How to generate a GeoTIFF file from a NumPy array#
Reference Use Case#
Common
Problem statement#
Using NumPy arrays instead of GeoTIFF files for geospatial data management presents significant limitations, especially in terms of metadata handling and georeferencing. GeoTIFF files are designed to store not only pixel values but also crucial spatial information, such as coordinate systems, georeferencing data and projection details. NumPy arrays, however, only store raw numerical data without any inherent geospatial context. This lack of metadata makes it difficult to accurately interpret or align the data within a geographic framework. Additionally, GeoTIFFs support efficient storage and compression for large datasets, which NumPy arrays lack, leading to potential issues with scalability and performance in geospatial applications.
Envisaged impact#
Using NumPy arrays instead of GeoTIFF files for geospatial data can have several significant impacts: loss of georeferencing information, data misinterpretation, scalability issues, limited compatibility and difficulty in data management. In summary, while NumPy arrays are useful for numerical computations, they lack the essential features needed for robust geospatial data management, leading to potential inaccuracies, inefficiencies and compatibility issues in geospatial analysis.
Affected component of FAIRiCUBE-Hub#
FAIRiCUBE Lab (Storage, CPU, RAM and Network)
Proposed solution#
How to generate a GeoTIFF file from a NumPy array is explained at this link.
Expected benefits#
Using GeoTIFF files instead of NumPy arrays offers several key benefits, particularly in the context of geospatial data management and analysis: georeferencing and metadata, interoperability, efficient storage and compression, multi-band support, scalability, data integrity and standardization.
In summary, GeoTIFFs provide a comprehensive and reliable solution for managing geospatial data, offering features that ensure accuracy, efficiency and broad compatibility across geospatial applications.