๐ง๐ฝโ๐ Post-Disaster Land Cover Classification.
Project description
๐ง๐ฝโ๐ palisades
๐ง๐ฝโ๐ Post-disaster land Cover classification using Semantic Segmentation on Maxar Open Data acquisitions.
pip install palisades
graph LR
palisades_ingest_target["palisades<br>ingest -<br>target=<target> -<br>predict - - - -<br>to=<runner>"]
palisades_ingest_query["palisades<br>ingest -<br><query-object-name> -<br>predict - - - -<br>to=<runner>"]
palisades_label["palisades<br>label<br>offset=<offset> -<br><query-object-name>"]
palisades_train["palisades<br>train -<br><query-object-name> -<br><dataset-object-name> -<br><model-object-name>"]
palisades_predict["palisades<br>predict - - -<br><model-object-name><br><datacube-id><br><prediction-object-name>"]
palisades_buildings_download_footprints["palisades<br>buildings<br>download_footprints -<br><input-object-name> -<br><output-object-name>"]
palisades_buildings_analyze["palisades<br>buildings<br>analyze -<br><prediction-object-name>"]
palisades_analytics_ingest["palisades<br>analytics<br>ingest -<br><analytics-object-name>"]
palisades_analytics_render["palisades<br>analytics<br>render<br>building=<building-id><br><analytics-object-name>"]
target["๐ฏ target"]:::folder
query_object["๐ query object"]:::folder
datacube["๐ง datacube"]:::folder
dataset_object["๐๏ธ dataset object"]:::folder
model_object["๐๏ธ model object"]:::folder
prediction_object["๐ prediction object"]:::folder
analytics_object["๐ analytics object"]:::folder
query_object --> datacube
target --> palisades_ingest_target
palisades_ingest_target --> palisades_ingest_query
palisades_ingest_target --> query_object
query_object --> palisades_ingest_query
palisades_ingest_query --> palisades_predict
query_object --> palisades_label
palisades_label --> datacube
datacube --> palisades_train
query_object --> palisades_train
palisades_train --> dataset_object
palisades_train --> model_object
model_object --> palisades_predict
datacube --> palisades_predict
palisades_predict --> palisades_buildings_download_footprints
palisades_predict --> palisades_buildings_analyze
palisades_predict --> prediction_object
prediction_object --> palisades_buildings_download_footprints
palisades_buildings_download_footprints --> prediction_object
datacube --> palisades_buildings_analyze
prediction_object --> palisades_buildings_analyze
palisades_buildings_analyze --> prediction_object
prediction_object --> palisades_analytics_ingest
palisades_analytics_ingest --> analytics_object
analytics_object --> palisades_analytics_render
palisades_analytics_render --> analytics_object
classDef folder fill:#999,stroke:#333,stroke-width:2px;
palisades help
palisades \
ingest \
[~download,dryrun] \
[target=<target> | <query-object-name>] \
[~ingest | ~copy_template,dryrun,overwrite,scope=<scope>,upload] \
[predict,count=<count>,~tag] \
[device=<device>,profile=<profile>,upload] \
[-|<model-object-name>] \
[~download_footprints | country_code=<iso-code>,country_name=<country-name>,overwrite,source=<source>] \
[~analyze | buffer=<buffer>,count=<count>] \
[~submit | dryrun,to=<runner>]
. ingest <target>.
target: Altadena | Altadena-test | Brown-Mountain-Truck-Trail | Brown-Mountain-Truck-Trail-all | Brown-Mountain-Truck-Trail-test | Palisades-Maxar | Palisades-Maxar-test
scope: all + metadata + raster + rgb + rgbx + <.jp2> + <.tif> + <.tiff>
all: ALL files.
metadata (default): any < 1 MB.
raster: all raster.
rgb: rgb.
rgbx: rgb and what is needed to build rgb.
<suffix>: any *<suffix>.
device: cpu | cuda
profile: FULL | DECENT | QUICK | DEBUG | VALIDATION
country-name: for Microsoft, optional, overrides <iso-code>.
iso-code: Country Alpha2 ISO code: https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes
Canada: CA
US: US
source: microsoft | osm | google
calls: https://github.com/microsoft/building-damage-assessment/blob/main/download_building_footprints.py
buffer: in meters.
runner: aws_batch | generic | local
palisades \
label \
[download,offset=<offset>] \
[~download,dryrun,~QGIS,~rasterize,~sync,upload] \
[.|<query-object-name>]
. label <query-object-name>.
palisades \
train \
[dryrun,~download,review] \
[.|<query-object-name>] \
[count=<10000>,dryrun,upload] \
[-|<dataset-object-name>] \
[device=<device>,dryrun,profile=<profile>,upload,epochs=<5>] \
[-|<model-object-name>]
. train palisades.
device: cpu | cuda
profile: FULL | DECENT | QUICK | DEBUG | VALIDATION
palisades \
predict \
[~tag] \
[~ingest | ~copy_template,dryrun,overwrite,scope=<scope>,upload] \
[device=<device>,profile=<profile>,upload] \
[-|<model-object-name>] \
[.|<datacube-id>] \
[-|<prediction-object-name>] \
[~download_footprints | country_code=<iso-code>,country_name=<country-name>,overwrite,source=<source>] \
[~analyze | buffer=<buffer>,count=<count>]
. <datacube-id> -<model-object-name>-> <prediction-object-name>
device: cpu | cuda
profile: FULL | DECENT | QUICK | DEBUG | VALIDATION
country-name: for Microsoft, optional, overrides <iso-code>.
iso-code: Country Alpha2 ISO code: https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes
Canada: CA
US: US
source: microsoft | osm | google
calls: https://github.com/microsoft/building-damage-assessment/blob/main/download_building_footprints.py
buffer: in meters.
palisades \
analytics \
ingest \
[acq=<-1>,buildings=<-1>,dryrun,upload] \
[-|<object-name>]
. ingest analytics.
palisades \
analytics \
render \
[building=<building-id>,~download,dryrun,upload] \
[.|<object-name>]
. render analytics.
๐STAC Catalog: Maxar Open Data |
๐๏ธVision Algo: Semantic Segmentation |
๐ง๐ฝโ๐Building Damage Analysis |
๐ง๐ฝโ๐Analytics |
Acknowledgments
- The concept and workflow of this tool is heavily affected by microsoft/building-damage-assessment.
palisades buildings download_footprintscallsdownload_building_footprints.py.palisades buildings analyzeis based onmerge_with_building_footprints.py.- Through satellite-image-deep-learning.
built by ๐ blue_options-4.197.1, based on ๐ง๐ฝโ๐ palisades-4.200.1.
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