C L C R

PYEOJohn Roberts, Dr Yaqing Gou

Change Detection map

Change Detection map

Change Detection map

3 commands for a change map

$ extract_signatures indonesia.tif indonesia_training.shp indonesia_sigs.csv

$ create_model_from_signatures indonesia_sigs.csv indonesia_basic.pkl

$ simple_classification indonesia.tif indonesia_basic.pkl indonesia_classes.tif

...and some other work

• Images needed to be found

• Images had to be downloaded

• Images had to be preprocessed

• Images had to be stacked for change detection

• Training data had to be defined and collected

• Map had to be made nice in QGIS

...and some other work

• Images needed to be found

• Images had to be downloaded

• Images had to be preprocessed

• Images had to be stacked for change detection

• Training data had to be defined and collected

• Map had to be made nice in QGIS Involves value judgements; needs humans

Does not need humans!

PYthon for Earth Observation (PYEO)• A set of building blocks for making

automatic GIS processing chains• Download to classification

• Goal: GIS without HPC• Many GIS profressionals are NOT

programmers

•3 ways of using• Pre-made processing chains

• Single step scripts

• As a Python library

Technical bits• Hosted on Github

• github.com/clcr/pyeo• Managed using Conda

• Built on the following core Python packages

• Numpy

• Scipy

• Scikit-learn

• GDAL

• Joblib

• And two Sentinel libraries

• sentinelhub

• sentinelsat

Pyeo structure• pyeo/

• Contains the core libraries

• apps/

• Processing chains and example scripts

• Built out of functions in pyeo/

• tests

• Test suite

• Contains examples of using individual functions

• docs

• Documentation for functions

Main libraries

raster_manipulation.py

• Creating rasters

• Stacking/clipping/managing rasters

• Creating base composites

• Preprocessing

• Cloud masking (fmask and sen2cor)

queries_and_downloads.py

• Querying data sources

• Filtering queries

• Downloading data

classification.py

• Extracting training data

• Creating models

• Applying pixel-wise models to rasters

– (segmentation to come)

filesystem_utilities.py

• Creating folders

• Logging

• Managing .SAFE files

• Querying metadata

Prototype libraries

validation.py

• Stratified sampling

• P-value calculation

• Implementation of Olafson etal

terrain_correction.py

• Applying

• Filtering queries

• Downloading data

your_library.py

Bring your work to the world?

Example complex data flow

Example application: Forest Sentinel

Satellite optical and radar

data acquisition

Forest Agency / Community field

based response to illegal logging

and deforestation

Cloud based data analysis and

forest cover change mapping

Web-based & smartphone

app deforestation alerts

and forest information

Sentinel-2 NRT change proc-chain

Auto data filtering/

downloadlatest

imagery

Preprocessing, sen2cor/ 6S

BOA correction

Scene classification,

Cloud masking

Repository of scene training

data

Sentinel 2 data

hub

Cloud free base image

Most recentS2

acquisition

Change detection

(RF)

Imagestack

Repository of change

training data

Thematic map of land

cover change (1)

Probability map of forest cover change

(2) Kmz alert file &Json

database (3)

Data download & atmospheric processing Data preparation for deforestation detectionDeforestation detection & output production

1 2

3

DEFORESTATION ALERT APP

DEFORESTATION ALERT APP

Future

Future

Thankyou for listening

Any questions?