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pyeo_linux_azure.ini
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# Edit the variables below and save before running the pipeline
[run_mode]
# flag for linear or parallel processing of the raster and vector processing pipeline
do_parallel = False
# wall_time_hours and qsub_processor_options are currently hardcoded to 24 hours, at line 495 of acd_national.py
qsub_processor_options = nodes=1:ppn=16,vmem=64Gb
# needs to be in the format "nodes=1:ppn=16,vmem=64Gb"
wall_time_hours = 3
watch_time_hours = 3
watch_period_seconds = 60
[forest_sentinel]
# aoi_name: The name of this area of interest. No spaces.
aoi_name=MauForest
# Baseline composite creation: Image acquisition dates for the initial cloud-free composite
composite_start=20230101
composite_end=20231231
# Change detection: Image acquisition dates in the form yyyymmdd
start_date=20240901
end_date=TODAY
# EPSG code for Kenya - north of equator and east of 36°E is EPSG:21097
# See https://epsg.io/21097 and https://spatialreference.org/ref/epsg/21097/
epsg=21097
# Cloud cover threshold for imagery to download
cloud_cover=25
# Certainty value above which a pixel is considered a cloud from sen2cor
cloud_certainty_threshold=0
# path to the trained machine learning model
model= /home/cmsstudent/pyeo/models/my_model/my_model.pkl
[environment]
# pyeo_dir needs to be an absolute path
pyeo_dir = /home/cmsstudent/pyeo
# tile_dir needs to be an absolute path
tile_dir = /home/cmsstudent/Desktop/pyeo_data
# Relative paths are relative to pyeo_dir/
integrated_dir = ./integrated
roi_dir = ./roi
roi_filename = /home/cmsstudent/Desktop/pyeo_data/mauforest/mauforest.shp
geometry_dir = /home/cmsstudent/pyeo/geometry/
s2_tiles_filename = kenya_s2_tiles.shp
log_dir = /home/cmsstudent/Desktop/pyeo_data/log
log_filename = my_log.log
credentials_path = /home/cmsstudent/pyeo/credentials/credentials.ini
# environment_manager = [venv, conda, pip]
environment_manager = conda
conda_directory = /home/cmsstudent/miniconda3/
conda_env_name = pyeo_env
# Path to the sen2cor preprocessor script, L2A_Process. Usually in the bin/ folder of your sen2cor installation.
sen2cor_path = /home/cmsstudent/Sen2Cor-02.10.01-Linux64/bin/L2A_Process
[raster_processing_parameters]
do_tile_intersection = True
# **************************************************************************************************************************
do_build_composite = True
do_classify = True
do_change = True
do_raster = True
chunks = 8
do_skip_existing = True
do_quicklooks = True
do_delete = False
do_zip = True
do_all = False
# do_build_prob_image, consider removing
do_build_prob_image = False
# do_update, consider removing
do_update = False
# do_dev, consider removing
do_dev = True
# **************************************************************************************************************************
# ***** STEP 4 SETUP GENERAL RASTER PROCESSING PARAMETERS *****
# download_source = scihub # scihub is now depracated
download_source = dataspace
# granules below this size in MB will not be downloaded
faulty_granule_threshold = 200
# list of strings with the band name elements of the image file names in "" string notation
# the wavebands specified here must match those used to build the random forest model specified in the Classify section below
band_names = ["B02", "B03", "B04", "B08"]
# file name pattern to search for when identifying band file locations in "" string notation
resolution_string = "10m"
# spatial resolution of the output raster files in metres. Can be any resolution. Recommended: 10,20 or 60.
output_resolution = 10
# **************************************************************************************************************************
# ***** STEP 4a DOWNLOAD REFERENCE IMAGES AND BUILD A MEDIAN COMPOSITE *****
# set buffer in number of pixels for dilating the SCL cloud mask (recommend 10 pixels of 10 m) for the composite building
buffer_size_cloud_masking_composite = 10
# maximum number of images to be downloaded for compositing, in order of least cloud cover
download_limit = 3
# **************************************************************************************************************************
# ***** STEP 4b: DOWNLOAD CHANGE DETECTION IMAGES FOR THE REQUIRED DATE RANGE *****
do_download = True
# set buffer in number of pixels for dilating the SCL cloud mask (recommend 30 pixels of 10 m) for the change detection
buffer_size_cloud_masking = 20
# **************************************************************************************************************************
# ***** STEP 4c: CLASSIFIY THE COMPOSITE AND CHANGE DETECTION IMAGES *****
# list of strings with class labels starting from class 1. Must match the trained model that was used.
class_labels = ["primary forest", "plantation forest", "bare soil", "cropland", "grassland", "open water", "burn scar", "cloud", "cloud shadow", "haze", "sparse woodland", "dense woodland", "artificial"]
# if sieve is 0, no sieve is applied. If >0, the classification images will be sieved using gdal and all contiguous groups of pixels smaller than this number will be eliminated
sieve = 0
# **************************************************************************************************************************
# ***** STEP 4d: DETECT CHANGES AND BUILD RASTER REPORTS *****
# find subsequent changes from any of these classes. Must match the trained model that was used.
change_from_classes = [1, 2]
# to any of these classes. Must match the trained model that was used.
change_to_classes = [3,4,5,7,11,12,13]
# **************************************************************************************************************************
# ***** STEP 5: VECTOR ANALYSIS OF TILE RASTER REPORTS *****
[vector_processing_parameters]
level_1_filename = gadm41_KEN_1.json
# vectorisation currently hardcoded to use level_1_filename
level_2_filename = gadm41_KEN_2.json
level_3_filename = gadm41_KEN_3.json
do_delete_existing_vector = True
do_vectorise = True
# ***** STEP 6: INTEGRATE VECTOR ANALYSES TO NATIONAL SCOPE *****
do_integrate = True
# **************************************************************************************************************************
# ***** STEP 7: FILTER NATIONAL SCOPE VECTORISED FOREST ALERTS *****
do_filter = False
# If there are any strings within counties_of_interest list, filtering by county will be attempted
counties_of_interest = [] # DO NOT USE, THIS IS DEPRACATED. Use admin_areas_of_interest instead
# Counties_of_interest = ["Kwale", "TransNzoia"]
admin_areas_of_interest = []
minimum_area_to_report_m2 = 120
# **************************************************************************************************************************
# ***** STEP 8: FILTER NATIONAL SCOPE VECTORISED FOREST ALERTS *****
# MANUAL ASSESSMENT OF ALERTS BY KFS STAFF AND FLAGGING FOR ALERTS FOR IN-PERSON INVESTIGATION BY RANGERS
# **************************************************************************************************************************
# ***** STEP 9: AUTOMATED DISTRIBUTION OF FILTERED ALERTS BY MESSAGING (E.G. WHATSAPP) *****
[alerts_sending_options]
do_distribution = True
email_alerts = True
email_list_file = /home/cmsstudent/pyeo/subscribers.txt
whatsapp_alerts = False
whatsapp_list_file = /home/cmsstudent/pyeo/whatsapp_list.txt
# **************************************************************************************************************************