Raster Data
Raster dataset size restrictions QGIS has restrictions on the maximum size of raster datasets stored in file geodatabases that it can access and render. Specifically, rasters over 140 GB in size exceed the support limits, resulting in errors when attempting to add them to a QGIS project. This restriction is tied to technical factors related…
Locating the Source of Misalignment Identifying poorly georeferenced data When bringing raster and vector datasets into QGIS for mapping, users may notice alignment issues even when both layers are projected into the same coordinate reference system (CRS). This misalignment is often due to inaccurate or missing georeferencing information in the source data. Georeferencing defines the…
Definition and Importance of Slope Slope, defined as the rate of elevation change across a landscape, is a fundamental geographic attribute with widespread utility. Slope calculations from global digital elevation models (DEMs) inform critical environmental research and applications in climatology, hydrology, geomorphology, ecology, natural hazard assessment, and more. As a first derivative of elevation, slope…
Understanding LiDAR Data Complexity LiDAR (Light Detection and Ranging) data is known for its massive file sizes that require specialized software to process and analyze. The complex point cloud structure contains dense XYZ coordinates that create an intricate 3D model of the scanned landscape. Massive File Sizes Requiring Specialized Software A single LiDAR data capture…
Understanding LAS File Sizes Light Detection and Ranging (LiDAR) data is often stored in the LAS file format. LAS files contain dense 3D point clouds that represent terrain and landscape features scanned by aerial LiDAR systems. As high-resolution LiDAR becomes more prevalent, LAS file sizes have increased dramatically into the multi-gigabyte range. Managing extremely large…
The Challenge of Complex Landscapes Accurately classifying ground points in complex landscapes with high variability poses significant challenges for point cloud classification algorithms. Complex landscapes contain diverse land cover types – forests, urban areas, water bodies, and exposed earth – that interact in complex ways. The high degree of spatial variability in ground height, slope,…
Understanding LiDAR Data Complexity LiDAR (Light Detection and Ranging) data is known for its massive file sizes that require specialized software to process and analyze. The complex point cloud structure contains dense XYZ coordinates that create an intricate 3D model of the scanned landscape. Massive File Sizes Requiring Specialized Software A single LiDAR data capture…
Removing Vegetation from LiDAR Data LiDAR (Light Detection and Ranging) data collected from aerial platforms contains returns from both the bare earth surface as well as objects above the ground including buildings, trees, and other vegetation. To create an accurate bare earth digital elevation model (DEM), points classified as vegetation must be removed while retaining…
The Challenge of Distinguishing Ground from Non-Ground Points Accurately separating ground points from non-ground points in LiDAR point clouds is a key step in generating high-quality digital terrain models (DTMs). However, distinguishing low vegetation, buildings, and other structures from bare earth points can be challenging, especially in urban areas. Manual classification is time-consuming, while traditional…
