Welcome to the exciting world of Geospatial Machine Learning! In this article, we’ll explore a curated list of resources that are focused on Machine Learning in Geospatial Data Science. Buckle up as we navigate through code projects, datasets, research papers, books, courses, and the companies driving innovation in this field.
Table of Contents
Code Projects and Workflows
Let’s start with some impactful code projects that can jumpstart your journey in Geospatial Machine Learning:
- A 2017 Guide to Semantic Segmentation with Deep Learning – by Sasank Chilamkurthy
- Deeplab Image Semantic Segmentation Network – by Thalles Silva
- deeplab_v3 – by anxiangSir
- deeplab_v3: Tensorflow Implementation of the Semantic Segmentation DeepLab_V3 CNN – by Thalles Silva
- Deep learning for satellite imagery via image segmentation – by Arkadiusz Nowaczynski
- Deep Learning for Semantic Segmentation of Aerial Imagery – by Lewis Fishgold and Rob Emanuele
- fieldRNN: Temporal Vegetation Classification with Recurrent Neural Networks – by TUM-LMF
- forecastVeg: A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health – by John Nay
- How to do Semantic Segmentation using Deep Learning – by James Le
Datasets
Finding the right datasets is crucial for effective machine learning. Here are some noteworthy options:
- Dstl Satellite Imagery Feature Detection: A set of 1km x 1km satellite images in both 3-band and 16-band formats.
- DeepSat (SAT-6) Airborne Dataset: 405,000 image patches in six land cover classes.
- SAT-4 and SAT-6 Airborne Datasets: Images extracted from the NAIP dataset.
- SpaceNet: A corpus of commercial satellite imagery to foster innovation.
Papers
Enhance your knowledge further with these influential papers:
- Caffe CNN-based classification of hyperspectral images on GPU (2018).
- Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community (2017).
- Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data (2017).
Books
Here are some invaluable books for considering deeper dives:
- Advances in Artificial Systems for Medicine and Education (2018).
- Deep Learning with Applications Using Python (2018).
Courses
Take your skills to the next level with these recommended courses:
- Classification Models (2018).
- Intro to Deep Learning (2018).
Companies
Lastly, here are some companies leading the charge in geospatial machine learning:
Troubleshooting Ideas
If you encounter any issues while exploring the resources provided, consider the following troubleshooting ideas:
- Check the URLs for any typos or outdated links.
- Ensure you have the necessary software and dependencies installed before running code samples.
- For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
Analogy to Understand Geospatial Machine Learning Code
Imagine you are a chef preparing a gourmet dish. Each ingredient represents a different aspect of the code projects in Geospatial Machine Learning. Just as you carefully select the finest ingredients, you need to pick appropriate datasets (your ingredients) that will yield the best outcome in your dishes (projects).
When you arrange these ingredients (datasets), you’re following a recipe (code implementation) that guides you step-by-step. The result is a delicious culinary masterpiece (a successful machine learning model). If any ingredient is out of place or missing, just as with any recipe, you may end up with an unsatisfactory dish! Hence, ensuring you have all your resources and guidelines ready is crucial.
Now get started on your journey in Geospatial Machine Learning, and may your models be as accurate as a GPS!
