Working with Remote Files | Protocols supported | Examples | COPC | EPT (Entwine Point Tiles) | VPC

Overall functionality | Reader | Triangulate | Rasterize | Rasterize - triangulation | Rasterize - internal metrics | Rasterize - R expression | Rasterize - buffered | Transform with | Write LAS | Local maximum | Tree Segmentation | Buffer | Hulls | Readers | Summarise | Plot inventory | Wildcard Usage | Compatibility with lidR | Stop pipeline if | Parallel processing | Pre-read a point cloud in memory | Other stages

Build a LAS object reading a las file | Basic structure of a LAS object | @data: the point cloud | @header: the header | @crs: the CRS | @index: the spatial indexing mode | Allowed and non-allowed manipulation of a LAS object | Extra attributes and extra bytes in a LAS object | Extra attributes | Extra bytes attributes | Validation of LAS objects | Display a LAS object | Memory considerations | Deep copies | Shallow copies | Parameter select | Parameter filter

Build a LAScatalog object reading a folder of las files | Basic structure of a LAScatalog object | Allowed and non-allowed manipulation of a LAScatalog object | Validation of LAScatalog object | Display a LAScatalog object | Apply lidR functions on a LAScatalog | Partial processing | Some practical examples | Example 1 - Raster | Example 2 - Raster | Example 3 - Raster | Example 4 - Tree detection | Example 5 - Decimate | Example 6 - Decimate | Example 7 - Clip | Example 8 - Clip

What problem are we trying to tackle? | Illustration | Step 1: Data Extraction | Step 1.1: Reading the Data | Step 1.2: Radius Clipping | Step 1.3: Ground Classification and Noise Filtering | Step 1.4: Matching feature extraction | Step 2: Centroid Alignment | Step 3: Coarse Alignment | Step 3.1: Initial Brute Force Alignment | Step 3.2: Refinement of Coarse Alignment | RMSE Computation | Step 4: Fine Alignment of CHM and DTM | Step 4.1: Fine Alignment in XY using trimmed ICP | Step 4.2: Fine alignment on the Z axis using trimmed ICP | Step 5 Extra fine alignment of trees | Step 5.1: Feature extraction | Step 5.2: Extra-fine feature alignment using trimmed ICP | Step 6: Full Registration | Benchmark | Accuracy | Speed | Trimmed ICP

Sequential strategy | Concurrent points strategy | Concurrent files strategy | Nested strategy | Special cases | Real timeline

Rasterize | Callback | Buffer | Parallelisation

Canopy Height Model | Code | Result | Digital Terrain Model | Multiple raster | Normalization | Local maximum | Complex Pipeline

Rationnale for lasR vs. lidR | Speed | Pipeline | R binding | Main differences between lasR and lidR | Data loading | Dependencies | Code | Should I use lidR or lasR? | Example 1 | Example 2 | Example 3 | Example 4 | Example 5

Concept | Exemple 1 | Exemple 2 | Naming convention

Overview | High-level API | Control of the chunk size | Control of the chunk buffer | Control of the chunk alignment | Filter points | Select attributes | Write independent chunks on disk | Modification of the file format | Progress estimation | Error handling | Empty chunks | Parallel processing | Overlapping tiles | Partial processing | Partial output | Low-level API | Making a valid function for catalog_apply() | Buffer management | Control the options provided by the catalog | Merge the outputs | Make a high-level function | Advanced usages of the engine | Modify the drivers | Multi-machines paralellisation

Generic considerations on LAScatalog processing | Read a las file vs read a laz file | Indexation of the points with lax files | Load only attributes of interest | Changing the hardware | Benchmarks | Simple canopy height model | Area-based Approach | Clip ground inventories

Design a noise filter | Extend the filter_noise function to a LAScatalog | Finalize the functions