Spatial Intelligence: Why GIS Practitioners Should Embrace Machine Learning- How to Get Started.

Author(s): Stephen Chege-Tierra Insights

Originally published on Towards AI.

Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? You just want to create and analyze simple maps not to learn algebra all over again.

As boundaries blur between maps and predictive models,GIS practitioners find themselves at the forefront of this revolution, poised to harness the full potential of spatial intelligence in an ever-evolving landscape of possibilities.

With the emergence of ARCGISpro which will replace ArcMap by 2026 mainly focusing on data science and machine learning, all the signs that machine learning is the future of GIS and you might have to learn some principles of data science, but where do you start, let us have a look.

How GIS and Machine Learning Works

According to Geography Publishing, In GIS, machine learning is like providing a cartographer with the strongest magnifying glass on the planet. With its help, we can find previously unknown patterns in geographic data, turning landscapes into spatial intelligence libraries.

It is currently hard for humans to analyze the vast amounts of raw geospatial data being acquired by various remote sensing devices and survey methods (drones/LiDAR). Additionally, the elimination of human loop processes has made it possible for AI/ML to construct training data for data annotation and labeling, which has a major influence on geospatial data.

Spatial analysis and modeling, which entails using a variety of methods and algorithms to analyze spatial data to identify patterns, correlations, and forecasts, is one of the main uses of GIS. This function can be improved by AI and ML, which allow GIS to produce insights, automate procedures, and learn from data.

Types of Machine Learning for GIS

1. Supervised learning– In supervised learning, the input data and associated output labels are paired, letting the system be trained on labelled data. Regression and classification tasks benefit from this kind of learning. Supervised learning can be applied to GIS applications such as species habitat mapping, land cover categorization, and temperature and precipitation prediction.

2. Unsupervised learning– Unsupervised learning recognizes what the data is using patterns from unlabeled data. For example, it takes millions of images and runs them through a training algorithm. After trillions of linear algebra computations, it can take a new picture and segment it into clusters.

3. Deep learning multiple– layer artificial neural networks are the basis of deep learning, a subdivision of machine learning (hence the word “deep”). Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two examples of deep learning methods that are being used more and more in GIS applications. These tasks include picture classification, object detection in aerial photography, and time-series analysis of satellite data.

I will not go into details on the types of machine learning algorithms and how they apply in a geospatial situation as I have already covered this topic in my previous post. But the most commonly used algorithm machine learning for geospatial analysis include Random Forest, linear regression, Logistic Regression Decision tree, K nearest neighbour and Naïve Bayes for supervised learning and K cluster for unsupervised learning.

GIS Random Forest script.

Load the necessary libraries
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import geopandas as gpd

# Load your geospatial datasetusing GeoPandas
data = gpd.read_file('path/to/yourdocument/yourshapefile.shp')

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the Random Forest classifier
rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)

# Train the classifier on the training data
rf_classifier.fit(X_train, y_train)

# Make predictions on the testing data
predictions = rf_classifier.predict(X_test)

# Evaluate the model's performance
accuracy = accuracy_score(y_test, predictions)
print("Accuracy:", accuracy)

How to Get Started

1. Get familiar with R and Python– When it comes to machine learning these two software languages are the heavy hitters, learning them will give you a better foundation in grasping machine learning algorithms. Also, both R and Python are compatible with GIS software such as ARCGISpro and QGIS, their libraries are specifically tailored for analyzing maps and imagery if needed. This will be a good way to get familiar with ML.

2. Set Clear and Doable Objectives -Machine learning is a broad and complex topic, which is why I suggest you give yourself realistic and timely objectives. For example, next month you will like to learn Random Forest, then go to K nearest neighbor as you get better and better.

3. Utilize relevant resources– Seek out books, online documentation and resource newsletters that address machine learning for GIS applications. There are courses accessible on websites like Coursera, Udemy, and edX that cover GIS and machine learning subjects.

4. Practice makes perfect– Practice isn’t the thing you do once you’re good. It’s the thing you do that makes you good- Malcolm Gladwell-practice, practice, if you want to be a master of something you have to practice.

5. Attend workshops and seminars– By attending Workshops and seminars it will help you network with other GIS Machine learning veterans who can help you in your venture. This will also expose you to current and timely information as machine learning is an ever-evolving topic.

6. Do not be intimidated– One look at a machine-learning script can bring chills down your spine. It can be frightening at first, but keep a positive attitude and believe in yourself, you got this. Everybody has to start from somewhere. So do not compare yourself with other established coders.

7. Patience and consistency– You can be patient but not consistent or consistent and not patient, that is why they go hand in hand as you need both. If you possess both of these attributes, I am sure you will be a machine-learning expert in no time.

Conclusion

Get started now as GIS is facing disruption and you do not want to be left behind. In terms of geographical analysis, adopting machine learning offers doors to previously unheard-of insights, efficiency, and breakthroughs.

By becoming proficient with these groundbreaking tools, GIS professionals will be able to lead the way towards a day when data-driven choices will affect how we manage and comprehend the environment we live in.

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Published via Towards AI