GDAL/OGR

When working with raster datasets of different projections and resolutions, it is often desirable to reproject them to the same projection and align them to the same pixel grid. In this post, we will explore the recently introduced options in the open-source GDAL utility gdalwarp that makes this process much simpler and efficient. In particular, we will be exploring the -r sum (Resample with Sum), -r average (Resample with Average) and -tap (Target Aligned Pixels). We will take the following 3 raster datasets and clip, resample and align them to a common pixel grid. LandScan Global: A high-quality global population grid that is available at 1km resolution in the geographic CRS WGS84 Lat/Lon (EPSG:4326). GHS Population Grid: A 100m resolution global population dataset that is distributed in the World Mollweide Equal Area Projection (ESRI:54009). NLCD Tree Canopy Cover: A 30m resolution gridded dataset with percent canopy estimate of tree cover in the NAD83 CONUS Alberts Projection (EPSG:5070). As you can see we have datasets that have widely varying pixel sizes and projections. If we wanted to compare them with each other - we must first harmonize them on a unified pixel grid. We will learn how to reproject, resample and align these to the NAD83 California Albers Projection (EPSG:3311) and at 1km resolution. ...

GDAL has support for GDAL Virtual File System which allows GDAL library and command-line tools to work with files on network storage. This is critical in the era of Cloud-Native Geospatialwhere it is becoming a standard practice to access and share geospatial data via cloud storage services. In this post, we will see how to use GDAL command-line tools to read and write data to Google Cloud Storage (GCS) using the /vsigs file system handler. We will focus exclusively on Google Cloud Storage for this post -but the same concepts apply when using other cloud services such as AWS S3 or Azure Blobstore. Similarly, other GDAL-based tools - such as rasterio - will be able to access the data from GCS using the same configuration options shown here. The post covers the following topics Reading Files from Public GCS Buckets. Creating Private GCS Buckets and Uploading Data Configuring Authentication and Reading Data from Private GCS Buckets Writing Data to Private GCS Buckets Using Environment Variables This post assumes familiarity with the GDAL command-line tools and assumes you have installed GDAL on your machine. You will find detailed instructions for installation in our course material for Mastering GDAL Tools. The code snippets are split over multiple lines for redability using the Windows Line Continuation character ^. If you are running these on Mac/Linux, you can replace them with \ instead. ...

Multi-criteria Overlay Analysis is the process of the allocation of land to suit a specific objective on the basis of a variety of attributes that the selected areas should possess. Although this is a common GIS operation, it is best performed in the raster space. This post outlines the typical workflow to take source vector data, transform them to appropriate rasters, re-classify them and perform mathematical operations to do a weighted suitability analysis. The post uses the command-line utilities provided by the open-source GDAL library. If you want to do such analysis in QGIS, please check my tutorial at Multi Criteria Overlay Analysis (QGIS3) We will work with crime and infrastructure data for the city of London and find suitable areas to build new parking facilities that can help reduce bicycle thefts. Our analysis will apply the following 3 criteria. The proposed parking must be In a bicycle theft hotspot Close to a bicycle route Far from existing parking facilities The problem statement: Identify suitable locations for building new bicycle parking facilities ...

When working with raster data, you may sometimes need to deal with data gaps. These could be the result of sensor malfunction, processing errors or data corruption. Below is an example of data gap (i.e. no data values) in aerial imagery. Source Image: © Commission for Lands (COLA) ; Revolutionary Government of Zanzibar (RGoZ), Downloaded from OpenAerialMap. (Note: The data gap is simulated using a python script and is not part of the original dataset) ...

gdal_calc is one of the lesser used tools among the GDAL utilities. There aren’t many examples of using it in the wild and some advanced features are not well documented. I am finding myself using it a lot lately and have discovered some really powerful use cases. ...

When working with elevation data, sometimes you may discover that 2 datasets from different providers have very different elevation values for the same location. A common reason for this being each dataset being referenced to a different surface. ...

GeoPDF is a unique data format that brings the portability of PDF to geospatial data. A GeoPDF document can present raster and vector data and preserve the georeference information. This can be a useful format for non-GIS folks to consume GIS data without needing GIS-software. While GeoPDF is a proprietary format, we have a close alternative in the open Geospatial PDF format. GDAL has added support for creating Geospatial PDF documents from version 1.10 onwards. In this post, I will show how to create a GeoPDF document containing multiple vector layers. ...

GDAL and OGR libraries come with handy command-line tools. These tools are quite powerful and can save you a lot of effort if you know how to use them. Here I will show how to use the ogrinfo and ogr2ogr tools to perform spatial joins. A single command can do complex operations on your spatial data and save you a lot of clicking-around and data-munging in a GIS. ...