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Global Earth Monitor project: continuous monitoring of large areas in a sustainable and cost-effective way

Authors:

Abstract

GEM project tries to: * Address the challenge of continuous monitoring of large areas in a sustainable and cost-effective way by establishing new and disruptive Earth Observation data-exploitation model of Copernicus data at a sustainable price. * Develop disruptive innovations with proprietary concept of Adjust- able Data Cubes and integrate them with ML framework eo-learn. * Combine technological and Methodological innovations into a unique continuous monitoring process based on seamless combination of multi-resolution data. * Validate the concept of continuous monitoring through the development of five specific use-cases and through their employment in a 6-month demonstration.
Main objectives
GEM platform
Use cases
Address the challenge of continuous monitoring of large areas in a
sustainable and cost-effective way by establishing new and disrup-
tive Earth Observation data-exploitation model of Copernicus data
at a sustainable price.
Develop disruptive innovations with proprietary concept of Adjust-
able Data Cubes and integrate them with ML framework eo-learn.
Combine technological and Methodological innovations into a
unique continuous monitoring process based on seamless combi-
nation of multi-resolution data.
Validate the concept of continuous monitoring through the develop-
ment of ve specic use-cases and through their employment in a
6-month demonstration.
eo-learn is a collection of open-source Python packages that have been developed to
seamlessly access and process spatio-temporal image sequences acquired by any sat-
ellite eet in a timely and automatic manner. eo-learn is easy to use, it’s design mod-
ular, and encourages collaboration -- sharing and reusing of specic tasks in a typical
EO-value-extraction workows, such as cloud masking, image co-registration, feature
extraction, classication, etc. Everyone is free to use any of the available tasks and is
encouraged to improve the, develop new ones and share them with the rest of the com-
munity.
Built-up area
The goal of the built-up area use case is identifying
new built-up areas using the drill-down method. The
use-case by Sinergise exploits the Global Mosaic
ARD cube of Sentinel-2 data at 120 m resolution as
a starting point of the drill down mechanism. After
the fast detection of built-up areas at that resolu-
tion, the process runs on 10 m resolution to classi-
fy articial surfaces and to detect changes. At that
point, very high-resolution imagery is used to detect
buildings.
Map Making
TomTom map making support use-case will inte-
grate GEM Land Cover services to perform a fully
automated and repeatable global land cover map-
ping for small-mid scale and optimised land cover
map at large scale.
Crop-type identication
Meteoblue use case seeks to improve automat-
ic crop identication, validate the obtained results
through reliable ground truth data and enable auto-
matic crop growth stage identication. It relies on
Statistical API aDC approach to get the data for the
millions of elds across large AOIs and on TUM’s
expertise in ML. The use case supports operation-
al decisions when managing crops and the quanti-
tative monitoring of actual vs. planned or reported
land use (production forecast).
Conict pre-warning map use case
SatCen pilot explores the Climate-Security nex-
us. A Conict Pre-Warning (CPW) Map aims to
provide a new security product to support deci-
sion-making. It looks at correlations between glob-
al climate changes and environmental issues with
human activity behaviours, in support to guaran-
teeing the security of citizens.
eo-grow is an Earth observation framework for scaled-up processing in Python. The
eo-grow package takes care of running the workows at a large scale. In eo-grow an
EOWorkow based solution is wrapped in a pipeline object, which takes care of param-
etrization, logging, storage, multi-processing, EOPatch management and more. How-
ever, pipelines are not necessarily bound to EOWorkow execution and can be used for
other tasks such as training ML models.
Sentinel-Hub is a cloud API for satellite imagery that takes care of all the complexity of
handling satellite imagery archive and makes it available for end-users via easy-to-inte-
grate web services.
In GEM we exploit large-scale processing capabilities of Sentinel-Hub:
• Batch Processing API for creating pixel-based Adjustable Data Cubes
Batch Statistical API for object-based Adjustable Data Cubes and Analysis Ready Data
• Bring Your Own Data for visualization and sharing of results
meteoblue is a weather service that offers access to environmental datasets like weath-
er simulations, ECMWF ERA5, gridded satellite information, soil properties and other
reanalysis datasets. Terabytes of data are accessible through the meteoblue Dataset
API. A unique feature of the meteoblue service is the option process data directly on
the service to perform expensive transformations and retrieve only the relevant infor-
mation from a dataset. The concept of transformations is exploited to offer environ-
mental data at satellite resolution in eo-learn.
The Sentinels Landsat
Collections
Commercial
Collections
DEM ENVISAT
MODIS Copernicus
services
Other Public
Collections
Bring Your
Own Data
Matej Batič1, Grega Milčinski1, Nicoletta Addimando2,
Francesco Perfetto3, Michele Lazzarini4, Marco Koerner5
1 Sinergise, 2 meteoblue, 3 TomTom, 4 European Union
Satellite Centre, 5 Technische Universität Munchen
continuous monitoring of large areas
in a sustainable and cost-effective way
Sentinel-2 L2A
120m Mosaic
© SentinelHub
Copernicus Sentinel-2
L2A data
© SentinelHub
SPOT
© AIRBUS DS 2020
Pleiades
© CNES 2020,
Distribution AIRBUS DS
Weather data Ground truth
data
EuroCrops Other (geo-int)
data
The project has received funding from European Union’s
Horizon 2020 Research and Innovation Programme” under
the Grant Agreement 101004112.
Open Imagery:
Copernicus, USGS
Machine
learning
API
OGC
Comercial Imagery:
Airbus, Planetscope,
Maxar
Bring Your Own Data:
Aerial Imagery, Other
Sensors
Web/Mobile Apps
GIS: Cloud and Desktop
Apps
Other Data: CORINE,
DEM
Sentinel-1 Sentinel-2
Automatically
computed
water mask
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