Use Cases
Use Case 1: System continues forward “keep-up” production of SLC for landslide AOI.
User adds an AOI to the system to keep up processing on , and sets the type of AOI as “landslide”.
System automatically processes to SLCs upon new data acquisitions in AOI and publishes to S3.
Use Case 2: System continues forward “keep-up” production of S1-GUNW for landslide AOI.
User adds an AOI to the system to keep up processing on, and sets the type of AOI as “landslide”.
System automatically processes to L2 S1-GUNW upon new acquisitions in AOI and publishes to S3.
Use Case 3: System monitors specific areas of interest to detect potential landslide anomalies, using higher resolution time series.
User adds an AOI to the system to keep up processing on , and sets the type of AOI as “landslide”.
System automatically creates coregistered SLC stacks upon new data acquisition in the AOI.
Note to Developer: Will need to add trigger rule upon SLC acquisition to send SLC stack (along with the corresponding “bbox”) through the TopsStack Processor PGE.System automatically performs PS time series processing on SLC stack.
Note to Developer: Will need to add trigger rule upon publishing of TopsStack Processor PGE output to send that output through StaMPS PGE.System automatically applies ML to detect potential anomalies, and publishes results back to GRQ catalog
Note to Developer: Will need to add trigger rule for this.User logs into system to browse potential anomalies.
Use Case 4: System monitors specific areas to detect potential landslide anomalies, using lower resolution time series
User adds an AOI to the system to keep up processing on , and sets the type of AOI as “landslide”.
System automatically processes to L2 S1-GUNW upon new data acquisition in the AOI.
Note to Developer: Will need to add trigger rule.System automatically performs displacement time series processing on S1-GUNWs.
Note to Developer: Will need to add trigger rule.System automatically applies ML to detect potential anomalies, and publishes results back to GRQ catalog
Note to Developer: Will need to add trigger rule for this.User logs into system to browse potential anomalies.
Notes:
Currently, we are testing the lower resolution time-series (from the MintPy PGE). The eventual goal is to have the possibility of using both the PS time-series and the MintPy time-series, but current efforts are focused on validating the MintPy time-series.
Future possible Use Cases:
The scope of this pipeline could be expanded to include monitoring of wider areas to identify potential landslide anomalies.
Pipeline
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Pipeline
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The landslide anomaly detection pipeline in the figure above has two possible roots. The first, outlined by the light blue dashed line, stems from the portion of the Standard Product Pipeline similarly outlined. This option uses higher resolution time-series data generated from coregistered SLC stacks via StaMPS PS time-series processing. The second option, outlined by the dashed red line, comes from L2 S1-GUNW data products, indicated by similar red outline on the Standard Product Pipeline figure. The GUNW products are run through the MintPy PGE, and create a lower resolution displacement time-series.
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For the lower resolution option, the user can define an AOI track. When the AOI track evaluator determines that the full track has been processed, it publishes a JSON file that a S1-GUNW is created within a defined volcano polygon, it triggers the MintPy PGE to run. The MintPy PGE then publishes a displacement time-series, which triggers the Tensorflow Predictor PGE to run and publish a Landslide Anomaly Map.
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Trigger dataset/action: On demand Action “MintPy (SNWE Bounds)”, trigger dataset = S1-GUNW within defined polygon.
Inputs: latitude_longitude_bounds (or polygon), track_number, start_date, end_date
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Outputs: predicted landslide anomaly data (.pred files)
Use Cases
Use Case 1: System continues forward “keep-up” production of SLC for landslide AOI.
User adds an AOI to the system to keep up processing on , and sets the type of AOI as “landslide”.
System automatically processes to SLCs upon new data acquisitions in AOI and publishes to S3.
Use Case 2: System continues forward “keep-up” production of S1-GUNW for landslide AOI.
User adds an AOI to the system to keep up processing on, and sets the type of AOI as “landslide”.
System automatically processes to L2 S1-GUNW upon new acquisitions in AOI and publishes to S3.
Use Case 3: System monitors specific areas of interest to detect potential landslide anomalies, using higher resolution time series.
User adds an AOI to the system to keep up processing on , and sets the type of AOI as “landslide”.
System automatically creates coregistered SLC stacks upon new data acquisition in the AOI.
Note to Developer: Will need to add trigger rule upon SLC acquisition to send SLC stack (along with the corresponding “bbox”) through the TopsStack Processor PGE.System automatically performs PS time series processing on SLC stack.
Note to Developer: Will need to add trigger rule upon publishing of TopsStack Processor PGE output to send that output through StaMPS PGE.System automatically applies ML to detect potential anomalies, and publishes results back to GRQ catalog
Note to Developer: Will need to add trigger rule for this.User logs into system to browse potential anomalies.
Use Case 4: System monitors specific areas to detect potential landslide anomalies, using lower resolution time series
User adds an AOI to the system to keep up processing on , and sets the type of AOI as “landslide”.
System automatically processes to L2 S1-GUNW upon new data acquisition in the AOI.
Note to Developer: Will need to add trigger rule.System automatically performs displacement time series processing on S1-GUNWs.
Note to Developer: Will need to add trigger rule.System automatically applies ML to detect potential anomalies, and publishes results back to GRQ catalog
Note to Developer: Will need to add trigger rule for this.User logs into system to browse potential anomalies.
Notes:
Currently, we are testing the lower resolution time-series (from the MintPy PGE). The eventual goal is to have the possibility of using both the PS time-series and the MintPy time-series, but current efforts are focused on validating the MintPy time-series.
Future possible Use Cases:
The scope of this pipeline could be expanded to include monitoring of wider areas to identify potential landslide anomalies.