Martin Christen

In this tutorial, you will learn about the various Python modules for processing geospatial data, including GDAL, Rasterio, Pyproj, Shapely, Folium, Fiona, OSMnx, Libpysal, Geopandas, Pydeck, Whitebox, ESDA, and Leaflet. You will gain hands-on experience working with real-world geospatial data and learn how to perform tasks such as reading and writing spatial data, reprojecting data, performing spatial analyses, and creating interactive maps. This tutorial is suitable for beginners as well as intermediate Python users who want to expand their knowledge in the field of geospatial data processing

Geospatial data, which refers to data that has a geographic component, is a crucial part of many fields, including geography, geography, urban planning, and environmental science. In this tutorial, you will learn about the various Python modules that are available for working with geospatial data. We will start by introducing the **GDAL** (Geospatial Data Abstraction Library) and **Rasterio** modules, which are used for reading and writing raster data (data stored in a grid of cells, where each cell has a value). You will learn how to read and write common raster formats such as GeoTIFF and ESRI ASCII, as well as how to perform common raster operations such as resampling and reprojecting. Next, we will cover the **Pyproj** module, which is used for performing coordinate system transformations. You will learn how to convert between different coordinate systems and how to perform common tasks such as converting latitude and longitude coordinates to UTM (Universal Transverse Mercator) coordinates. After that, we will introduce the **Shapely** module, which is used for working with geometric objects in Python. You will learn how to create and manipulate points, lines, and polygons, as well as how to perform spatial operations such as intersection and union. Then, we will cover the **Folium** module, which is used for creating interactive maps in Python. You will learn how to create simple maps, add markers and popups, and customize the appearance of your maps. Next, we will introduce the **Fiona** module, which is used for reading and writing vector data (data stored as individual features, each with its own geometry and attributes). You will learn how to read and write common vector formats such as ESRI Shapefile and GeoJSON, as well as how to access and manipulate the attributes of vector features. After that, we will cover the **OSMnx** module, which is used for working with OpenStreetMap data in Python. You will learn how to download and manipulate street networks, buildings, and other geospatial data from OpenStreetMap. Next, we will introduce the **Libpysal** module, which is used for performing spatial statistics and econometrics in Python. You will learn how to calculate spatial weights, perform spatial autocorrelation tests, and estimate spatial econometric models. Then, we will cover the **Geopandas** module, which is used for working with geospatial data in a Pandas DataFrame. You will learn how to load and manipulate vector data, perform spatial joins, and create choropleth maps. After that, we will introduce the **Pydeck** module, which is used for creating interactive 3D maps in Python. You will learn how to create 3D point clouds, 3D building models, and other 3D geospatial visualizations. Next, we will cover the **Whitebox** module, which is a powerful open-source GIS toolkit for performing geospatial data processing and analysis. You will learn how to use Whitebox to perform tasks such as raster reclassification, terrain analysis, and hydrological modeling. Finally, we will introduce the **ESDA** (Exploratory Spatial Data Analysis) and **LeafMap** modules, which are used for exploring and visualizing spatial patterns and relationships in data. You will learn how to calculate spatial statistics such as Moran's I and local spatial autocorrelation statistics, and how to create interactive choropleth maps. By the end of this tutorial, you will have a solid understanding of the various Python modules that are available for working with geospatial data and will have hands-on experience applying these tools to real-world data. This tutorial is suitable for beginners as well as intermediate Python users who want to expand their knowledge in the field of geospatial data processing.

In this workshop we show our recent deep learning projects and how semantic segmentation is applied

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Other people: Adrian Meyer