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ActiveInferenceInstitute
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geo-infer-data

Manage geospatial data through ingestion, ETL pipelines, and quality assurance.

13|3|Updated May 13, 2025
Search Tags:#data quality#data management#pipeline#etl#storage#geospatial#gis
One-click install
npx skills add https://github.com/ActiveInferenceInstitute/GEO-INFER --skill geo-infer-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: geo-infer-data
Source: https://github.com/ActiveInferenceInstitute/GEO-INFER/tree/main/GEO-INFER-DATA
Command: npx skills add https://github.com/ActiveInferenceInstitute/GEO-INFER --skill geo-infer-data

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires aiohttp, api, asyncio, bz2, cloud, concurrent, core, database, file, gzip, h5py, hashlib, ingestion, lzma, models, pickle, pipeline, psutil, pymongo, re, rest_api, schemas, service, storage, stream, tarfile, threading, urllib3, utils, uuid, uvicorn, validation, xarray, zipfile, geopandas, pandas, numpy, shapely, rasterio, fiona, pyproj, scipy, scikit-learn, pyyaml, openpyxl, xlrd, fastapi, uvicorn[standard], requests, httpx, psycopg2-binary, redis, sqlalchemy, minio, boto3, pydantic, great-expectations, h3, rtree, lz4, zstandard, structlog, asyncio-mqtt, aiofiles, pytest, pytest-asyncio, pytest-cov, pytest-mock, python-dotenv, mypy, mkdocs, mkdocs-material, apache-airflow, pyspark, azure-blob-storage, google-cloud-storage, kafka-python, confluent-kafka, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a robust foundation for managing, processing, and storing geospatial data, ensuring all components have reliable access to analysis-ready information.

Core Features & Use Cases

  • Data Ingestion: Seamlessly ingest data from diverse sources like databases, APIs, and files (GeoJSON, Shapefile, GeoParquet).
  • ETL Pipelines: Build and manage complex Extract, Transform, Load workflows with automatic optimization and error recovery.
  • Data Quality: Ensure data integrity through comprehensive validation and quality assurance processes.
  • Use Case: Automate the ingestion of satellite imagery, sensor data, and crowdsourced environmental reports, process them through an ETL pipeline, and store the cleaned, analysis-ready data in a PostgreSQL database optimized for spatial queries.

Quick Start

Use the geo-infer-data skill to ingest satellite imagery from a specified bounding box and date range.

Frequently Asked Questions about geo-infer-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a geospatial data pipeline for ingesting satellite imagery and sensor data?▼

To build a geospatial data pipeline, use this Skill to automate ingestion from APIs and files, process the data through ETL workflows with automatic error recovery, and store the cleaned output in a database optimized for spatial queries.

What is the best way to ingest and validate diverse GIS formats like GeoJSON and Shapefile?▼

The best way to ingest GIS formats is using a pipeline that supports GeoJSON, Shapefile, and GeoParquet, applying comprehensive validation and quality assurance processes during the extract and transform stages to ensure data integrity.

Can I use geopandas and rasterio for ETL workflows with PostgreSQL storage?▼

Yes, you can use geopandas and rasterio within the ETL workflows to process spatial data, leveraging PostgreSQL connectors to store and optimize the cleaned, analysis-ready datasets for regional and civic applications.

Does this geospatial data management approach support cloud storage and streaming sources like Kafka?▼

Yes, the geospatial data management approach supports cloud storage connectors like MinIO and AWS S3, and integrates with Kafka to handle continuous streaming data ingestion alongside traditional database and file sources.

How do I ensure data quality when automating ETL pipelines for environmental reports?▼

To ensure data quality in ETL pipelines, implement comprehensive validation and quality assurance processes using frameworks like Great Expectations during data transformation, catching errors automatically before loading into storage.

What are the limitations of using xarray and h5py for geospatial data storage optimization?▼

While xarray and h5py handle multidimensional array storage, limitations arise with complex vector geometries; using GeoParquet and PostGIS via psycopg2 often yields better query performance for heavily indexed spatial relationships.

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