Raster Forge: Interactive Raster Manipulation Library and GUI for Python

TL;DR

Raster Forge addresses the need for an accessible, beginner-friendly raster data toolkit tailored to remote sensing and wildfire applications. It combines a Python library with a PySide6 GUI, offering Layer and Raster containers and six processing families (Composites, Multispectral Indices, Topographical Features, Distance Field, Height Map, Fuel Map) to import, visualize, and process raster layers. The work demonstrates practical utilities such as gamma-corrected composites, index-based masks, terrain feature extraction, and fuel-map generation, with demonstrated impact on wildfire modeling and data-harmonization workflows. The tool aims to speed up raster data processing across domains like disaster management, hydrological modeling, agriculture, and environmental monitoring, and anticipates broader adoption via web tools and synthetic data generation.

Abstract

Raster Forge is a Python library and graphical user interface for raster data manipulation and analysis. The tool is focused on remote sensing applications, particularly in wildfire management. It allows users to import, visualize, and process raster layers for tasks such as image compositing or topographical analysis. For wildfire management, it generates fuel maps using predefined models. Its impact extends from disaster management to hydrological modeling, agriculture, and environmental monitoring. Raster Forge can be a valuable asset for geoscientists and researchers who rely on raster data analysis, enhancing geospatial data processing and visualization across various disciplines.

Figures (6)

• Figure 1: Major libraries used to build Raster Forge. Adapted from rasterio-gitpyside-logonumpypngspyndex-logoopencv-media-kit.
• Figure 2: The is arranged into three distinct panels: the layers panel located at the top-left, the processes panel positioned at the bottom-left, and the viewer panel situated on the right.
• Figure 3: Layer import panel showcasing its initial state (a) and after selection of a file (b). The predicted values for width and height dynamically adjust based on the chosen scale.
• Figure 4: Upon importing layers, the graphical user interface () offers access to layer functionalities. For each layer, four functionalities are available, namely: view layer, edit layer's name, access layer information, and delete layer.
• Figure 5: Layer information panel composed of three tabs: metadata, statistical insights, and value histogram.