DOCSEARCHbyAlgolia
Submit Docs

scapesClassification

website·Indexed 30 days ago

https://ghtaranto.github.io/scapesClassification//index.html

An R package for raster object analysis and classification. It provides tools for identifying seed cells, local maxima or minima, and raster object borders, as well as functions for position index segmentation, neighborhood list generation, and conditional reclassification of cells.

Tokens
47.3K
Snippets
165
Records
276
Agent score
73%

What's inside scapesClassification

  1. Overview of scapesClassification for geo-space modeling

    The scapesClassification R-package allows users to translate mental models of seascapes, landscapes, and geo-spaces into computer representations. It classifies spaces and geographic objects using user-defined rules based on spatial data and the spatial relationships between different classes and objects. The process operates on raster surfaces, segmenting the space into distinct raster objects (groups of cells with unique IDs).

  2. Overview of scapesClassification package capabilities

    The scapesClassification package is used to identify raster objects and marine seafloor structures, specifically geomorphic management units (GMUs). The typical workflow for using the package involves: 1. Formatting input data, 2. Identifying island shelf units, 3. Identifying peak cells, and 4. Defining relief units and raster objects.

  3. Overview of scapesClassification

    The scapesClassification R-package allows users to translate mental models of seascapes, landscapes, and geo-spaces into computer representations. It classifies spaces and geographic objects using user-defined rules based on spatial data and the spatial relationships between different classes and objects. The process is performed on raster surfaces, where the package provides functions to segment the space and manipulate distinct raster objects (groups of cells with a unique ID).

  4. Overview of scapesClassification package capabilities

    The scapesClassification package is used to identify raster objects and marine seafloor structures, specifically Geomorphic Management Units (GMUs). The workflow generally follows these steps: formatting input data, identifying island shelf units, identifying peak cells, and defining relief units and raster objects.

  5. Overview of scapesClassification R-package

    The scapesClassification R-package translates mental models of seascapes, landscapes, and geo-spaces into computer representations. It performs classifications on raster surfaces by segmenting space and identifying distinct raster objects (groups of cells with a unique ID). The process relies on four key concepts:

    1. Anchor Locations: Identifying prominent features to start the classification.
    2. Spatial Relationships: Using expected relative positions between classes to guide classification.
    3. Focal Evaluations: Evaluating classification rules only in suitable locations to reduce misclassification.
    4. Multi-step Process: Using newly classified areas to define subsequent focal areas for further evaluation.

  6. Overview of scapesClassification package capabilities

    The scapesClassification package is used to identify raster objects and marine seafloor structures, specifically Geomorphic Management Units (GMUs). The workflow generally involves four main stages: formatting input data, defining island shelf units, identifying peak cells, and determining relief units and raster objects.

  7. Understand the GNU General Public License v3 (GPLv3) terms

    The GNU General Public License v3 is a free, copyleft license designed to guarantee the freedom to share and change software. Key principles include:

    • Freedom to Distribute: Users can distribute copies of the software, including charging for them, provided the same freedoms are passed to recipients.
    • Source Code Access: Recipients must be able to receive the source code.
    • Modification: Users can modify the software or use pieces of it in new free programs, but modified versions must be marked as changed to avoid misattributing issues to original authors.
    • No Warranty: The license explicitly states there is no warranty for the software.
    • Anti-Circumvention: The license prohibits practices that deny users the ability to install or run modified versions of the software on their devices.

  8. Understand Class Vectors in scapesClassification

    Class vectors are the central mechanism for storing classifications in scapesClassification. They map raster cells to numeric classes (or NA if unclassified).

    Key characteristics:

    • The nth element of a class vector corresponds to the raster cell in the nth row of the attribute table.
    • They act as both input and output for classification functions.
    • When used as input, they allow for focal evaluation of rules.
    • Functions update class vectors by assigning numeric classes only to previously unclassified cells that meet specific conditions, enabling multi-step classification workflows.

  11. Understand the GNU General Public License v3 (GPLv3) permissions

    The GNU GPLv3 is a copyleft license designed to guarantee the freedom to share and change software. It allows users to copy, distribute, and modify the software, provided that the same freedoms are passed on to all recipients. Key requirements for those distributing the software include: (1) asserting copyright, (2) providing the license to recipients, and (3) ensuring recipients can obtain the source code. Modified versions must be clearly marked as changed to avoid attributing issues to the original authors. The license also prohibits practices that deny users the ability to install or run modified software on their devices.

  12. Understand Class Vectors in scapesClassification

    Class vectors are the primary mechanism for storing function classifications in scapesClassification. They map raster cells to numeric classes (or NA if unclassified).

    Key characteristics:

    • The nth element of a class vector corresponds to the raster cell in the nth row of the attribute table.
    • They act as both input and output for classification functions.
    • When used as input, they allow for focal evaluation of rules.
    • Functions update class vectors by assigning numeric classes only to previously unclassified cells that meet specific conditions, enabling multi-step classification workflows.