORB 
CHIST 
Method(s)
[ 0, 0, 0, 32, 0 , 0, …., 123, 0 ]
Descriptor (scaling)
PHASE 1: Indices (i) extraction using atmospherically corrected bands (b)
PHASE 3: Model Creation (data driven)
Train set
Input (time series data for polygons) Output( Labe)l
[ 0, 0, 0, 22, 0, 5, …., 100, 0, 12/04/2016] [1.1.1]
[ 0, 0, 0, 32, 0, 0, …., 123, 0, 07/05/2016] [1.1.1]
…….
[ 4, 1, 1, 22, 0, 0, …., 123, 0, 22/10/2016] [1.1.1]
Training
SVM 
RF 
NN 
Evaluation
PHASE 4: Crop estimation for other parcels using the data-driven input -output model(s)
Input (time series data for a polygon)
[ 0, 0, 0, 22, 0 , 5, …., 100, 0, 12/8/2016 ]
…….
[ 4, 1, 1, 22, 0 , 0, …., 123, 0, 12/8/2016 ]
Land Cover
Classification Model
Model
Multi-Dimensional
Dimension varies
i1
i2
i3
in
Image (i1, i2, i3, … , in)
b1
b2
b3
bn
Image (b1, b2, b3, … , bn) Object (reference parcel or holding LPIS) Land Cover Class System
Crop Code & Crop Description
1.1.1 xxxxxxxxxxxx1.1.1
Segment
(Polygon: x1y1, x2y2, x3y3, x4y4)
PHASE 2: Descriptor Extraction for each object per Image
Feature Extraction t22
All pixels
All Bands or/ and Indices
SIFT 
SURF 
ORB 
CHIST 
Method(s)
[ 0, 0, 0, 32, 0 , 0, …., 123, 0 ]
Descriptor (scaling)
PHASE 1: Indices (i) extraction using atmospherically corrected bands (b)
PHASE 3: Model Creation (data driven)
Train set
Input (time series data for polygons) Output( Labe)l
[ 0, 0, 0, 22, 0, 5, …., 100, 0, 12/04/2016] [1.1.1]
[ 0, 0, 0, 32, 0, 0, …., 123, 0, 07/05/2016] [1.1.1]
…….
[ 4, 1, 1, 22, 0, 0, …., 123, 0, 22/10/2016] [1.1.1]
Training
SVM 
RF 
NN 
Evaluation
PHASE 4: Crop estimation for other parcels using the data-driven input -output model(s)
Input (time series data for a polygon)
[ 0, 0, 0, 22, 0 , 5, …., 100, 0, 12/8/2016 ]
…….
[ 4, 1, 1, 22, 0 , 0, …., 123, 0, 12/8/2016 ]
Land Cover
Classification Model
Model
Multi-Dimensional
Dimension varies
i1
i2
i3
in
Image (i1, i2, i3, … , in)
b1
b2
b3
bn
Image (b1, b2, b3, … , bn) Object (reference parcel or holding LPIS) Land Cover Class System
Crop Code & Crop Description
1.1.1 xxxxxxxxxxxx1.1.1
Segment
(Polygon: x1y1, x2y2, x3y3, x4y4)
PHASE 2: Descriptor Extraction for each object per Image
Feature Extraction t22
All pixels
All Bands or/ and Indices
SIFT 
SURF 
ORB 
CHIST 
Method(s)
[ 0, 0, 0, 32, 0 , 0, …., 123, 0 ]
Descriptor (scaling)
PHASE 1: Indices (i) extraction using atmospherically corrected bands (b)
PHASE 3: Model Creation (data driven)
Train set
Input (time series data for polygons) Output( Labe)l
[ 0, 0, 0, 22, 0, 5, …., 100, 0, 12/04/2016] [1.1.1]
[ 0, 0, 0, 32, 0, 0, …., 123, 0, 07/05/2016] [1.1.1]
…….
[ 4, 1, 1, 22, 0, 0, …., 123, 0, 22/10/2016] [1.1.1]
Training
SVM 
RF 
NN 
Evaluation
PHASE 4: Crop estimation for other parcels using the data-driven input -output model(s)
Input (time series data for a polygon)
[ 0, 0, 0, 22, 0 , 5, …., 100, 0, 12/8/2016 ]
…….
[ 4, 1, 1, 22, 0 , 0, …., 123, 0, 12/8/2016 ]
Land Cover
Classification Model
Model
Multi-Dimensional
Dimension varies
i1
i2
i3
in
Image (i1, i2, i3, … , in)

11. TRAIN SET
Land Cover Classification using Indices
Crop Cover (ha) Parcels No.
Wheat 545 498
Stone fruits 367 364
Legumes 158 142
Maize 229 164
Trees 244 210
Fallow 354 358
Pasture 1,976 80
Total 3,873 1,816
Total Area (ha): 282,600
Agricultural Areas (ha): 53,580

12. TEST SET
Land Cover Classification using Indices
Crop Acc. (%)
Wheat 90%
Stone fruits 81%
Legumes 87%
Maize 57%
Forest Trees 61%
Fallow 65%
Pasture 78%
Total 74%
Crop Cover (ha) Parcels No.
Wheat 2,164 4611
Stone fruits 2,400 5045
Legumes 1,344 1588
Maize 2,917 3823
Forest Trees 549 894
Fallow 3,258 8744
Pasture 1,167 51
Total 13,799 25,264

13. Two back-end web services that rely on EO-S2 data:
• Polygon, time period -> Times series of indices, Estimated Crop Pattern
• Polygon, time period -> Crop classification
GAIA Smart Farm & GAIA Subsidy (CAP)
Advisor
Farmer
GAIA Cloud
API
That allows:
• On-the-fly automated cross checks of parcels declaration (proactive control)
• Full scale automatic compliance cross checks (post-declaration control - locate the
outliers)
• Crop monitoring – Smart farming (Advices based on Facts)
Plus Smart Farming data:
• Digital data (Valid Train set).
• Other type of data describing soil and atmospheric conditions (better mapping).

14. Next steps:
• Period 2017 – 2018: Subsidy and Smart Farming services for 16 large scale
areas (pilot sites) and for 15 significant crops.
• Collaboration for specific sites and crops with the partners of DataBio H2020
project (3 years from 2017 to 2020).
• Enrich the modelling process with more Descriptors (SL1 + L8).
• Deep Learning (for specific time windows using VHR data).
• Big Data Analytics (Outliers)
Thoughts - Discussions :
• IACS will take advantage of other Cloud Platforms (Food Security).

15. Thank you for your attention
Any questions?
15
PanagiotisILIAS(NP)
p_ilias@neuropublic.gr
DimitrisKAPNIAS(GAIA)
d_kapnias@c-gaia.gr
22nd CAP/IACS conference, 24-25 November 2016, Lisbon - Portugal