Automated update of geospatial models with new survey data

Purpose

1. Automate the continuous integration of new survey and field data into existing geospatial models for mining operations.

2. Automates data ingestion, processing, validation, and model updating to ensure real-time accuracy of resource estimation and operational planning.

3. Enables automatedly syncing field surveyor inputs with centralized geospatial platforms, reducing manual workload and human error.

4. Supports rapid, automatable updates enabling timely decision-making for engineering, compliance, and operational teams.

Trigger Conditions

1. Automated trigger on receipt of new survey data files (CSV, XLSX, JSON, shape files) in a monitored cloud storage or database.

2. Scheduled automation (e.g., hourly, daily) for routine model refreshing.

3. Manual surveyor submission via web form or mobile app.

4. API notification from drones or remote sensors that detect new geospatial measurements.

5. Automatedly detecting significant data deltas prompting mandatory model updates per engineering thresholds.

Platform Variants

1. AWS Lambda

• Feature/Setting: Event-driven automator for S3 upload, configuring function to execute Python script on new survey file detection.
  

2. Azure Functions

• Feature/Setting: HTTP-triggered automating function for data integration APIs, auto-runs on field data POST to blob storage.
  

3. Google Cloud Functions

• Feature/Setting: Cloud Storage trigger for automate-on-upload to BigQuery geospatial tables, auto-update processing pipeline.
  

4. ESRI ArcGIS REST API

• Feature/Setting: 'Apply Edits' automation endpoint, configure webhook to ingest and update survey features.
  

5. QGIS Processing Toolbox

• Feature/Setting: Automate batch geoprocessing with Python scripts, scheduled via cron or CI/CD pipeline.
  

6. Mapbox Uploads API

• Feature/Setting: Automating automated upload and update of geospatial datasets through /uploads endpoint.
  

7. CARTO SQL API

• Feature/Setting: Auto execute SQL INSERT/UPDATE from field data to geospatial visualization layers.
  

8. FME Server

• Feature/Setting: Automator for scheduled or event-based geodata ETL flows, integrating field sources and model layers.
  

9. Safe Software FME Cloud

• Feature/Setting: API-driven automator jobs for survey-to-model updating workflows, automate transformation and validation.
  

10. PostGIS Trigger Functions

• Feature/Setting: Automate updates via PL/pgSQL triggers on GIS tables, runs on every new data row insert.
  

11. Snowflake Streams and Tasks

• Feature/Setting: Automated task on new data stream for geospatial data, processes and updates model storage.
  

12. Databricks Jobs API

• Feature/Setting: Automated notebook execution for survey data processing and model updates using REST jobs endpoint.
  

13. SAP HANA Spatial Services

• Feature/Setting: Automated API-driven update to geospatial layers leveraging HANA spatial calculation views.
  

14. OGC SensorThings API

• Feature/Setting: Subscribed automation for data changes, automatedly triggers model update when new observations detected.
  

15. Python GeoPandas

• Feature/Setting: Scriptable automation to merge, validate, and update geospatial datasets in Python, executed on schedule.
  

16. Microsoft Power Automate

• Feature/Setting: Workflow automator for file drop > GIS update > email notification in field operations.
  

17. Zapier

• Feature/Setting: Multi-service automation, such as Google Drive file update triggers ESRI model update.
  

18. Apache Airflow

• Feature/Setting: DAGs configured to automate the entire pipeline from survey input to model output with error checks.
  

19. Salesforce MuleSoft

• Feature/Setting: Automated integration of survey systems and GIS models via flows and connectors.
  

20. IBM Cloud Functions

• Feature/Setting: Event-triggered automating updates and scripts for mining geospatial data ingestion and processing.
  

Benefits

1. Reduces manual intervention, ensuring automated, timely updates and compliance with engineering standards.

2. Consistent accuracy of geospatial models using automatable validation and error checking.

3. Accelerates field-to-model cycle, delivering real-time updates for operational automating and decision support.

4. Scalable automation enables expansion to multiple mining sites and data sources without workflow redesign.

5. Minimizes risks of delayed or erroneous updates through automated, auditable processes.