cbm
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Introduction to JRC CbM
DIAS for CAP Checks by Monitoring
System architecture
Backend
Frontend
Data analytics
Setup
Setup DIAS VMs
Software prerequisites
Database for JRC CbM
Data preparation
Parcel extraction
Backing up the database
Build a RESTful API
RESTful API
Overview
Parcel information
Parcel Time Series
Sentinel image chips
Parcel orthophotos
Parcel peers
Data analytics
POST requests
Use cases
SALMS R-package
Calendar view
FOI Assessment
Machine learning
Marker detection
Overview of the marker detection tool
Modules description
Option file
Graphical user interface
Examples of marker detection
CbM python package
Overview
Installation
Configuration
DIAS catalog
Extraction
FOI examples
Data access
Developers
Contributing to CbM
Pull requests
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Introduction to JRC CbM
DIAS for CAP Checks by Monitoring
Main elements of the JRC CbM
DIAS and Copernicus Sentinel
CbM in the context of the CAP
JRC solutions for CbM
Contributing to JRC CbM
System architecture
Scope of the CbM system
Cloud resources: DIAS infrastructure
What are DIAS
DIAS Virtual Machines
Sentinel data access: DIAS object storage
What is DIAS object storage
Copernicus ARD generation
Data repository - PostgreSQL/PostGIS
Extraction of statistics - Python processing
Data access - RESTful API
Interfaces for data use - Jupiter Notebooks
JRC CbM roles
Backend
Goals
General requisites to set up the CbM system
How to create a CbM infrastructure
Links to the technical documentation
Set up a DIAS virtual machine
CbM database
CARD
Extract signatures
Create a RESTful API
Deployment of the CbM system
Expertise required
Enhancement Proposals
Terrain Correction
S2 Multi-band
Dask tests
ML Revision
RESTful meteo
Frontend
Goals
Links to the technical documentation
RESTful API
Python CbM package
Expertise required
Data analytics
Goals
Links to the technical documentation
Marker detection
Applications
Expertise required
Setup
Setup DIAS VMs
Connecting to the ‘tenant host’ vm via SSH
Software prerequisites
Docker (Ubuntu 20.04)
PostGIS
Optimizing
Essential CbM tables
Jupyter server
Build Jupyter image from source
Database for JRC CbM
DB in the CbM architecture
Spatial database in a nutshell
Main database elements
The spatial bit
PostgreSQL and PostGIS
SQL
JRC CbM DB structure
Database access
Server/client structure
Access parameters
User access policy
Data retrieval
PgAdmin
phpPgAdmin
Psql
QGIS
Jupiter Notebook
RESTful API
R
Export and import data
Performance optimization
Basic optimization
Advanced optimization
Data preparation
Adding shapefiles (.shp) to PostGIS database
Transfer metadata from the DIAS catalog
OpenSearch compliant catalogs (CREODIAS, MUNDI)
Database catalog (SOBLOO)
Benchmarking data formats for fast image processing
Parcel extraction
Overview
Implementation details
Configuration files
Run with docker
Parallelization with docker swarm
Create VMs
Install docker and set up docker swarm
Configure and run
Caveats
Backing up the database
Overview
Use pg_dump
Build a RESTful API
Prerequisites
Create RESTful API users
Database connection
Dataset configuration
Deploy the RESTful API docker container
Build from source
Provide available options (Optional)
Adding orthophotos (Optional)
RESTful API
Overview
Parcel information
Parcel Time Series
parcelTimeSeries
weatherTimeSeries
Simple python client to plot parcel Time Series
Sentinel image chips
chipByLocation
rawChipByLocation
Example client code.
Parcel orthophotos
Parcel peers
parcelPeers
parcelStatsPeers
parcelsByPolygon
Data analytics
parcel selection
time series statistics
histogram analysis, clustering
…
POST requests
rawChipsBatch
Example test script
rawS1ChipsBatch
Use cases
SALMS R-package
Introduction
Descriptors
Input data
FOIinfo
ts-files
plotTimeSeries
createSignals
What is week0
Produce and plot means, standard deviations and t-tests
Analysing signal probabilities
Summarizing the results
Correlations
References
Calendar view
1. Introduction
2. Dependencies
2.1 Calendar view Modules
3 Structure of the code
3.1 Initialization
3.2 Main Processing Loop
3.3 Output examples
FOI Assessment
Concept
Methods and algorithms
Test results
Cardinality
FOI Clustering
Machine learning
Introduction
Data preparation
Determine which classes to include
Composing the features for machine learning
Splitting in training and testing sets
Running the training and testing
Checking the results
Confusion matrix for testing sets
Combining different tensorflow runs
Identifying outliers
Next steps
Marker detection
Overview of the marker detection tool
Modules description
Option file
Graphical user interface
Examples of marker detection
CbM python package
Overview
Notebook widgets
Data stracture
Get widget
View widget
Installation
Dependencies
Installing from PyPI
Installing for development
Installing from source:
Uninstallation
Install GDAL
Troubleshooting
Configuration
Configuration widget
DIAS catalog
Notebook widget
Extraction
Extract widget
FOI examples
Version 1
Version 2
Data access
Parcel information
Time series
Show time series
Download time series
Background images
Show background images
Download background images
Sentinel chips
Developers
Contributing to CbM
Using the issue tracker
Pull requests
Bug reports
License
Pull requests
Read the Docs
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Versions
latest
Downloads
pdf
html
epub
On Read the Docs
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