A practical guide for geospatial practitioners and remote sensing enthusiasts Introduction Land cover classification — the process of labeling every pixel in a satellite or aerial image with a category like forest, water, urban, or cropland — is one of the most impactful applications of deep learning in geospatial science. Accurate, up-to-date land cover maps…
A comprehensive guide for GIS professionals, remote sensing analysts, and ML engineers working at the intersection of deep learning and geospatial data. Table of Contents Introduction Satellite imagery has undergone a quiet revolution. What was once the exclusive domain of government agencies and large defense contractors is now commercially available at sub-meter resolution, refreshed daily,…
If you work in GIS, remote sensing, or geospatial analysis, two names come up constantly: Landsat and Sentinel. Both are free. Both are globally available. Both have powered some of the most important environmental and land-use studies of the last decade. But they are not interchangeable. Choosing the wrong one for your project wastes time,…
For decades, spatial analysis followed a familiar script. You loaded your data, applied a geostatistical model, ran your overlay or interpolation, and interpreted the output. The methods were rigorous. They were also largely manual, rule-based, and bounded by what a human analyst could define upfront. Machine learning is changing that. Not by replacing spatial thinking,…
Land use classification is one of the most common tasks in GIS and remote sensing. It answers a simple question: what is on the ground, and where? To answer that question reliably, you need imagery you can trust. And for most analysts working today, that imagery comes from Sentinel-2. This article walks through the full…
Urban areas are among the fastest-changing landscapes on Earth. Cities grow outward into agricultural land and forests. Peri-urban zones shift from rural to built-up within a single decade. Infrastructure extends into areas that satellites imaged as open land just years before. Remote sensing is one of the few tools capable of capturing this change systematically,…
Satellite imagery is one of the most powerful inputs in any GIS workflow. It drives land cover classification, change detection, environmental monitoring, disaster response, and urban planning. But access to high-quality imagery has historically come with a high price tag. That has changed significantly over the last decade. Governments, space agencies, and open-data initiatives have…
Remote sensing has fundamentally changed how we observe and analyze the Earth. Where traditional surveys once required boots on the ground, modern sensors mounted on satellites, aircraft, and drones can now capture rich spatial data across vast areas in hours. But not all remote sensing data is the same. Three of the most powerful and…
Introduction Satellite imagery has transformed how we monitor the planet. One of the most widely used techniques for extracting meaningful information from that imagery is NDVI: the Normalized Difference Vegetation Index. It is a simple formula with a profound application. It tells you, from space, how healthy vegetation is across a landscape. This article walks…
Introduction If you work with spatial data in Esri‘s ecosystem, chances are you have heard about the ArcGIS Python API. It is one of the most powerful tools available to GIS professionals who want to automate workflows, manage geospatial content, and perform spatial analysis without clicking through a GUI every time. This guide is written…
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