Python Looking Down at Us
Python in Earth of Observation Pt.2 of the mini-series regarding Python’s use cases in space
Table of Contents:
- Introduction
- Python’s Role in EO Applications
- Machine Learning and AI in EO with Python
- Conclusion/Further reading
Introduction
Brief overview of Earth Observation (EO) sector
Earth Observation (EO) refers to the use of remote sensing technologies to monitor land, marine (seas, rivers, lakes) and atmosphere. -European Union Agency for the Space Programme
Concretely it is the collection of data about the Earth using sensors. An important aspect of the sector is the satellite component as they provide crucial data about our surroundings in a way that is often more efficient than in-situ testing. The value of this data is immense because of the many ways it can help manage disasters and urban planning among others. As of now, gathering data is no longer the issue, rather it is processing and analyzing the data that is more pressing.
Importance of Python programming language in EO applications
In my previous article (What is Python being used for in space?), I have already covered the advantages of Python for Earth observation and the space industry as a whole. If you want the full breakdown go there. The condensed version is that Python’s simplicity, flexibility, and libraries are what make it compelling for geospatial science.
In this article, I will explore the actual pipeline of the EO satellite image processing pipeline and how it plays a pivotal role in unlocking insights from EO data, empowering researchers, scientists, and practitioners to make informed decisions and address pressing global challenges.
Python’s Role in EO Applications
Data acquisition and preprocessing using Python libraries
In EO acquiring and preprocessing satellite images and geospatial data are key to laying the base for later analysis and insights. In this context, Pyhton becomes a great tool to streamline these tasks thanks to a its rich ecosystem of libraries.
Libraries such as GDAL (Geospatial Data Abstraction Library) and Rasterio allow for reading, writing, and manipulating geospatial raster data formats, including industry-standard formats such as GeoTIFF and NetCDF. These libraries facilitate integration between multiple workflows.
Data analysis and visualization with Plotly for interactive and publication-quality graphs
Data is cool but the most important thing is the insights we can derive from them. In this scenario Plotly can be a useful tool to look into to create stunning graphs and powerful visualizations. The tool allows users to create interactive plots, line charts, scatter plots, heatmaps, and 3D surface plots among others. With Plotly’s interactive features, users gain better insights into spatial patterns and trends. Another tool/integration: Jupyter Notebooks (integrated with Plotly) enables researchers to document workflows and share their findings.
Using Plotly for data visualization in EO, researchers, and industry members can create compelling and informative visualizations that facilitate decision-making, and drive innovation in the field.
Machine Learning and AI in EO with Python
If you have been following the news lately one of the biggest things about AI and its revolutionary ability to optimize processes in different workflows. It’s the same in EO where tailor-made models can accurately identify and analyze changes in land cover over time. And more often than not these models are developed with Python’s libraries.
With Python’s ML Libraries developing supervised, unsupervised, and semi-supervised learning approaches for land cover classification becomes much easier. And yet again Python’s support for a wide range of things comes in handy as researchers and analysts can use feature extraction, dimensionality reduction, and model evaluation to enhance the accuracy and robustness of land cover classification models.
Two of the most talked about models when it comes to image analysis are the CNN or convolutional neural network and RNN or recurrent neural networks. These powerful models can analyze EO imagery in chronological order to detect and characterize changes in land cover, which then provides valuable insights into environmental dynamics.
Python and AI model development go hand in hand and they can be valuable in land cover classification and change detection by unlocking new opportunities in terms of monitoring and understanding the Earth’s surface and thus contributing to more sustainable land management practices.
Conclusion
As we can see Python has become vital to transform the EO industry. It has provided researchers and scientists with new and improved tools in order to derive insights from geospatial data.
Looking ahead, the future of Python in the sector seems bright. Indeed the continued vigor and interest around AI will keep pushing Python to the forefront in terms of its capabilities. This will also translate to a greater volume of datasets that can be used to progress Earth Observation.
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Website: www.vytahconf.com
Tickets: https://www.eventbrite.com/e/vytahconf-space-business-conference-tickets-681748507137?aff=erelexpmlt
Question: adam@vytahconf.com
Further Reading
A truly Practical guidebook covering this topic in more detail in the following book(Earth Observation Using Python: A Practical Programming Guide) which can be accessed using this link below:
