Introduction

Main Idea

Machine learning has been playing a key role in different domains of geospatial science, such as, natural resource management, hydrology, agricultural monitoring, land cover dynamics, and so on. Researchers and scientists often use raster data derived from satellites, airplanes or unmanned aerial vehicles (UAVs) coupled with novel machine learning algorithms to estimate physiochemical parameters or explain the underlying processes of different phenomenon. Geospatial raster data is different from natural images often seen in computer vision applications. For example, a common task in utilizing machine learning for raster data is to derive hand-crafted features based on different disciplines or research questions. Such features have can explain certain characteristics, which cannot be interpreted by the individual bands or channels. To date, there has been many vegetation indices or texture features reported in literature. Therefore, it is difficult for researchers or scientists to derive the necessary feature from raster data and extract the values for the sample areas of interest. We hereby propose a Python-package called “Raster4ML”, which helps the users to easily create machine learning ready dataset from given geospatial data. The package can automatically calculate more than 350 vegetation indices and numerous texture features. It can also derive the statistical outputs from areas of interests for multiple raster data, which reduces the manual processing time for users. On the other hand, the ackage provides supports for dynamic visualization of the geospatial data that can help the users to interact with the inputs and outputs. The package will assist the geospatial community to derive meaningful features from geospatial datasets efficiently and automatically. This will enable them to focus more on the algorithm development, training, and reproducibility of the machine learning application rather than the preprocessing steps.

License

Copyright (c) 2022, Remote Sensing Lab, Sourav Bhadra, Vasit Sagan All rights reserved.

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