Spatial programming and remote sensing are essential components of modern technology and are transforming the way we understand our world. From analysing large amounts of satellite data to monitoring environmental changes, remote sensing provides a wealth of information about the earth and its systems. And, with the advancement of spatial programming, we are now able to automate and streamline these processes, making it easier to gather and analyse data on a massive scale. In this blog, we will delve into the exciting world of spatial programming and remote sensing, exploring new techniques, tools, and applications in this dynamic field.
This series of posts will introduce you to the methods required for spatial programming. Focusing on building your core programming techniques helps you use satellite imagery, track land-use change, track social distance during a pandemic, etc. Some Python programming experience is required, however, the material will be presented in a student-friendly manner and will focus on real-world applications. Open-source Python packages such as GeoPandas, Rasterio, Sklearn, and Geowombat are also necessary.
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