Geospatial Data Science with Earth Engine and Foursquare in Python

Large-scale geospatial analysis once required massive local computing power and expensive desktop software. Today, cloud-based platforms and powerful APIs allow you to analyze planetary-scale satellite imagery and location intelligence directly from your Python environment. This text-based course guides you through the fundamentals of geospatial data science. You will learn how to connect to Google Earth Engine and the Foursquare API to extract, manipulate, and analyze spatial data, combining remote sensing imagery with real-world location intelligence to solve complex spatial problems. What you'll learn: - Understand the fundamental concepts of GIS, remote sensing, and coordinate reference systems. - Configure Python environments and write clean, type-hinted scripts to query geospatial APIs. - Extract and filter multi-spectral satellite imagery using Google Earth Engine. - Retrieve and analyze point-of-interest (POI) location data using the Foursquare API. - Apply modern Python libraries like geopandas to clean and structure spatial datasets. - Implement basic machine learning and classification workflows on remote sensing data. The course begins with core geospatial definitions and API authentication basics before moving into hands-on data retrieval. You will progress from simple queries to advanced spatial analysis and image classification workflows using structured, step-by-step written explanations and code snippets. This course is designed for beginners to geospatial analysis, data science enthusiasts, and developers looking to expand their skills into spatial data. No prior experience with GIS software or remote sensing is required, though a basic understanding of Python is helpful. Start your journey into geospatial data science and unlock the power of cloud-based spatial analysis today.